hoadoan1997/llm-knowledge-base/compile-ingest

Claude Code and agent skill by hoadoan1997/llm-knowledge-base

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Use when ingesting any raw file into the wiki. Covers what the AGENTS.md compile checklist steps actually mean, image handling, concept extraction rules, and when to apply which long-doc strategy.

Install

npx vskill@latest install hoadoan1997/llm-knowledge-base/compile-ingest

SKILL.md

Skill: compile-ingest

Follow the compile checklist in AGENTS.md. This skill fills in the what behind each step.


fetch-url: command

When you receive fetch-url: <url> [name]:

bash
./tools/fetch-url.sh "<url>" [name]

The script auto-detects static vs JS page:

  • Static page (≥200 words) → uses requests + markdownify — fast, token-efficient
  • JS-rendered page (<200 words) → falls back to r.jina.ai automatically
  • Force JS: ./tools/fetch-url.sh "<url>" --force-jina

After the script saves raw/articles/<name>.md, continue with the normal compile checklist from Step 3.


Step 3 — Image Handling (before reading)

Check for external images first: grep -c 'https://' "raw/articles/file.md"

If count > 0 → run ./tools/fetch-images.sh "raw/articles/file.md" before reading. Without this, external images show as broken URLs — Claude cannot see them and will write wiki content with missing visual context.

Image triage when writing wiki:

  • HIGH VALUE (diagrams, flowcharts, architecture, data charts) → describe in detail + embed ![name](../../raw/images/name.png)
  • LOW VALUE (banners, avatars, stock photos) → ignore completely, do not mention

Step 4 — Core Cognitive

Summary format (wiki/summaries/<name>.md)

Required sections in this order:

  1. Executive Summary — 1–2 paragraphs: context, problem, main thesis
  2. Deep Analysis — logical breakdown with sub-headings
  3. Key Insights — 2–5 bullets of the most novel/counter-intuitive findings
  4. (optional) Limitations & Open Questions
  5. Concepts + Relations — wikilinks at the end

Write to be understood, not just to record. Explain why something matters, not just what it is.

Concept extraction (wiki/concepts/<name>.md)

Hard limit: 1–3 MACRO concepts per document. Minor terms and sub-concepts must be grouped under a parent macro — do not give them their own files.

Required frontmatter: domain: field is non-negotiable. Use the domain MOC slug (ai, product, meta...). If no MOC exists yet: use a short slug, lint will detect it.

Compiled Truth + Timeline format — every concept file ends with:

markdown
---
<!-- TIMELINE (append-only — do not edit existing entries) -->
-YYYY-MM-DD: Source — one line on how/why compiled or updated
  • Above --- = compiled truth (rewrite when evidence changes)
  • Below --- = timeline (append-only, never edit existing entries, only add new)
Long document strategy

Run ./tools/scan.sh --info <file> → check word count + section map → pick strategy:

WordsStrategyNotes
< 4KSTUFFINGRead entire file at once
4K–10KREFINESequential reads by section, running summary
10K–25KMAP-REDUCEParallel agents per section (see below)
> 25KHIERARCHICALPer-section summaries → synthesize

MAP-REDUCE — correct flow (do NOT chunk by line count):

  1. ./tools/scan.sh --info <file> — output now includes section map with exact line boundaries
  2. Chunk by section boundaries from the map, skip References/Appendix/Proofs
  3. Spawn agents in parallel, one per logical section group
  4. Each agent: read its lines → produce structured notes (NOT a full summary)
  5. REDUCE: combine all notes → write final summary + concepts

Cost trade-off: MAP-REDUCE costs ~3–4x more tokens than sequential. Use only when context overflow is a real risk (> ~15K words in dense technical text). For most papers and articles, sequential read is faster and cheaper.


Step 4.5 — Verify before finalizing

  • [ ] Summary has all required sections?
  • [ ] Concepts ≤ 3 macro, each with domain:?
  • [ ] All [[wikilinks]] point to files that exist (or will exist after this compile)?
  • [ ] No unexplained jargon stacking?

If any check fails → fix first, then proceed to Step 5.


Step 5 — finalize-compile.sh

bash
./tools/finalize-compile.sh "raw/path/to/file" "One-line key insight" --model claude-sonnet-4-6

--model is required — pass your actual model ID. Supported IDs: see tools/metrics.py.

If no domain MOC exists yet and ≥10 concepts exist → manually create MOC following wiki/domains/_about-domains.md.


Gotchas

Concept explosion — The most common mistake. After reading an interesting document, the instinct is to create a concept for every interesting term. Resist. Ask: "Is this a MACRO idea that stands on its own, or a sub-concept that belongs under an existing concept?" When in doubt, group it.

Missing `--model` flag — finalize-compile.sh runs silently without it but cost tracking is broken. The flag is not optional; it's required for metrics.

Editing TIMELINE entries — The <!-- TIMELINE --> section is append-only. Never edit existing dated entries, even to fix typos. Only add new dated lines at the bottom.

Skipping fetch-images.sh — If you read a raw .md file before downloading its images, external URLs appear as broken links. You'll write wiki content without seeing the actual diagrams. Always grep for https:// first.

Choosing strategy without `scan.sh --info` — Don't guess the word count. A "short article" might be 12,000 words. Running scan.sh --info takes 2 seconds and determines the right strategy.

Concept file > 150 lines — If you're going over, the concept is doing too much. Split into parent + sub-concept, or move examples to a summary instead.

Not verifying `[[wikilinks]]` before finalizing — Orphaned links will fail lint. Check that every [[linked-concept]] either exists already or will be created in this same compile session.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
64
Repository forks
28
7-day trend score
0

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