knowledge-source-recon
Reconnaissance pattern to inventory all knowledge sources across the workspace-hub ecosystem's existing intelligence infrastructure. Maps raw sources for LLM Wiki ingestion planning. Leverages pre-built registries and indexes rather than re-scanning directories.
Best use case
knowledge-source-recon is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Reconnaissance pattern to inventory all knowledge sources across the workspace-hub ecosystem's existing intelligence infrastructure. Maps raw sources for LLM Wiki ingestion planning. Leverages pre-built registries and indexes rather than re-scanning directories.
Teams using knowledge-source-recon should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/knowledge-source-recon/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How knowledge-source-recon Compares
| Feature / Agent | knowledge-source-recon | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Reconnaissance pattern to inventory all knowledge sources across the workspace-hub ecosystem's existing intelligence infrastructure. Maps raw sources for LLM Wiki ingestion planning. Leverages pre-built registries and indexes rather than re-scanning directories.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Knowledge Source Reconnaissance ## When to Use - Preparing raw source inventories for LLM Wiki implementation - Planning knowledge base consolidation across workspace-hub - Auditing what knowledge exists before building new pipelines - Creating GitHub issues for knowledge infrastructure work - Any task requiring a "what do we know and where is it" answer ## Core Principle DO NOT re-scan directories. The workspace-hub ecosystem already has comprehensive intelligence infrastructure — read the registries, catalogs, and reports that already exist. Re-scanning is wasteful and misses the registry metadata (classification, status, relationships). ## The Three Intelligence Systems The workspace ecosystem tracks knowledge across three layers: 1. **Document/Resource Intelligence** — Indexed local files (standards, conference papers, research literature, engineering refs) 2. **Online Intelligence** — Remote resources cataloged for future download (papers, tools, APIs, data portals) 3. **Repo Intelligence** — Engineering code, functions, standards implementations in digitalmodel repo ## Scan Procedure Read these specific files — they are the authoritative sources: ### Phase 1: Document/Resource Intelligence | File | What It Contains | Command | |---|---|---| | `data/document-index/index.jsonl` | Master document index (647K+ lines) | `wc -l` for line count | | `data/document-index/enhancement-plan.yaml` | Classified files by domain (1M+ files) | Parse YAML, read `by_domain` section | | `data/document-index/standards-transfer-ledger.yaml` | Standards tracking (status, impl, domains) | Read `summary` section | | `data/document-index/conference-index.jsonl` | Conference paper catalog (27K+ papers) | `wc -l` for count | | `data/document-index/conference-index-stats.yaml` | Conference stats per collection | `cat` for full stats | | `data/document-index/research-literature-report.md` | Domain-organized research PDFs | `cat` for full breakdown | | `data/document-index/engineering-refs-catalog.md` | Engineering reference files | `cat` for catalog | ### Phase 2: Online Intelligence | File | What It Contains | Command | |---|---|---| | `data/document-index/online-resource-registry.yaml` | 247 remote resources (tools, repos, papers, APIs) | Read `summary` section for breakdown | | `data/document-index/public-og-data-sources.yaml` | 38 data API/portal sources (ingested + pending) | Read `already_ingested`, `known_not_ingested`, `newly_discovered` | | `data/document-index/conference-paper-catalog.yaml` | Conference paper metadata catalog | `wc -l` for scope | | `data/document-index/intelligence-accessibility-registry.yaml` | Discoverability/accessibility map for intelligence assets; flags hard-to-discover registries/wikis | Read `assets` entries with `discoverability`/`gaps` | | `data/document-index/resource-intelligence-maturity.yaml` | Canonical progress/coverage ledger for resource-intelligence parsing and review | Read `status` section | | `data/document-index/resource-intelligence-maturity.md` | Human summary only; may be stale relative to YAML | Cross-check against YAML, do not treat as authoritative | ### Phase 3: Repo Intelligence | Path | What It Contains | Command | |---|---|---| | `knowledge/seeds/*.yaml` | Structured knowledge (career learnings, law cases, mooring failures, naval arch resources) | `ls -la` + count entries per file | | `knowledge-base/wrk-completions.jsonl` | Session work summaries (420 records) | `wc -l` | | `knowledge/dark-intelligence/` | Excel-to-YAML extraction outputs | `find -name "*.yaml" | wc -l` | | `digitalmodel/specs/module-registry.yaml` | Engineering function registry | `wc -l` for scope | | `digitalmodel/` repo stats | 7,355 public functions, 42 standards impl | Read README.md or capability report | ### Phase 4: Mounted Filesystem Sources | File | What It Contains | |---|---| | `data/document-index/mounted-source-registry.yaml` | 11 source roots with mount paths, dedup rules, availability checks | Read the `source_roots` list — each entry has `source_id`, `mount_root`, `local_or_remote`, and `canonical_storage_policy`. ## Output Format Produce a markdown table organized by intelligence system with columns: Source Name, Location, Scale/Count, Status, Notes. Always include a summary table with totals. ``` ## SCALE SUMMARY | Category | Count | Notes | |---|---|---| | Classified documents | 1,033,933 | 12 domains via enhancement-plan.yaml | | Conference papers | 27,735 | 30 collections | | Research literature | 174 | 12 domain folders | | Online resources | 247 | 221 pending download | | Data API sources | 38 | 20 ingestable | | Knowledge seeds | ~100 | YAML entries across 5 files | | Mounted filesystems | 11 | Local + remote mounts | | Engineering functions | 7,355 | digitalmodel repo | | Standards tracked | 425 | 424 indexed, 1 implemented | ``` ## Key Insights 1. **The largest remaining semantic gap is summary coverage, not raw indexing** — `data-audit-report.md` shows 1,033,933 indexed records but only 639,585 with summaries (61.9%), leaving 394,348 records needing context enrichment. 2. **Index-level `other` still hides 44,705 project/miscellaneous files** — even though standards-level `other` has been eliminated, the document index still has a large miscellaneous bucket worth targeted reclassification. 3. **221 of 247 online resources have `download_status: not_started`** — massive untapped source pool. 4. **Intelligence discoverability is itself a gap** — `intelligence-accessibility-registry.yaml` flags assets like `online-resource-registry.yaml` as hard-to-discover / not linked from navigation surfaces. 5. **Knowledge seeds are NOT all indexed** by query-knowledge.sh — maritime-law-cases, mooring-failures, naval-architecture-resources are excluded. 6. **The riser-eng-job mount has 15,449 PDF/DOC/DOCX files** across 4 projects (93GB) — a major literature source. 7. **DDE remote mounts have 18 unique standard orgs** not present in /mnt/ace (ASME, AWS, NACE, etc.). 8. **Session corpus (wrk-completions.jsonl, 420 records)** represents tacit institutional knowledge — perfect for wiki ingest once structured. 9. **`resource-intelligence-maturity.md` can be stale; YAML is authoritative** — on 2026-04-13 the Markdown still said 5 docs / 0 read while YAML showed 425 docs / 29 read / 6.8%. 10. **`enhancement-plan.yaml` may lag current audits** — it still reported `by_domain.other.count: 176,527`, while newer audit artifacts reported index-level `other` at 44,705 and standards `other` eliminated. ## Pitfalls - Do NOT attempt to `find` across /mnt/ace recursively — there are millions of files and it will hang - Do NOT parse index.jsonl directly (572MB) — read the summary YAML/MD files instead - Remote mounts (`/mnt/remote/`) may be unavailable — check mount status before attempting to read - Enhancement-plan.yaml may be stale relative to later audit artifacts; verify against `data-audit-report.md` before citing `other` counts - `resource-intelligence-maturity.md` is a convenience summary only; always trust the YAML ledger if numbers disagree - Enhancement-plan.yaml is large — parse selectively, don't dump it whole - Knowledge/dark-intelligence YAML files are gitignored — they exist locally but may not be on all machines ## Related Skills - `llm-wiki` — the target system this inventory feeds into - `knowledge-pipeline` — existing knowledge workflow skeleton - `document-inventory` — generic single-directory scanner (don't use for workspace-hub recon)
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