Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/datacore-one/datacore/knowledge-lintergit clone --depth 1 https://github.com/datacore-one/datacoreWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00382 |
| Opus 5 | $0.00000 | $0.00191 |
| Sonnet 5 | $0.00000 | $0.00076 |
| Haiku 4.5 | $0.00000 | $0.00038 |
Grade A, and why
knowledge-linter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Knowledge Linter
Semantic health checks for the knowledge base. Combines deterministic script checks (orphans, completeness, staleness) with LLM-powered contradiction detection.
When to Use
- Weekly review (scheduled lint pass)
- Manual
/knowledge-lintcommand - After large ingestion batches
Workflow
Phase 1: Script Checks (deterministic)
Run knowledge_lint.py on [space]/3-knowledge/:
- Orphan zettels (no inbound links)
- Incomplete literature notes (missing required sections)
- Stale seedlings (unchanged 180+ days)
Present findings with severity and suggestions.
Phase 2: Contradiction Detection (LLM-powered, optional)
Only runs if user requests --deep or during monthly review.
- Load all zettels for the space
- Group by tag/topic (use frontmatter tags)
- For each group, read all zettels and check for:
- Direct contradictions (A claims X, B claims not-X)
- Superseded claims (newer source overrides older)
- Definitional drift (same term defined differently)
- Present contradictions with source citations
- User decides: update zettel, archive one, or mark as "contested"
Phase 3: Suggestions
Based on findings, suggest:
- Sources to re-ingest for incomplete literature notes
- Zettels to cross-link for orphans
- Stale seedlings to review or archive
- Missing zettels for concepts mentioned but not yet created
Integration
- Weekly review: Runs Phase 1 automatically, Phase 2 on request
- structural-integrity: Complements (structure vs semantics)
- Datacortex: Uses backlink data for orphan detection when available
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 50 lines · 0 tokens per session scan A c766708086e0
knowledge-linter is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 382 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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