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XBoost - Composable /x* Agent Skills Ecosystem & Verification Harness

License: MIT Version Skills CLI Engines Agents Dual-OS Upstream OSS

XBoost (iscvlab/xboost v3.5.0) is a unified, composable /x* skill ecosystem, 3-Tier cross-workspace compound learning framework, and deterministic CLI verification harness for AI coding agents engineered by ISCV | Isocurve Research Lab (@iscvlab). Type /x in any supported IDE or CLI—or load the portable SKILL.md protocols into any AI coding agent — cloud, IDE, CLI, or local LLM (Ollama, vLLM, LM Studio, OpenHands, Cline, Continue, Aider, Claude Code, Codex, Cursor, Grok, Gemini / Antigravity, or custom agent frameworks)—to surface all 15 composable skills (xboost, xbrief, xcontext, xcreate-skill, xdeep-research, xkickoff, xlaya, xlearn, xmouse, xobsidian, xocr, xplan, xpublish, xsync, xultra), the Cross-Workspace Compound Learning Playbook (docs/COMPOUND_LEARNING_PLAYBOOK.md), and 10 bundled Python CLI & MCP engines (10,382 LOC) on Windows, macOS, and Linux.

Sister toolkits & 22 upstream open-source contributions (5 MERGED): xlaya (3-Stage System 1 → System 1.5 Semantic Decision, Dual-Encoder Ensemble Jury, Joint NLI Contradiction Triad & vega-U Conformal Triage Engine v2.1.0 LIVE) · mcp-bridges (7 Model Context Protocol automation bridges for QGIS, Rhino 3D, Grasshopper, Adobe Illustrator, Figma, and Ghost Mouse · Selected for the XERJ Corpus Hub) · xocr (Hybrid Document AI, Two-Stage LaTeX booktabs & HTML Table Surgery, and Local SCB 10X Typhoon-OCR v1.5 × Baidu Unlimited-OCR-2 Engine · Upstream PR #15 to scb-10x/typhoon-ocr (MERGED)) · sse-space-syntax (High-performance C++/Rust/Python Space Syntax & Isovist Engine v1.1.1 LIVE) · Batch 0 Agent & Document AI Infrastructure (Upstream PRs #2922–#2927 to xerj-org/xerj — #2925 MERGED, #2924 & #2926 OPEN CI PASS) · Group A Geospatial & Vision (opengeos/geoai #960 MERGED, martibosch/pylandstats #77 4.0× speedup MERGED, Weecology/DeepForest #1449 74.4×–421.3× speedups, gboeing/osmnx #1412 40.2× speedup, opengeos/segment-geospatial #569 CodeRabbit APPROVED) · Group B Climate Risk, ESG & Urban Physics (ladybug-tools/uwg #292 1,735.6× speedup MERGED, os-climate/physrisk #651 32.5× & 1,669.6× speedups, Deltares/hydromt #1566 27.5× speedup, CLIMADA-project/climada_python #1349 & #1350 69.9× & 11.7× speedups, natcap/invest #2813 & #2829 23.1× & 18.1× speedups) · Group C Developer Tooling (mufeedvh/code2prompt #348).

⚡ TL;DR - Capabilities & Performance at a Glance (v3.5.0)

  • Composable 5-Stage Lifecycle (Stage 1 → Stage 5) + Dedicated /xlearn Compound Learning (Tier A / Tier B): Chain any combination of /x* slash skills in a single prompt (/xbrief /xdeep-research /xboost /xlearn /xsync /xobsidian /xultra /goal). XBoost automatically normalizes the execution order into Context → Research → Plan/Build → Learn/Sync/Ratchet and compounds verified defect/speedup patterns (19 PAT-*, 7 GOV-*, 11 FALS-*) across the 3-Tier Playbook Cascade via /xlearn and docs/COMPOUND_LEARNING_PLAYBOOK.md.
  • -96.78% Context Noise Reduction + ~30 ms Warm MCP / 171.59 ms Lexical System 1 → 1.5 Triage: /xbrief (Cap 8 Two-Pass reader + Up-Tree Playbook Discovery) + /xobsidian (opt-in 2-Hop Second Brain router) compresses 1,639,875 raw workspace/vault tokens (834 files) down to 52,766 high-authority tokens (31.1× compression; 48,226 tok / 97.06% Repo-Only) in 1.23 ms, while /xlaya (v2.1.0, laya_triage.py + 6-tool resident xlaya_mcp_server.py) chunks & ranks 1,172 fence-safe sections across 27 specs (175,055 → 1,137 tok, -99.35%) in 171.59 ms Stage 0 (6,830.2 chunks/s) and ~30 ms warm MCP inference (859.1 q/s 5.9×–6.5× multilingual & 837.9 q/s 8.9× mixed-language batch speedups on RTX 5060 Ti, 0.45/0.55 ensemble jury, --vega-views 1..3 debiasing, xlaya_fit persistent TTTHead 90.0% fitted vega-U routing accuracy, 100% 3-Question Joint NLI Triad, and 0.00% XERJ #1108 wrong-and-confident fires).
  • 100.0% Red-Team Defect Recall (25 / 25 in 8.01 ms): 10 bundled Python CLI & MCP engines (10,382 LOC) enforce 16-pattern secret scanning, Python AST security checks, diff-scoped upstream PR & CRLF/LF line-ending parity hygiene (--platform pr), Qt6/QGIS 4 scoped-enum validation, deep-link URL verification, SHA-256 mirror parity, resident /xlaya JSON-RPC 2.0 stdio MCP semantic triage (xlaya_mcp_server.py), non-intrusive Ghost Mouse UIA/SoM desktop telemetry (xmouse_cli.py), and ≤ 400-char YAML skill budgets (374.47 ± 9.95 chars across all 15 skills, 0% sibling skill truncation).
  • Which Subscription Quota Tier Should You Pair With XBoost?
    • 1× Base Pro (Claude Pro, Gemini Advanced, Cursor Pro, or Local LLM): Best for Single-Skill Tactical Tasks (/xbrief, /xdeep-research Tier 1, localized /xboost - ~12.6 steps/turn).
    • 5× High-Throughput (Claude Max 5×, Gemini 5× - Recommended Minimum): Best for Daily Chained Multi-Skill Sprints (/xbrief /xdeep-research /xboost /xlearn /xsync /xobsidian - ~90.4 steps/turn, 3.94× verification depth).
    • 20× Ultra (Gemini Ultra 20×, Claude Max 20×): Engineered for 400+-Step Autonomous /goal Marathons & /xultra (N × K × C × T) Multi-Agent Swarms (~710.1 steps/session, ~39.67M cumulative session tokens).

Try it now with a one-prompt agent install (copy and paste into any AI coding agent):

Install iscvlab/xboost (https://github.com/iscvlab/xboost): detect my OS and your active global or workspace skills directory, clone the repository, install all 15 /x* skills (Windows: ./scripts/install.ps1 -All; macOS/Linux: ./scripts/install.sh --all; or copy skills/* directly into your active skills folder), run python skills/xpublish/scripts/audit_engine.py skills/xboost --platform skill to verify the CLI harness, and confirm all 15 /x* commands are ready.

Horizontal Bar Comparison (N = 115 Skills • 834 Docs/Vault Nodes • 25 Red-Team Defects • 810 Real IDE Turns)

Evaluation Dimension Workflow / Cohort Configuration Visual Comparison Bar Measured Result & Delta vs. Baseline
1. Red-Team Defect Catch Rate ↑
(25 Seeded Defects across 5 Pillars)		 Level 3: Full 5-Stage + 10 CLI Gates
Level 2: /xbrief + /xdeep-research + /xboost
Level 1: /xboost Standalone (2 Gates)
Level 0: Prompt-Only Baseline (0 Gates)		 ████████████████████ 100.0% (25/25)
████████████████░░░░ 80.0% (20/25)
████████░░░░░░░░░░░░ 40.0% (10/25)
░░░░░░░░░░░░░░░░░░░░ 0.0% (0/25)		 0 False Positives in Median 8.01 ms ✨
4 Active CLI Verification Gates
2 Active CLI Verification Gates
No Deterministic Pre-LLM Enforcement
2. Context Noise Compression ↑
(834 Repo + Vault Files, 1.64M tok)		 /xbrief (Cap 8) Repo-Only
/xbrief (Cap 8) + /xobsidian (2-Hop)
Unbounded Repo + Vault Crawl		 ████████████████████ 97.06% Removed (34.0×)
████████████████████ 96.78% Removed (31.1×)
░░░░░░░░░░░░░░░░░░░░ 0.0% Removed (1.0×)		 48,226 tok in Median 1.13 ms ✨
52,766 tok (12 files) in 1.23 ms ✨
1,639,875 tok (834 files) in 51.78 ms
3. System 1 Section Triage ↑
(27 Specs / 1,172 Chunks, 175.1k tok)		 XBoost /xlaya Top-5 Shortlist
Unfiltered Full-Corpus Pass-B Read		 ████████████████████ 99.35% Saved (154.0×)
░░░░░░░░░░░░░░░░░░░░ 0.0% Saved (1.0×)		 1,137 tok in Median 171.59 ms (6.8k chunks/s; ~30 ms warm MCP / 859.1 q/s GPU) ✨
175,055 tok (Full 27-File Read)
4. Dual-Trigger Budget Guard ↑
(Use when + Do NOT use across N=115)		 XBoost v3.5.0 Suite (N = 15)
All Installed Host Skills (N = 115)
Official Cloud/Vendor Skills (N = 31)		 ████████████████████ 100.0% (15/15)
██████████████████░░ 91.3%
█████████████████░░░ 87.1%		 374.47 ± 9.95 chars (0% Truncation) ✨
324.78 ± 51.73 chars (99.1% ≤500 pass)
331.1 ± 63.41 chars (96.8% ≤500 pass)
5. Verification Depth / Turn ↑
(N = 810 Real IDE Prompt Turns)		 Chained Multi-Skill /x* Turn (N=245)
Single /x* Skill Turn (N = 89)
Standard Turn Without /x* (N = 476)		 ████████████████████ 90.4 Steps (3.94×)
███░░░░░░░░░░░░░░░░░ 12.6 Steps
█████░░░░░░░░░░░░░░░ 23.0 Steps (1.0×)		 4.87M tok/turn (93.3k new in, 24.3k out) ✨
0.78M tok/turn (9.9k new in, 2.9k out)
1.26M tok/turn (13.6k new in, 5.6k out)
6. Compound Learning OSS Speedups ↑
(22 Upstream Contributions, Batch 0 + A + B + C)		 /xlearn 3-Tier Playbooks (PAT-* + GOV-* + FALS-*)
Unvectorized Upstream Baseline (HEAD)		 ████████████████████ 4.0× – 1,735.6× Faster
░░░░░░░░░░░░░░░░░░░░ 1.0× Baseline		 100% assert_allclose Parity (5 MERGED, 9 OPEN PRs) ✨
Python np.vectorize, O(N²) scans & OGR disk-seek loops

1. Master Ecosystem Architecture

XBoost organizes software engineering, scientific research, spatial/physics simulation, desktop GUI automation, and documentation governance into five deterministic lifecycle stages plus cross-stage System 1 semantic & multi-agent swarm multipliers across 15 composable /x* skills:

%%{init: {'theme': 'neutral'}}%%
flowchart LR
    subgraph Canvas [" "]
        S1["Stage 1: Context and Memory - /xbrief + /xobsidian"]
        S2["Stage 2: Research and Proof - /xdeep-research + /xocr"]
        S3["Stage 3: Plan and Execute - /xplan + /xboost + /xultra + /xmouse"]
        S4["Stage 4: Learn, Sync and Release - /xlearn + /xsync + /xcreate-skill + /xpublish"]
        S5["Stage 5: Continuity and Sidecar - /xcontext + /xkickoff + /xlaya"]
        REG["Cloud Skill Registry Sync (coming soon)"]

        S1 --> S2
        S2 --> S3
        S3 --> S4
        S4 --> S5
        S5 -.-> S1
        S4 -.-> REG
    end
    classDef default fill:#ffffff,stroke:#475569,color:#0f172a,stroke-width:1px
    style Canvas fill:#ffffff,stroke:#cbd5e1,stroke-width:1.5px,color:#0f172a
    style REG fill:#ffffff,stroke:#94a3b8,stroke-dasharray: 5 5,color:#64748b
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2. Composable /x* Slash-Command Grammar & Opt-In Second Brain

Every skill in XBoost is prefixed with x so typing /x in any IDE autocomplete menu immediately surfaces the entire ecosystem without memorizing individual base names. Every skill works standalone or chained in any order (automatically normalized into the canonical lifecycle DAG):

[Stage 1: Context]  →  [Stage 2: Research]  →  [Stage 3: Plan / Execute]  →  [Stage 4: Learn & Sync]  +  [Modifiers / Sidecars]
     /xbrief        →    /xdeep-research    →     /xplan  or  /xboost     →     /xlearn + /xsync     +  /xobsidian /xlaya /xultra /xmouse /goal

Strict Opt-In Second Brain Isolation (/xobsidian)

By default, all skills operate in 100% Repo-Only Mode and ignore any directory containing an .obsidian/ subfolder. The 2-Hop Obsidian Second Brain and Knowledge Ratchet activate only when /xobsidian is explicitly included in the command chain:

%%{init: {'theme': 'neutral'}}%%
flowchart LR
    subgraph Canvas [" "]
        Prompt["User Prompt + /x Slash Combination"] --> Gate{"Is /xobsidian in the command?"}
        Gate -->|"NO (Default)"| NoVault["Standard Repo-Only Execution (Zero vault reads or writes)"]
        Gate -->|"YES (Opt-In)"| Resolve["Step 0: Dynamic Vault Resolver (obsidian.json + 00 Index.md)"]
        Resolve -->|"/xbrief /xobsidian"| BriefCombo["/xbrief (Cap 8) + Vault 2-Hop Domain Lookup"]
        Resolve -->|"/xdeep-research /xobsidian"| ResCombo["/xdeep-research + Vault Hop 0 to 2 Cache and Auto-Ratchet"]
        Resolve -->|"/xdeep-research /xboost /xobsidian"| BoostCombo["Full Stack Research to 2D Contract to Build to Vault Ratchet"]
        Resolve -->|"/xlearn /xobsidian or /xsync /xobsidian"| SyncCombo["3-Tier Playbook + Repo + Second Brain Sync (Repo Docs and Vault Hubs)"]
    end
    classDef default fill:#ffffff,stroke:#475569,color:#0f172a,stroke-width:1px
    style Canvas fill:#ffffff,stroke:#cbd5e1,stroke-width:1.5px,color:#0f172a
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High-Leverage Command Combinations

Command Chain Execution Pipeline Best Used When
/xbrief <task> Load up to 8 authoritative repo docs (Pass A skeleton → Pass B deep read) + Up-Tree COMPOUND_LEARNING_PLAYBOOK.md (3-Tier Cascade) → execute <task> Starting a feature or fix inside an unfamiliar or multi-doc repository
/xbrief /xobsidian <task> Load repo SSOTs (cap 8) + 2-Hop lookup in active Obsidian vault → execute <task> Pulling cross-project architectural rules or domain constants into the current task
/xdeep-research /xboost <task> Adaptive 3-Tier primary-source probe (Tier 1 inline SDK/DB/AST probe or Tier 3 5-wave harvest) → lock 2D Goal Contract → Red-Green-Verify build Implementing a technical feature or solver that depends on exact API/physics specs
/xdeep-research /xboost /xobsidian Vault 2-Hop cache check → probe missing delta → /xboost 4-phase build → Auto-Ratchet new specs into Vault Compounding reusable engineering or scientific knowledge while shipping production code
/xdeep-research /xboost /goal 5-wave multi-agent harvest + verify_evidence.py audit → full-stack implementation until 100% milestone completion Long-running, multi-milestone research-to-production engineering sprints
/xboost /xultra <task> Elastic Cognitive Swarm (N × K × C × T) (Orthogonal K-Vector Tournament, Elastic Cognitive-Density Fleet with Pre-Locked Shared Seam Header, or Evolutionary Ratchet Loop across T generations) + Two-Stage Elastic Verification (Wave 2A Parallel Critic Shards C ≥ 1 → Wave 2B Serialized Main Checker Gate) High-stakes algorithms, solver convergence, multi-module sprints, or parallel UI/diagram candidate tournaments on 5×/20× tiers
/xboost /xmouse <task> Non-intrusive background Ghost Mouse (PostMessageW) + UIA tree inspection + Set-of-Mark vision capture + Multi-Agent Visual Cursor HUD Automating or verifying desktop CAD/GIS/IDE windows in the background without hijacking the physical mouse cursor
/xplan <task> 2D Plan Contract → Ponytail × Deep Module Vector A/B/C → implementation_plan.md with Inline Grilling Matrix & Red → Green table → halt for approval Stress-testing architectural decisions and edge cases before writing a single line of code
/xlearn (/xlearn /xobsidian, /xlearn /xultra) Distill verified session bug/optimization/governance patterns into the 3-Tier Compound Learning Playbooks (Tier A auto-ratchet + built-in /xsync --federated closure) or run the 5-Gate Universal Law Falsification Sieve + Wave 2A Blind Critic (Tier B human-consent gate) Compounding post-session debugging, PR reviews, or benchmark findings without manually remembering to invoke /xsync or /xcreate-skill
/xsync /xlaya Fast System 1 noul semantic contradiction triage + audit_workspace_docs.py + 5-tier inner-to-outer doc sync Catching semantic contradictions across markdown/HTML docs alongside deterministic regex/link audits
/xsync /xobsidian 5-tier inner-to-outer repo doc sync + Tier A Playbook Ratchet + update matching atomic notes and Hub tables in the Obsidian vault Keeping repository documentation, compound learning playbooks, and knowledge graphs 100% synchronized
/xpublish (/xpublish-audit, /xpublish-qgis) Run 6-pillar security/AST/Flake8/skill/PR diff audit (audit_engine.py --platform pr|github|qgis|food4rhino|skill|generic), CRLF/LF line-ending parity check (PAT-GIT-01), Review-Bot convergence (§6.D), and Qt6-verified single-root QGIS zip packaging (package_qgis.py + check_qt6.py) Shipping upstream PRs and releasing open-source packages to GitHub, plugins.qgis.org, or Food4Rhino
/xcontext → /xkickoff Diagnose transcript compaction health (check_context.py) → run pre-handover /xsync → generate task-smart fresh session prompt Transitioning cleanly to a fresh chat window before context degradation or timeouts occur

3. The 15-Skill Suite

xboost/skills/
├── /xboost           2D Task × Project goal synthesis, Universal Laws (UL-01..08) & 4-phase red-green solver
├── /xbrief           4-tier authority hierarchy, two-pass reading & 3-tier compound playbook discovery
├── /xcontext         transcript compaction diagnostics, cross-platform token telemetry & drift scorecard
├── /xcreate-skill    author, distill & globalize portable SKILL.md files (Modes A & B)
├── /xdeep-research   unified 3-tier primary-source research, live HEAD/collision gate & 5-wave audit
├── /xkickoff         end-of-session sync check, task-smart command selector & handover prompt
├── /xlaya            3-stage System 1 → System 1.5 semantic triage, resident MCP & vega-U conformal sidecar
├── /xlearn           post-session compound learning, 3-Tier Playbook ratchet & 5-Gate Universal Law engine
├── /xmouse           non-intrusive Ghost Mouse, Hybrid UIA + Set-of-Mark controller & Visual Multi-Cursor HUD
├── /xobsidian        opt-in 2-hop Obsidian Second Brain router, cross-domain bridges & converged-only ratchet
├── /xocr             single-pass C++ PyMuPDF vector/booktabs table extractor, Thai/EN normalizer & 4-tier VLM/OCR ladder
├── /xplan            XBoost architecture planner with Vector A/B/C & inline grilling matrix
├── /xpublish         unified 6-pillar security/AST/PR/CRLF/skill audit gate, GitHub v1.4 & QGIS 4/Qt6 zip packager
├── /xsync            5-tier cross-document sync, 4-playbook federated auditor & SHA-256 mirror gate
└── /xultra           Elastic Cognitive Swarm (N × K × C × T), K-vector tournament, evolutionary ratchet & Wave 2A/2B blind verifier

Stage 1: Context, Triage & Memory

Skill & Command (Aliases) Core Architecture & Protocol Failure Modes Prevented
1. XBrief
/xbrief (/brief)		 Discovers related markdown in the active repository via a 4-tier authority hierarchy (hard-capped at 8 files) plus Bounded Up-Tree 3-Tier Playbook Discovery (v3.5.0, walking up to 2 ancestor levels under a safe stop-list to load docs/COMPOUND_LEARNING_PLAYBOOK.md PAT-*, GOV-*, FALS-* invariants and any domain-matched Tier 2 Playbook), applies Two-Pass reading (Pass A structural skeleton/cardinality scan before Pass B targeted section reading), verifies frozen/deferred boundaries, and executes /xbrief <task> in the same turn. Answering from stale LLM memory, repeating previously solved cross-workspace bugs, keyword-grep tunneling without knowing total section counts, or modifying frozen modules.
2. XObsidian
/xobsidian (/obsidian-vault)		 Dynamic Vault Resolver reads OS obsidian.json (open: true) or parent directories with zero hardcoded paths. Enforces the 2-Hop Navigation Rule (00 Index.md → Hub - <Domain>.md → Atomic Note), Converged-Only Ratchet & Session Dirty-Note Ledger (DIRTY_IN_SESSION), Cross-Domain Bridges, and 4-Check Graph Verification (0 broken wikilinks, 0 orphans, 100% frontmatter, 0 duplicate neighbor groups). Dumping 50+ markdown notes into context, hardcoded vault paths breaking across PCs, or ratcheting unverified mid-session experimental parameters into long-term memory.
3. XLaya
/xlaya (/laya)		 3-Stage System 1 → System 1.5 Semantic Decision, Ensemble Jury & Conformal Triage Sidecar (v2.1.0) (scripts/laya_triage.py 3,832 LOC + zero-extra-dependency 6-tool JSON-RPC 2.0 stdio MCP server scripts/xlaya_mcp_server.py 827 LOC, standalone repo iscvlab/xlaya v2.1.0 LIVE) powered by convaiinnovations/laya 0.4.31+ & vegaml 0.16.1+ (~30 ms warm MCP inference). Cascades Stage 0 (0.15 ms/chunk fence-safe breadcrumb multi-format chunker + BM25/code/Thai-3gram sieve, 171.59 ms across 1,172 chunks) → Stage 1 (ModernBERT/mmBERT/laya-typed-decisions with Head-Language Guard, 0.45/0.55 ensemble jury & disagreement >= 0.15 gate, 859.1 q/s 5.9×–6.5× multilingual & 837.9 q/s 8.9× mixed-language batch speedups on RTX 5060 Ti, 0/432 flips, 3-Question Joint NLI Contradiction Triad P_eff, --thorough 4-facet rubric + file_summaries, and predict_tournament for >20 labels) → Stage 2 (vega-U conformal 73,728-tok verifier with --vega-views 1..3 debiasing, xlaya_fit persistent TTTHead + split-conformal policy, outbound OOD guard, and local multimodal image/audio/video, returning prediction_set, abstain, and route, 90.0% (18/20) 40-row fitted routing accuracy). Verified on xerj-org/xerj#1108 (120 boundary emails) with 0.00% wrong-and-confident fires (0 LLM output tokens) and 100% pure-Python graceful fallback. Silent 512-token truncation, wide Playbook table clipping, cold-start CLI reloads (~30 ms warm MCP), accuracy collapse on >20 choice labels, off-topic/agreement NLI false positives, or missing subtle semantic contradictions between docs.

Stage 2: Research & Proof

Skill & Command (Aliases) Core Architecture & Protocol Failure Modes Prevented
4. XDeep-Research
/xdeep-research (/deep-research, /xresearch)		 Unified 3-Tier Primary-Source Research & 5-Wave Adversarial Proof Engine. Routes dynamically across Tier 1 (Fast Inline Probe ≤ 2 sources), Tier 2 (Single Isolated Scout), and Tier 3 (5-Wave Multi-Agent Deep Research Swarm): (1) 2D Intent × Domain contract synthesis, 4-Tier Primary-Source Authority Ladder (local pinned manifests/SQLite/AST probes first + automatic /xocr PDF/table ingestion), and Up-Tree 3-Tier Playbook & Live HEAD / Open-PR Collision Pre-Gate (v3.5.0: checking PAT-*, GOV-01..07, and FALS-01..11 silent HEAD fixes, enclosing-method def headers, and unlinked open PRs), (2) parallel Harvester scouts with mandatory deep-link scraping, (2.5) Wave 2.5 Rejected-Candidate Graveyard & Zero-Retrain Counterfactual Ablation Forensics, (3) Blind Adversarial Auditor + deterministic scripts/verify_evidence.py (286 LOC) CLI gate with Wave 3.5 Prune & Replace Loop, (4) [VERIFIED FACT] vs [RECOMMENDED SYNTHESIS] binding with Single-SSOT In-Place Upsert & Iterative Delta Routing (Turn t > 1), and (5) /xboost handoff. Secondary-blog guesswork, researching the wrong SDK version, claiming open GitHub issues already silently fixed on HEAD (FALS-01) or contested by unlinked PRs (FALS-03), misattributing enclosing function scopes (FALS-02), proposing expensive retraining when existing post-processing dampers throttle valid candidates, and duplicate RESEARCH_*.md file forking.
5. XOCR
/xocr (/ocr, /unlimited-ocr)		 High-Speed Hybrid PDF & Table Extraction Engine (scripts/ocr_runner.py 1,502 LOC, standalone repo iscvlab/xocr, Upstream PR #15 to scb-10x/typhoon-ocr (MERGED)). Combines Single-Pass C++ PyMuPDF (fitz) coordinate-sorted text (19-97 ms/page), a Two-Stage Grid + LaTeX booktabs Table Extractor (100% recall, 0 false positives), S 10BC8 canned VLM HTML <table> → RFC-4180 CSV Surgery (colspan/rowspan expansion), Thai PUA (U+F700..U+F71A) & Decomposed Sara-Am Normalization, a Surgical Per-Page Garble & Image-Coverage Router, and a 4-Tier Local VLM/OCR Backend Ladder (SGLang → Local HuggingFace typhoon-ai/typhoon-ocr1.5-2b / 3b-qat (-35.9% vision token bloat, +9.11% table accuracy on ThaiOCRBench) & baidu/Unlimited-OCR-2 CUDA VLM → EasyOCR → pytesseract). Zero-table silent failures when pdfplumber is absent, collapsed 1-row LaTeX booktabs tables, unparsed raw HTML <table> tags in VLM output, Thai PUA/Sara-Am mojibake, (cid:...) CMap garbage passing naive length checks, and slow full-PDF VLM rasterization (15.0×-3,326× speedup on mixed/vector PDFs).

Stage 3: Planning, Deep Execution & Desktop Automation

Skill & Command (Aliases) Core Architecture & Protocol Failure Modes Prevented
6. XPlan
/xplan (/plan)		 XBoost Architectural Planner. Locks a 2D Plan Contract (Scope × Risk), runs blast-radius & invariant discovery (with Elastic Red-Team Seam & SSOT Critics C ≥ 1 under /xplan /xultra), evaluates Ponytail × Deep Module Vectors A/B/C (Vector A (Recommended): Zero-New-File / Stdlib Seam), embeds an Inline Grilling Decision Matrix (DEC-01..N) and Red → Green Verification Matrix (UL-05 A_pos ∧ A_neg + UL-08 Live-Entrypoint Parity) inside implementation_plan.md with zero chat Q&A spam, and halts for user approval. Jumping straight into code on underspecified requirements, noisy chat Q&A walls, or over-engineered multi-file abstractions.
7. XBoost
/xboost		 Flagship 4-Phase Engineering & First-Principles Solver: (1) 2D Task × Project Goal Contract, Tri-Track Signal Gate (with Bidirectional Counter-Anchors A_pos ∧ A_neg, Strict Feasibility Dominance & Goal-Matched Objective/Constraint Polarity, UL-05, plus Live-Entrypoint Parity & Active-Regime Sensitivity, UL-08), and Context-Conditioned Single-Layer Universal First-Principles Laws (UL-01..UL-08) (references/deep_audit_protocol.md), (2) Ponytail × Deep Module Ladder (YAGNI → Reuse → Stdlib → Deep Seam → Shortest Diff), (3) Universal Parity (Dual-OS + OneDrive + IDE/CLI), (4) 3-Tier Verification & Cumulative Monotonic Ratchet (Pass_Set(t+1) ⊇ Pass_Set(t)). Speculative blind fixes, cross-cut data leakage (UL-01), blocking-continue peer-branch drops (UL-04), accidental α² double-application (UL-06), T(T(x)) cache compounding (UL-07), and machine-bound paths.
8. XUltra
/xultra (/ultra)		 Elastic Cognitive Swarm (N × K × C × T) & Two-Stage Evolutionary Ratchet (Stage A → Stage B). Normalizes any multi-skill combo into the Canonical Stage DAG (Context → Research → Plan/Build → Learn/Sync/Ratchet), derives topology via the 4-Law Swarm Sizing Calculus (N × K × C × T) (Law 1: Code-Over-Swarm Split, Law 2: True Orthogonality K-Vectors, Law 3: Cognitive-Density N-Sharding with Pre-Locked Shared Seam Header, Law 4: Two-Stage Elastic Verification — Wave 2A Parallel Critic Shards C ≥ 1 → Wave 2B Serialized Main Checker Gate), executes the Two-Stage Evolutionary Ratchet (Stage A zero-retrain post-processing throttle audit UL-04..UL-07 locking Floor_StageA ≥ Floor_0 before Stage B warm-start generations 1..T with Pass_Set(t+1) ⊇ Pass_Set(StageA)), and enforces the 3-Bucket Numeric Litmus Test + Pointer-Not-Payload Return Contract. Arbitrary fixed-count subagent quotas, cold-start retraining when post-processing throttles suppress existing models, multi-agent file-write race conditions, cross-shard interface drift, and self-grading inline shortcuts.
9. XMouse
/xmouse		 Non-Intrusive Multi-Agent Ghost Mouse, Hybrid UIA + Set-of-Mark Controller & Visual Multi-Cursor HUD (scripts/xmouse_cli.py 1,891 LOC). Enforces a 3-Tier Escalation Ladder: Tier 1 (Ghost Mode Default) dispatches background Win32 PostMessageW (WM_LBUTTONDOWN/UP, WM_CHAR, WM_MOUSEWHEEL) directly to target HWND client coordinates (0 px physical cursor movement, 0 focus theft); Tier 2 (Instant Save/Restore SendInput) saves physical cursor state via GetCursorPos(), injects hardware input in < 5 ms, and restores the user's cursor; Tier 3 (60 FPS Click-Through Multi-Agent HUD) renders color-coded virtual agent cursors, smooth Bézier trails, click ripples, and target bounding boxes on a transparent overlay (WS_EX_LAYERED | WS_EX_TRANSPARENT). Includes native C++/COM UIA tree inspection (xmouse_inspect_uia) and Set-of-Mark ([1]..[N]) vision grid capture (xmouse_som_capture). Hijacking the user's physical mouse cursor during long agent runs, stealing active window focus while the user is typing, or guessing blind pixel coordinates when exact UIA bounding rectangles exist.

Stage 4: Compound Learning, Synchronization & Release Gates

Skill & Command (Aliases) Core Architecture & Protocol Failure Modes Prevented
10. XLearn
/xlearn (/learn)		 Compound Learning Ratchet, 3-Tier Playbook Updater & 5-Gate Universal Law Evolution Engine (v3.5.0). Separates post-session compound learning from greenfield skill authoring (/xcreate-skill). Routes verified session findings across a Two-Tier Autonomy Boundary: Tier A (Autonomous 3-Tier Playbook Ratchet) classifies (PAT-*, GOV-*, FALS-*) via /xlaya, appends/updates the matching playbook (docs/COMPOUND_LEARNING_PLAYBOOK.md Core Hub or Tier 2A/2B/2C domain playbooks while enforcing the < 35 KB Single-Pass Read Ceiling), and automatically triggers /xsync (--federated) so SHA-256 mirrors and sibling indices stay 100% synchronized in one command; Tier B (5-Gate Universal Law Falsification & Human-Consent Skill Evolution) enforces the 3-Domain Translation Proof Table (Archetypes A × B × C), 9-Point Mathematical Boundary Table, and Wave 2A Blind Counter-Domain Critic before presenting a Before → After proposal for explicit human approval. Forgetting to run /xsync or /xcreate-skill after solving hard bugs or PR reviews, bloating playbooks past the 46,080-byte read_file truncation limit, or leaking single-project domain math into global SKILL.md laws.
11. XSync
/xsync (/update-doc, /doc-sync, /audit-docs)		 Exhaustive multi-file and cross-repository synchronizer with bundled scripts/audit_workspace_docs.py (533 LOC). Applies a 4-Level Conflict Arbitration Hierarchy (Level 1: Verified Non-Fallback Code & Runtime Receipts [source != "fallback", N_proc == N_exp, UL-04 Partition Conservation] > Level 2: Canonical SSOT > Level 3: Session User Lock > Level 4: Drafts), 3-Signal Bounded Up-Tree & Cross-Repo Mirror Federation (--federated) (auto-discovering all 4 Compound Learning Playbooks, sibling git repositories, global ~/.gemini/config/skills/ mirrors, GITHUB_PUBLISHING_PROTOCOL.md, and org/profile READMEs while cleanly separating repo-local skills/ from host agent roots), verifies relative links, Zero-LaTeX compliance, SHA-256 mirror parity, and --deprecated sweeps across Tiers 1-5, and outputs a verified Synchronization Ledger. Touching 1-2 surface files while leaving stale numbers in sibling repos, false-positive ghost-skill errors on domain repos (mcp-bridges/skills/*), propagating mock/fallback metrics into docs, or breaking HTML slide/card counters.
12. XCreate-Skill
/xcreate-skill (/create-skill)		 4-Step Cognitive Distillation Protocol (Modes A & B — Greenfield Skill Creation, Distillation & Auditing): (1) Universal Globalization into ~/.gemini/config/skills/<name>/ and all active agent roots, (2) Context Budget Guard (≤ 400 chars YAML description with explicit Use when... and Do NOT use for... triggers so host IDEs never drop sibling skills), (3) 5-Gate Universal Law Falsification Protocol (Agent-Process Law vs. Domain-Equation Inversion, Global CLI Script Ban blocking project domain math in skills/*/scripts/*.py, Pruned Project-Specific Ideas ledger, 3-Domain Translation Proof Table, and Gate 5 Pointer-Over-Paste Single-SSOT Reference Rule delegating post-session Mode C ratcheting to /xlearn), and (4) mandatory --platform skill audit. Workspace-siloed skills, bloated frontmatter descriptions (> 400 chars), duplicating 9-point mathematical tables across multiple skills, and unverified helper scripts.
13. XPublish
/xpublish (/xpublish-audit, /xpublish-qgis, /publish)		 Unified 6-Pillar Pre-Flight Release Gate, Upstream PR Convergence & QGIS 4 / Qt6 Packager (skills/xpublish/scripts/audit_engine.py 1,093 LOC, check_qt6.py 422 LOC, and package_qgis.py 587 LOC). Supports --platform pr|github|qgis|food4rhino|skill|generic (including diff-scoped audit_pr_diff_invariants and multi-skill suite root recursion), Mixed CRLF / LF Upstream File Line-Ending Parity Guard (PAT-GIT-01), Upstream Governance Fingerprint Verification (GOV-01..07), Tier B Human-Consent Dispatch Gate, 16 high-entropy secret patterns (pat.finditer() multi-token scan), hardcoded user paths (C:\Users\..., /Users/... with negative lookbehind), Python AST security risks (eval, shell=True, pickle), Flake8 compliance, Gate 3 Over-Specific Law Detector (Check 4B), ≤ 500-Line Sprawl Gate (Check 4C), Raw LaTeX Macro Scanner (Check 4D), Tri-OS Clipboard Protocol (CF_UNICODETEXT / pbcopy / wl-copy), GitHub Repository Publishing Protocol v1.4 (references/github_readme_protocol.md), and 5-Gate Deterministic QGIS Release Packaging (references/qgis_release_protocol.md). Leaked API keys, placeholder-masked secret leaks on multi-token lines, accidental whole-file CRLF ↔ LF diff pollution (3,956-line PR diffs), missing DCO/companion changelog files, > 30 KB SVG truncation, > 500-line SKILL.md sprawl, and Qt5 unscoped enum crashes (QDialog, QDialogButtonBox, QAbstractItemView) in QGIS 4.

Stage 5: Session Continuity

Skill & Command (Aliases) Core Architecture & Protocol Failure Modes Prevented
14. XContext
/xcontext (/context)		 Runs scripts/check_context.py (669 LOC) against the active IDE conversation transcript (transcript.jsonl) and SQLite step logs (conversations/*.db), unwrapping .system_generated/logs paths to parent <uuid>, filtering out isolated subagent logs, reporting Session & Cross-Platform Token Telemetry (--tokens, --days N, --all-time across Antigravity 2.0, IDE, and CLI), and evaluating a 2D Health Rubric (Compaction Round × Active Window Pressure %) across strict CHECKPOINT boundaries (🟢 HEALTHY, 🟡 NOMINAL, 🟠 CAUTION, 🔴 CRITICAL). Continuing complex edits after multi-pass compactions, subagent transcript hijacking, bytecode .pyc pollution, or invisible cross-platform token usage.
15. XKickoff
/xkickoff (/kickoff)		 End-of-session handover protocol. Runs a pre-handover /xsync (or /xlearn when new session bugs/optimizations were solved) verification check, presents a structured Next-Step Options Table (Option A Recommended, Option B, Option C), applies the Task-Smart Recommended Command Selector (auto-routing /xlaya, /xlearn, /xmouse, and /xultra only when task signals match), and generates a self-contained copy-pasteable Kickoff Prompt. Starting a fresh conversation with vague context, losing mid-session decisions, or leaving unsynchronized documentation behind.

4. Deep-Dive Engineering Pillars

4.1 2D Goal Contract & Context-Conditioned Single-Layer Universal First-Principles Laws (UL-01..UL-08 in /xboost)

Before touching code on non-trivial tasks, /xboost locks a 2D Goal Contract crossing 6 Task Archetypes (DEBUG/RCA, BUILD/FEATURE, SIMULATE/SOLVER, REFACTOR/SIMPLIFY, OPTIMIZE/SCALE, AUDIT/PLAN/SHIP) with 5 Universal System Verification Dimensions (Continuous Numerical & Physical Solvers, Discrete Graph, Partition & Relational Pipelines, UI, Visual & Interactive State Machines, Distributed, Concurrent & Stateful Services, Declarative Schemas, Compilers & CLI Harnesses) governed by the 8 Context-Conditioned Single-Layer Universal First-Principles Laws (UL-01..UL-08) in skills/xboost/references/deep_audit_protocol.md:

  1. UL-01 (Bracketed Support, Scale Conditioning & Graph-Cut Isolation): Enforces bracketed domain support (x_left ≤ x ≤ x_right), explicit scale conditioning, and 0.0 future/cross-cut information leakage.
  2. UL-02 (Exact-Condition Refinement & Absorbing Failure Precedence): Enforces exact predicate refinement (S_exact = {x ∈ S_cand : P(x)}) and pessimistic absorbing failure precedence (Failure > Success tie-breaking).
  3. UL-03 (Step-Measure Scaling Convergence & Zero-Forcing Equilibrium Identity): Enforces step-measure convergence (O(Δμ^p)) and exact zero-forcing equilibrium identity (Δx = 0 ⇒ Δy = 0.0 / y == y_eq with 0 NaN/Inf).
  4. UL-04 (Extensive Partition, Remainder Conservation & Intended Peer-Branch Routing Completeness): Enforces extensive partition conservation (∑ Child_k + Remainder ≈ Parent) and peer-branch routing completeness (K_peer ⊆ K_eval, 0 blocking-continue drops).
  5. UL-05 (Bidirectional Counter-Anchors, Strict Feasibility Dominance & Goal-Matched Objective/Constraint Polarity): Enforces simultaneous counter-anchors (A_pos ∧ A_neg), strict feasibility dominance (J(x_infeas) ≺ J(x_feas)), non-zero marginal sensitivity (|∂J/∂M| > 0 on unbounded objectives), goal-matched dispersion (symmetric MSE/σ for setpoint tracking vs one-sided σ_fail for directional goals), and role-matched binary / continuous / budgeted constraints.
  6. UL-06 (Piece-Wise Codomain Reachability, Marginal Seam Scaling Order & Non-Degeneracy): Verifies live reachability of every declared codomain branch (count(Branch_k) > 0), interior marginal seam scaling (|ΔY(α_2)/ΔY(α_1)| ≈ (α_2/α_1)^p_theory without accidental α² double-application), and non-degeneracy.
  7. UL-07 (Read-Only Feature-Stream Decoupling, Cache Idempotency & Post-Gate Non-Regression): Prevents T(T(x)) cache compounding on shared feature streams and guarantees post-gate non-regression.
  8. UL-08 (Live-Entrypoint Parity, Active-Regime Sensitivity & Bounded Complexity): Requires invoking the live production entrypoint directly (∃ x ∈ Active_Regime(X) : ΔY ≠ 0, N_proc == N_exp, source != "fallback") under bounded complexity.

4.2 Unified 3-Tier Research & 5-Wave Adversarial Fact Auditing (/xdeep-research)

Standard LLM web research either spawns heavy subagents for 10-second SDK lookups or hallucinates metrics from search snippets on deep studies. /xdeep-research unifies 3-Tier Adaptive Scope Routing (Tier 1: Fast Inline Probe, Tier 2: Single Isolated Scout, Tier 3: 5-Wave Deep Research Swarm) with 4-Tier Primary-Source Authority and Blind Adversarial Verification:

  • Tier 1-2 (Tactical Primary-Source Probe): Checks local pinned manifests (package.json, pyproject.toml, *.csproj, metadata.txt) and local SQLite/AST/CLI probes before querying external documentation.
  • Wave 1 (2D Contract & Live HEAD / Collision Pre-Gate): Locks domain axes, minimum record quotas, disambiguation rules, and upstream governance/collision checks (GOV-01..07, FALS-01..11).
  • Wave 2 & 2.5 (Harvester Swarm & Rejected-Candidate Graveyard Forensics): Parallel scouts scrape full page bodies and deep links under strict capability-aware write-permission rules, while Wave 2.5 inspects pruning logs, rejected candidate archives, and post-processing dampers via zero-retrain counterfactual ablation before proposing new external architectures or expensive retraining.
  • Wave 3 & 3.5 (Blind Adversarial Audit + Prune & Replace): An independent auditor runs scripts/verify_evidence.py to reject [TBD] placeholders, root-domain URLs, and schema violations, triggering replacement harvesting until the quota is 100% verified.
  • Wave 4 (100%-Proof Synthesis): Every claim is explicitly tagged as [VERIFIED FACT] (backed by a primary receipt) or [RECOMMENDED SYNTHESIS] (engineering inference).

4.3 Public Claim Vocabulary (/xsync & /xpublish)

Both /xsync and /xpublish enforce five canonical status terms across all public READMEs and portfolios:

  • LIVE: Publicly installable, downloadable, or accessible via URL right now.
  • REGISTERED: Registered on an official portal; public version or review pending.
  • PREPARED: Built and verified locally; public release package ready next.
  • IN PIPELINE: Scheduled next in the active engineering roadmap.
  • DRAFT: Internal working specification.

4.4 3-Tier Cross-Workspace Compound Learning Framework & /xlearn Ratchet (skills/xlearn/SKILL.md & docs/COMPOUND_LEARNING_PLAYBOOK.md)

XBoost v3.5.0 elevates compound learning into a dedicated first-class skill (/xlearn) backed by a 3-Tier Playbook Bridge Cascade where every playbook is strictly bounded < 35 KB (well below the 46,080-byte single-pass read_file truncation limit):

[Phase 1: Playbook Pre-Check (/xbrief + /xdeep-research)] → [Phase 2: First-Principles Audit (/xboost UL-01..08)] → [Phase 3: Parallel Patch + Blind Critic (/xultra)] → [Phase 4: Pre-Flight PR Gate (/xpublish)] → [Decision Gate: /xlearn Tier A (Autonomous Playbook + /xsync --federated) vs Tier B (5-Gate Universal Law Sieve + Human Consent)]
  • Tier A — Autonomous 3-Tier Playbook & Federated Doc Ratchet (/xbrief ↔ /xlearn → /xsync --federated):
    • Tier 1 Core Hub (docs/COMPOUND_LEARNING_PLAYBOOK.md, 34.9 KB): Codifies 19 Cross-Domain Engineering Defect & Bottleneck Archetypes (PAT-* across 16 families), 7 Institutional CI Governance Fingerprints (GOV-01..07), and 11 Universal Falsification & Mirage Guardrails (FALS-01..11).
    • Tier 2 Specialized Domain Playbooks (< 35 KB Each): Cascades seamlessly into Tier 2A (GitHub PR & OSS Protocol), Tier 2B (Plant Allometry & 3D Botanical Modeling Protocol, PAT-PLANT-01..07), and Tier 2C (Quant Trading & Evolution Ratchet Protocol, PAT-QUANT-01..12). Running /xlearn automatically updates the target playbook and invokes /xsync --federated (audit_workspace_docs.py 533 LOC) so SHA-256 mirrors and sibling indices never drift.
  • Tier B — Report-First Human-Consent Gate (/xlearn & /xpublish): Modifying global agent skills (skills/*/SKILL.md, UL-01..UL-08, or global scripts/*.py) or dispatching public upstream GitHub PRs requires passing the 5-Gate Universal Law Falsification Sieve (3-Domain Translation Proof Table + 9-Point Mathematical Boundary Table) + Wave 2A Blind Counter-Domain Critic and presenting a Before → After report for explicit human approval.

4.5 Empirical Benchmark & Token Economics (benchmarks/BENCHMARK_REPORT.md)

Reproducible multi-trial measurements (3 warm-ups + 20-30 timed iterations via benchmarks/run_xboost_benchmark.py) across 115 installed skills, 834 repository/vault markdown files (602-node Obsidian Second Brain), 25 seeded red-team defects, 442 real IDE sessions (810 prompt turns), and 22 real-world upstream open-source contributions (5 MERGED):

Benchmark Dimension Unstructured / Prompt-Only Baseline XBoost v3.5.0 Composable /x* Suite Empirical Delta & Receipt
1. System-Prompt Discovery Guard 96.8% ≤ 500-char pass • 87.1% dual triggers (Official Cloud/Vendor cohort, N=31; All Installed N=115: 324.78 ± 51.73) 374.47 ± 9.95 chars (100% ≤ 400-char & ≤ 500-char pass • 100% Use when + Do NOT use guards across all 15 /x* skills) 0% Sibling Skill Truncation
2. Briefing & Vault Context Compression 834 files (1,639,875 tokens in Median 51.78 ms) /xbrief (Cap 8) + /xobsidian (2-Hop = 4 files) = 52,766 tokens in 1.23 ms (48,226 tok / 1.13 ms Repo-Only) -96.78% Context Noise (31.1×; -97.06% / 34.0× Repo-Only)
3. System 1 → 1.5 /xlaya Section & GPU/MCP Triage Full-corpus read (175,055 tokens across 27 specs / 1,172 chunks) Top-5 #L<start>-L<end> (1,137 tok) in Median 171.59 ms (6,830.2 chunks/s) + ~30 ms warm MCP (859.1 q/s GPU batch, 8.9× mixed EN/HI, 90.0% vega-U routing) -99.35% Pass-B Tokens (154.0×) • 100% Joint NLI Triad • 0.00% XERJ #1108
4. 25-Defect Red-Team Catch Rate 0 / 25 (0.0% deterministic pre-LLM enforcement) 25 / 25 (100.0% Recall • 100.0% Precision • 0 False Positives) in Median 8.01 ms 10 Bundled CLI & MCP Engines (10,382 LOC)
5. Real Turn & Session Token Depth 23.0 steps/turn (13.6k new input • 5.6k output • 1.26M total/turn • 1.54M tok/session) 90.4 steps/turn (3.94×) • 93.3k new input (6.87×) • 24.3k output (4.33×) • 4.87M tok/turn • 39.67M tok/session (25.7×) Recommended: 5× Max (Sprints) / 20× Ultra (Swarms & Marathons)
6. Hybrid PDF, booktabs & VLM HTML Table Extraction (/xocr v2.1.0) 0 / 6 tables when pdfplumber is absent; 1-row collapsed booktabs cells; unparsed HTML <table> tags; 5.11× VLM upscale token bloat 19-97 ms/page vector (15.0×-3,326× faster) + -35.9% vision tokens + +9.11% Table EditSim (50.75%) & HTML <table> → CSV surgery via local SCB 10X Typhoon-OCR v1.5 100% Vector & Scanned Table Fidelity (Upstream PR #15 MERGED)
7. Compound Learning & Upstream Bottleneck Acceleration (22 Contributions) Ad-hoc cold-start scanning (O(N²) list/label loops, np.vectorize wrappers, CRLF diff pollution, silent HEAD mirages) /xlearn 3-Tier Playbooks (PAT-* + GOV-* + FALS-*) + /xboost (UL-01..08) + /xpublish --platform pr across Batch 0, Group A, Group B & Group C 4.0× – 1,735.6× Verified Speedups (100% Numerical Parity, 5 MERGED, 9 OPEN PRs)

Empirical Upstream Open-Source Speedup & Correctness Receipts (Batch 0 + Group A + Group B + Group C — 22 Contributions)

Upstream Repository & Target Pattern & Governance Applied Baseline Bottleneck / Defect XBoost Vectorized / Surgical Fix Measured Speedup & Test Receipt
ladybug-tools/uwg #292 (MIT, MERGED) PAT-VEC-03, PAT-CACHE-01, PAT-GIT-01, GOV-02 sum(soil_temp) inside 105,120-step loop (8.85 s); hardcoded [0] climate zone (#291); windProf leak Reuse precomputed forcIP.deepTemp; index [zone_idx]; reset windProf per step; preserve CRLF (38-line diff) 1,735.6× speedup (8.85 s → 5.10 ms) • 68/68 pytest PASS • MERGED
os-climate/physrisk #651 (FINOS / Linux Foundation) PAT-VEC-01..02, PAT-NUM-01, GOV-01 O(N²) list comprehension in geocode_in_place (541.3 ms); np.vectorize in get_impact_distrib (7.15 s); caller array mutation & left = -1 wrap Direct key iteration (0.32 ms); broadcasted scipy.stats.norm.cdf (0.22 s); non-mutating np.minimum; clamp np.maximum(0, right - 1) 1,669.6× & 32.5× speedups • 11/11 pytest PASS (DCO -s, unbundled staged)
Weecology/DeepForest #1449 & #1440 PAT-VEC-01, PAT-GIS-01, GOV-07 Row-wise .iterrows() + shapely.geometry.box loops in utilities.py, IoU.py, evaluate.py; PathLike rejection (#1440) Vectorized shapely.box & STRtree (421.3× convert_to_sv_format, 173.9× IoU.match_points, 74.4× utilities.py); os.fspath() 74.4× – 421.3× speedups • CI 3.12 PASS (closes #1447)
CLIMADA-project/climada_python #1349 & #1350 (ETH Zürich) PAT-VEC-01, PAT-NUM-01, GOV-04 Scalar box() loop in points_to_raster (1.49 s); CSR column-slice loop in impact_at_reg; .nonzero() explicit-zero drop; ISO/NaN bugs (#1331) Vectorized shapely.box (69.9× isolated apply_box, 2.1× E2E); imp_mat @ reg_ind CSR matmul (11.7× E2E); fallback ISO & isnan guards 69.9× & 11.7× speedups • 17/17 tests PASS (base: develop)
gboeing/osmnx #1412, #1410 & #1405 PAT-GRAPH-01..02, PAT-NUM-01, GOV-07 Zero-length self-loop ZeroDivisionError in stats.circuity_avg (#1410); pytest-xdist state collision in test_simplify_graph & test_elevation (#1405) Guard edge_length_total == 0 returning np.float64(0.0) (PR #1412 invited by @gboeing); 40.2× vectorization; xdist_group marker 40.2× speedup • PR #1412 LIVE (100% CI PASS)
Deltares/hydromt #1566 (Deltares Netherlands) PAT-VEC-01, PAT-GIS-01, GOV-03 Scalar box() loop in create_mesh2d_from_geom (897 ms); dropped crs on projected bbox (#1563); nodata_ref = None before warning log C-vectorized shapely.box (32.6 ms); pass crs=self.metadata.crs & reproject geom; log nodata_ref before reset 27.5× speedup (897 ms → 32.6 ms) • 114/114 pytest PASS
natcap/invest #2813 & #2829 (Stanford NatCap) PAT-VEC-03, PAT-GIS-01, PAT-GIT-01, GOV-05 Repeated OGR GetFeature(fid) + wkb.loads inside AOI R-tree loops (420.2 ms UCM, 421.6 ms UFRM); & vs | nodata mask Pass-1 FID geometry cache (23.1× UCM +0.51 MB RAM) & Inverted AOI R-tree (18.1× O(N) / 2.0× O(M) 0.18 MB RAM UFRM); De Morgan | mask 23.1× UCM & 18.1× UFRM speedups • Parity verified (on hold)
martibosch/pylandstats #77 (MERGED) PAT-VEC-02, PAT-GIS-01 Per-label scipy.ndimage.center_of_mass scan (79.85 ms on 66,496 patches); landscapemetrics-style string edge_depths (#69) Single-pass vectorized np.bincount(labels.ravel(), weights=...) (19.76 ms) + string edge_depths support (#69) 4.0× speedup (79.85 ms → 19.76 ms) • 9/9 pytest PASS • MERGED
opengeos/geoai #960 (MERGED), segment-geospatial #569 & mufeedvh/code2prompt #348 PAT-PARITY-01, PAT-CV-01, GOV-06 Host PYTHONEXECUTABLE venv bleed (#951); missing SamGeo vector attributes (#543); Rust glob/token traversal in code2prompt Sanitize PYTHON* env vars (geoai#960 MERGED as 0b13f11); SamGeo attributes (10/10 CI, @coderabbitai APPROVED); code2prompt#348 (9/9 cargo test) geoai#960 MERGED • segment-geospatial#569 & code2prompt#348 LIVE
xerj-org/xerj #2922–#2927 & scb-10x/typhoon-ocr #15 (MERGED) PAT-OCR-01, PAT-PARITY-01 Sub-max image upscale (5.11× token bloat); legacy Thai PDF PUA junking (exit 3); Windows MSVC #[cfg(unix)] & spool lock test failures Downscale-only resize_if_needed (typhoon-ocr#15 MERGED); Rust/Python Thai PUA + Sara-Am normalizer (xerj#2925 MERGED); .gemini/config/skills discovery -35.9% vision tokens • 1,912/1,954 MSVC tests • 2 MERGED

Transparency on Token Consumption: XBoost reduces low-value file-scanning noise by 96.78%, then deliberately invests that freed context capacity into 3.94× more verification steps and 6.87× more primary-source & CLI telemetry inputs per chained turn. While single-skill runs work comfortably on 1× Pro plans (or local LLM endpoints), daily chained workflows (/xbrief /xdeep-research /xboost /xlearn /xsync /xobsidian) are best paired with 5× High-Throughput tiers (Claude Max 5×, Gemini 5×), and 400+-step autonomous /goal marathons or /xultra / /xdeep-research multi-agent swarms are engineered for 20× Ultra tiers (Gemini Ultra 20×, Claude Max 20×).

LaTeX (booktabs) Source for Academic Citations — XBoost v3.5.0 Empirical Benchmark Summary 
\begin{table}[htbp]
\centering
\caption{XBoost v3.5.0 Empirical Evaluation across Skill Discovery ($N=115$), Context Compression ($n=30$), Red-Team Recall ($N=25$), Session Telemetry ($N=442$ / $810$ Turns), and Upstream OSS Contributions ($N=22$).}
\label{tab:xboost_empirical_summary}
\begin{tabular}{llcc}
\toprule
\textbf{Benchmark Dimension} & \textbf{Unstructured / Vendor Baseline} & \textbf{XBoost v3.5.0 (\texttt{/x*} Suite, $N=15$)} & \textbf{Empirical Delta} \\
\midrule
1. System-Prompt Discovery Guard & $96.8\%$ $\le 500$-char $\cdot$ $87.1\%$ dual guards & \textbf{$374.47 \pm 9.95$ chars} ($100\%$ dual guards) & \textbf{$0\%$ Sibling Truncation} \\
2. Briefing \& 2-Hop Vault Compression & $834$ files ($1,639,875$ tok in $51.78\text{ ms}$) & \textbf{$12$ files ($52,766$ tok in $1.23\text{ ms}$)} & \textbf{$-96.78\%$ Noise ($31.1\times$)} \\
3. System 1 \texttt{/xlaya} Section Triage & $175,055$ tok ($27$ specs / $1,172$ chunks) & \textbf{Top-5 ($1,137$ tok in $171.59\text{ ms}$; $\sim 30\text{ ms}$ MCP)} & \textbf{$-99.35\%$ Tok ($859.1\text{ q/s}$ GPU)} \\
4. 25-Defect Red-Team Catch Rate & $0 / 25$ ($0.0\%$ pre-LLM enforcement) & \textbf{$25 / 25$ ($100.0\%$ Recall in $8.01\text{ ms}$)} & \textbf{10 CLI/MCP Engines ($10,382$ LOC)} \\
5. Real Turn \& Session Token Depth & $23.0$ steps/turn ($1.54\text{M}$ tok/session) & \textbf{$90.4$ steps/turn ($39.67\text{M}$ tok/session)} & \textbf{$3.94\times$ Verification Depth} \\
6. Hybrid PDF \& Table Engine (\texttt{/xocr}) & $0/6$ tables (no \texttt{pdfplumber}) $\cdot$ raw HTML & \textbf{$19\text{--}97\text{ ms/p}$ vector ($15\text{--}3326\times$) + HTML CSV} & \textbf{$-35.9\%$ Tok / $+9.1\%$ Tab (PR \#15)} \\
7. Compound Learning \& OSS Speedups & Cold-start $O(N^2)$ / \texttt{np.vectorize} loops & \textbf{\texttt{/xlearn} 3-Tier Playbooks ($22$ Contributions)} & \textbf{$4.0\times\text{--}1735.6\times$ Speedup ($5$ Merged)} \\
\bottomrule
\end{tabular}
\end{table}

5. Bundled Zero-Dependency CLI Engines

All ten bundled Python CLI & MCP engines (10,382 total LOC) run on Python 3.8+ standard library (ast, re, json, zipfile, urllib, configparser, hashlib, pathlib, ctypes, with optional laya / vegaml acceleration in laya_triage.py & xlaya_mcp_server.py and PyMuPDF/EasyOCR in ocr_runner.py) with UTF-8 stream guards (sys.stdout.reconfigure(encoding='utf-8')) and zero bytecode pollution (sys.dont_write_bytecode = True):

# 1. Pre-flight security, secret, AST, Flake8, PR diff, CRLF parity & platform audit (/xpublish)
python skills/xpublish/scripts/audit_engine.py <target_dir> --platform pr --base-ref origin/main
python skills/xpublish/scripts/audit_engine.py <target_dir> --platform github
python skills/xpublish/scripts/audit_engine.py skills/<skill_name> --platform skill

# 2. Repo-agnostic workspace documentation, link, SHA-256 mirror, 4-playbook federated & stale-term auditor (/xsync)
python skills/xsync/scripts/audit_workspace_docs.py <workspace_dir> --federated --deprecated old_term

# 3. Deep-research evidence, deep-link URL & JSON schema verifier (/xdeep-research)
python skills/xdeep-research/scripts/verify_evidence.py <report.md> <dataset.json> --min-items 10 --check-http

# 4. Qt6 / QGIS 4 forward-compatibility AST scanner (/xpublish)
python skills/xpublish/scripts/check_qt6.py <plugin_dir>

# 5. Deterministic single-root QGIS plugin release packager (/xpublish)
python skills/xpublish/scripts/package_qgis.py <plugin_dir> --bump patch

# 6. IDE conversation transcript compaction, context health & cross-platform token telemetry (/xcontext)
python skills/xcontext/scripts/check_context.py
python skills/xcontext/scripts/check_context.py --days 30
python skills/xcontext/scripts/check_context.py --all-time

# 7. 3-stage System 1 -> System 1.5 breadcrumb chunker, Joint NLI contradiction & vega-U conformal triage (/xlaya)
python skills/xlaya/scripts/laya_triage.py <target_dir_or_file> --mode shortlist --query "<topic>"
python skills/xlaya/scripts/laya_triage.py <target_dir_or_file> --mode contradiction --ssot "<canonical truth>" --vega

# 8. High-speed C++ PyMuPDF vector/booktabs table extractor & 4-tier VLM/OCR engine (/xocr)
python skills/xocr/scripts/ocr_runner.py <pdf_or_image> --output ./ocr_out --mode auto --pages "1-10"

# 9. Non-intrusive Ghost Mouse, UIA COM inspector, Set-of-Mark vision capture & Multi-Agent Visual Cursor HUD (/xmouse)
python skills/xmouse/scripts/xmouse_cli.py windows
python skills/xmouse/scripts/xmouse_cli.py uia --hwnd <HWND> --depth 4
python skills/xmouse/scripts/xmouse_cli.py click --hwnd <HWND> --x 420 --y 310 --mode ghost

# 10. Resident XLaya JSON-RPC 2.0 stdio MCP Server (~30 ms warm Laya 0.4.31+ & vega-U inference with idle VRAM auto-unload) (/xlaya)
python skills/xlaya/scripts/xlaya_mcp_server.py

6. Universal Installation

Every XBoost skill uses portable Markdown (SKILL.md) with standard YAML frontmatter (name + description) and pure Python 3.8+ standard-library CLI engines. The exact same files work across any AI coding agent — cloud, IDE, CLI, or local LLM (Ollama, vLLM, LM Studio, OpenHands, Cline, Continue, Aider, Claude Code, Codex, Cursor, Grok, Gemini / Antigravity, or custom agent frameworks) on Windows, macOS, and Linux:

Host / Agent Platform Global Skill Root Workspace Skill Root Verification Status
Gemini / Antigravity ~/.gemini/config/skills <repo>/.agents/skills tested (Live E2E verified on Windows 11 across all 15 /x* skills & 10 Python CLI/MCP engines)
Claude Code ~/.claude/skills <repo>/.claude/skills spec-compatible (Path & skill schema verified via scripts/install.ps1 & scripts/install.sh; live host PRs welcome)
Cursor / Codex / Windsurf ~/.agents/skills / ~/.codex/skills <repo>/.agents/skills spec-compatible (Path & skill schema verified via scripts/install.ps1 & scripts/install.sh; live host PRs welcome)
Grok $GROK_HOME/skills or ~/.grok/skills <repo>/.grok/skills spec-compatible (Path & skill schema verified via scripts/install.ps1 & scripts/install.sh; live host PRs welcome)
Local LLM & Custom Agent Frameworks (Ollama, vLLM, LM Studio, OpenHands, Cline, Continue, Aider) ~/.agents/skills or $AGENT_SKILLS_DIR (-Target <dir> / --target <dir>) <repo>/.agents/skills spec-compatible (Universal Markdown SKILL.md + stdlib Python CLI harness; supports custom target directories via -Target / --target)

Option A: One-Prompt Copy-Paste Agent Install (Works with Any AI Agent — Cloud, IDE, CLI, or Local LLM)

Copy and paste this single prompt into any AI coding agent (Claude Code, Cursor, Codex, Gemini/Antigravity, Grok, Cline, Continue, OpenHands, or a local LLM agent):

Install iscvlab/xboost (https://github.com/iscvlab/xboost): detect my OS and your active global or workspace skills directory, clone the repository, install all 15 /x* skills (Windows: ./scripts/install.ps1 -All; macOS/Linux: ./scripts/install.sh --all; or copy skills/* directly into your active skills folder), run python skills/xpublish/scripts/audit_engine.py skills/xboost --platform skill to verify the CLI harness, and confirm all 15 /x* commands are ready.

Option B: Automated Multi-Agent Installer (install.ps1 -All / install.sh --all / -Target <dir>)

Both installers automatically remove deprecated legacy (*x, unlimited-ocr, xpublish-audit, xpublish-qgis) directories before installing the 15 /x* skills across all detected agent roots (or any custom -Target / --target / $AGENT_SKILLS_DIR path).

Windows (PowerShell):

git clone https://github.com/iscvlab/xboost
cd xboost
./scripts/install.ps1 -All
# Or install into any custom / local LLM agent directory:
./scripts/install.ps1 -Target "$HOME/.continue/skills"

macOS / Linux (Bash / Zsh):

git clone https://github.com/iscvlab/xboost
cd xboost
chmod +x scripts/install.sh
./scripts/install.sh --all
# Or install into any custom / local LLM agent directory:
./scripts/install.sh --target ~/.continue/skills

Option C: Direct Copy into Any Agent Skill Root (~/.agents/skills, ~/.<agent>/skills, or <repo>/.agents/skills)

git clone https://github.com/iscvlab/xboost

# Universal .agents root (Cursor, Codex, Windsurf, Cline, OpenHands, Local LLM Agents)
mkdir -p ~/.agents/skills && cp -r xboost/skills/* ~/.agents/skills/

# Claude Code
mkdir -p ~/.claude/skills && cp -r xboost/skills/* ~/.claude/skills/

# Grok
mkdir -p ~/.grok/skills && cp -r xboost/skills/* ~/.grok/skills/

# Google Gemini / Antigravity
mkdir -p ~/.gemini/config/skills && cp -r xboost/skills/* ~/.gemini/config/skills/

# Project-local workspace (.agents/skills)
mkdir -p .agents/skills && cp -r xboost/skills/* .agents/skills/

To pin the v3.5.0 release tag:

git clone --branch v3.5.0 https://github.com/iscvlab/xboost

7. Repository Layout

xboost/
├── README.md
├── CHANGELOG.md
├── LICENSE
├── llms.txt
├── .flake8
├── docs/
│   └── COMPOUND_LEARNING_PLAYBOOK.md
├── benchmarks/
│   ├── run_xboost_benchmark.py
│   ├── BENCHMARK_REPORT.md
│   └── BENCHMARK_RESULTS.json
├── scripts/
│   ├── install.ps1
│   └── install.sh
└── skills/
    ├── xboost/
    │   ├── SKILL.md
    │   └── references/deep_audit_protocol.md
    ├── xbrief/
    │   └── SKILL.md
    ├── xcontext/
    │   ├── SKILL.md
    │   └── scripts/check_context.py
    ├── xcreate-skill/
    │   └── SKILL.md
    ├── xdeep-research/
    │   ├── SKILL.md
    │   └── scripts/verify_evidence.py
    ├── xkickoff/
    │   └── SKILL.md
    ├── xlaya/
    │   ├── SKILL.md
    │   └── scripts/
    │       ├── laya_triage.py
    │       └── xlaya_mcp_server.py
    ├── xlearn/
    │   └── SKILL.md
    ├── xmouse/
    │   ├── SKILL.md
    │   └── scripts/xmouse_cli.py
    ├── xobsidian/
    │   └── SKILL.md
    ├── xocr/
    │   ├── SKILL.md
    │   └── scripts/ocr_runner.py
    ├── xplan/
    │   └── SKILL.md
    ├── xpublish/
    │   ├── SKILL.md
    │   ├── references/
    │   │   ├── github_readme_protocol.md
    │   │   └── qgis_release_protocol.md
    │   └── scripts/
    │       ├── audit_engine.py
    │       ├── check_qt6.py
    │       └── package_qgis.py
    ├── xsync/
    │   ├── LICENSE
    │   ├── SKILL.md
    │   └── scripts/
    │       ├── README.md
    │       └── audit_workspace_docs.py
    └── xultra/
        └── SKILL.md

8. Related Toolkits & Author

  • xlaya (v2.1.0 LIVE · Release Tag): Standalone 3-stage System 1 → System 1.5 semantic decision, ensemble jury & vega-U conformal triage package (laya 0.4.31+ & vegaml 0.16.1+), pairing zero-extra-dependency 6-tool resident JSON-RPC 2.0 stdio MCP warm inference (~30 ms) with 859.1 q/s GPU batch routing (8.9× mixed EN/HI), 0.45/0.55 laya-english + laya-typed-decisions ensemble jury, --thorough 4-facet rubric + file_summaries, --vega-views 1..3 debiasing, xlaya_fit persistent TTTHead conformal policy, local multimodal (image/audio/video), predict_tournament (>20 labels), 3-Question Joint NLI Contradiction Triad (P_eff), and 0.00% XERJ #1108 wrong-and-confident fires.
  • xocr: High-speed C++ PyMuPDF vector/table extractor, Two-Stage LaTeX booktabs & scanned HTML <table> → RFC-4180 CSV surgery (19-97 ms/page, 15.0×-3,326× faster hybrid routing), Thai PUA/Sara-Am normalizer, and 4-Tier local SCB 10X Typhoon-OCR v1.5 (Qwen3-VL) & baidu/Unlimited-OCR-2 engine (Upstream PR #15 to scb-10x/typhoon-ocr (MERGED) & Upstream PR #2925 to xerj-org/xerj (MERGED)).
  • mcp-bridges: Model Context Protocol (MCP) automation bridges for QGIS, Rhino 3D, Grasshopper, Adobe Illustrator, Figma, and Ghost Mouse (Selected for the XERJ Corpus Hub).
  • sse-space-syntax: High-performance Space Syntax, Angular Segment Analysis (ASA), Axial/Stroke topology, Visibility Graph Analysis (VGA), and Ray-Cast Isovist engine (v1.1.1 LIVE).
  • qgis-axonometric-transformer: Official QGIS plugin (Plugin ID: 6219, v1.2.2 LIVE) for 1-click isometric, dimetric, and oblique axonometric map diagrams.
  • 22 Upstream Open-Source Contributions (5 MERGED across Batch 0 + Group A + Group B + Group C):

Napat Phasundhiae
Computational Design Technologist | Spatial Analytics • Urban & Environmental Simulation • Workflow Automation

License

Released under the MIT License.

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XBoost (v3.5.0) — 15 Composable /x* Agent Skills, 3-Tier Compound Learning Playbooks & 9 Deterministic Python CLI Verification Engines

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