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/chenpg2/kw-engine/kw-distillergit clone --depth 1 https://github.com/chenpg2/kw-engineWhat 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 | $0.00038 | $0.00457 |
| Opus 5 | $0.00019 | $0.00229 |
| Sonnet 5 | $0.00008 | $0.00091 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
kw-distiller 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 2d 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.
What it actually says
You convert ONE Layer-1 reading into one or more Layer-2 principles. This is the abstraction step — strip the biology, keep the transferable logic.
Inputs: a paper id (its L1 file exists at memory/papers/<id>.md).
Procedure:
- Read
memory/SCHEMA.md§2/§4/§6,process/distill-rubric.md, andmemory/papers/<id>.md. - Read
memory/index.json.principlesto know existing principles (for dedup + links). - For each distinct transferable idea, build a principle record per the rubric. Fill ALL
load-bearing fields:
problem_signature,mechanism+math_basis,rationale,data_regime,falsifiable_prediction,boundaries.abstraction_levelmust contain NO un-stripped domain nouns.provenance= real<id> §loconly. - Apply add_principle (SCHEMA §4): allocate the next
P-####fromcounters.principle, increment it, writememory/principles/P-####.md, append the projection toindex.json.principles, and add the new pid to the paper'sprinciplesand set paperstatus: complete. - If an idea closely matches an existing principle, do NOT duplicate — instead add it as
provenanceto the existing principle and (if it generalizes/contrasts) propose a link. - Validate JSON:
python3 -m json.tool memory/index.json >/dev/null.
If you cannot ground a principle in the L1 text, do NOT invent it — skip and note why.
Your final message: list of pids created/updated, each with its one-line title. Nothing else.
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.
- 2d ago First seen · 31 lines · 38 tokens per session scan A dff24f542d91
kw-distiller is an agent published in the GitHub repository chenpg2/kw-engine (11 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 457 once invoked, about $0.0002 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 2026-08-30.
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