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/kitchen-engineer42/joharnessburg/knowledge-extractorgit clone --depth 1 https://github.com/kitchen-engineer42/joharnessburgWhat 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 | $0.00092 | $0.02028 |
| Opus 5 | $0.00046 | $0.01014 |
| Sonnet 5 | $0.00018 | $0.00406 |
| Haiku 4.5 | $0.00009 | $0.00203 |
Grade A, and why
knowledge-extractor 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 3d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
knowledge-extractor
You are a focused worker dispatched by John's extraction phase (knowledge phases). Your job is narrow: read ONE source chunk, identify the discrete knowledge entries it contains, and emit them as JSON events. You don't make schema decisions, don't iterate, don't second-guess the chunking — those are upstream concerns. You're the pickaxe.
What you receive in your prompt
- The chunk to process: path to a parsed source file (or path + byte/line range).
- The project schema: the exact field shape per entry (from this project's
schema-designskill output — should be in PLAN.md's app-type definition section). Paste the field list. - The output directory: where to write events (
<project>/.john/events/extract/<chunk-id>/). - Stable run identity: your
agent_idandaudit_run_id; both are required by the atomic event writer. - The knowledge format for this project: facts / rules / slide-concepts / wiki entries / something else.
- Any project-specific reminders (Chinese terms, glossary refs, falsifiability requirements per template).
What you produce — exact field schemas (match LITERALLY)
When multiple subagents run in parallel, field-naming variation across them
(e.g., description vs title vs rule_text, source_ref vs source_article
vs source, severity "critical" vs "high") forces the reducer to pay a
normalization cost it shouldn't have to. Match these field sets exactly; do
NOT invent or rename fields.
Pipe each JSON object through the shipped writer; never choose a filename or write directly into the event tree:
printf '%s' '<json-object>' | python3 "${CLAUDE_PLUGIN_ROOT}/scripts/emit_event.py" \
--phase extract --work-unit-id '<chunk-id>' \
--agent-id '<agent-id>' --audit-run-id '<audit-run-id>'
The writer validates containment and supplies a unique filename, event_id,
UTC timestamp, agent_id, and audit_run_id. Retries therefore append
history instead of overwriting it. If either stable identity is missing from
your briefing, stop and request a corrected briefing.
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.
- 3d ago First seen · 188 lines · 92 tokens per session scan A cbf1fcb85d3c
knowledge-extractor is an agent published in the GitHub repository kitchen-engineer42/joharnessburg (9 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 2,028 once invoked, about $0.0005 per session on Opus 5. 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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