Incremental compile
A self-improving knowledge base about LLM agent infrastructure
npx -y skills add chappyasel/meta-kb --skill incremental-compileAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
Copied from the file, not written here
Incrementally recompiles changed parts of the wiki after new sources are ingested. The skill handles change detection, LLM-powered impact analysis, and user confirmation. All compilation is delegated to the script pipeline (bun run compile --incremental), which handles entity extraction, resolution with anchoring, synthesis with evidence registry, reference cards, claims, self-eval, and state saving.
SKILL.md
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Incremental Compile
Recompile only what changed. The skill's job is judgment and presentation. The script's job is compilation.
If no build/incremental-state.json exists, fall back to the full
compile-wiki skill instead.
Pipeline Overview
Phase 0: Change detection (run bun run compile --status)
Phase 1: Impact analysis (you reason about changes, ask user to confirm)
Phase 2: Script compilation (run bun run compile --incremental)
Phase 3: Present results (read compilation-diff.json, eval-report.json)
Phase 0: Change Detection
Run the script's status command:
bun run compile --status
Read the output to understand:
- How many sources were added/modified/deleted
- Which buckets are dirty (relevance-gated: low-relevance sources don't dirty buckets)
- Whether the config changed (requires full recompilation)
Also read each new/modified source's frontmatter (key_insight, tags,
relevance_scores.composite) for impact reasoning in Phase 1.
If the status says "Wiki is current", report that and stop.
Phase 1: Impact Analysis
This is the skill's primary value-add over the script.
-
Read each new/modified source (frontmatter + first 2K chars of body).
-
Assess impact per dirty bucket: Read the existing
wiki/{bucket}.mdsynthesis article abstract. Consider:- Does the new source add a genuinely new perspective?
- Does it contradict existing claims? (Check key_insight against article)
- Is it high enough relevance to matter?
-
Report findings to the user before proceeding:
Impact analysis: knowledge-substrate: DIRTY — new project (Dash) with 6-layer context system self-improving: DIRTY — SEAL paper introduces in-weight self-adaptation agent-memory: CLEAN (new sources below top-25 cutoff) ... Contradictions detected: (none / list specifics) Estimated dirty entities: ~15 Proceed with recompilation? (Y to continue, N to abort) -
Handle edge cases:
- If >50% of sources changed, suggest full recompilation instead
- If sources were deleted, note which entities might lose full-article status
- If config changed, explain that full recompilation is required
Phase 2: Script Compilation
After user confirmation, run a single script call:
bun run compile --incremental
This handles everything:
- Pass 0: Load and index all raw sources
- Pass 1a: Incremental entity extraction (only changed sources)
- Pass 1b: Entity resolution with ID anchoring (prevents slug churn)
- Pass 2: Graph construction
- Pass 3a: Synthesis articles for dirty buckets (with evidence registry + source boosting)
- Pass 3a.5: Blind review of synthesis articles
- Pass 3b: Reference cards for dirty entities only
- Pass 3c: Claims extraction (dirty buckets only, merges with clean)
- Pass 4: Field map + indexes
- Pass 5: Mermaid diagrams + backlinks
- Pass 6: Changelog (appends, includes compilation diff)
- Pass 7: Self-eval with incremental eval cache
- Pass 8: Auto-fix failed claims
- State save: Updates
build/incremental-state.json
Dirty detection is relevance-gated: sources must beat the per-bucket top-25 cutoff (with +1.5 boost for changed sources) to dirty a bucket. Entity dirtiness is based on changed source_refs, not cosmetic re-resolution.
Phase 3: Present Results
After compilation completes, read and present:
-
build/compilation-diff.json— Which synthesis articles changed, word count deltas, sections added/removed, new citations. -
build/eval-report.json— Self-eval accuracy. How many claims were verified vs carried forward from cache. Any failures. -
build/lessons.md— Unfixable patterns flagged for human review.
Present a concise summary:
Compilation complete:
- 2 synthesis articles updated (knowledge-substrate, self-improving)
- 12 reference cards regenerated, 3 new cards added
- Self-eval: 28/30 passed (93.3%), 2 carried forward from cache
- 1 unfixable claim flagged in build/lessons.md
Suggest follow-ups if relevant:
- "Consider deep-researching {project} for stronger source coverage"
- "The self-improving article has {N} unsupported claims — review build/eval-report.json"
Fallback: When to Use Full Compilation
Use compile-wiki instead of this skill when:
build/incremental-state.jsondoesn't exist (first compilation)- Domain config (
config/domain.ts) changed materially - More than 50% of sources changed
- The user explicitly requests a full recompilation
- You detect systemic issues (many stale claims, low eval accuracy)
Quality Rules
The script handles all quality enforcement:
- Evidence registry prevents cross-article duplication
- Source boosting (+1.5) ensures new sources enter synthesis articles
- Blind review (Pass 3a.5) catches unsupported claims
- Self-eval (Pass 7) verifies claims against sources
- Auto-fix (Pass 8) repairs citation errors
If build/lessons.md exists, the script reads it automatically.