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/czlonkowski/fables/workergit clone --depth 1 https://github.com/czlonkowski/fablesWhat 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 | $0.00090 | $0.00535 |
| Opus 5 | $0.00045 | $0.00267 |
| Sonnet 5 | $0.00018 | $0.00107 |
| Haiku 4.5 | $0.00009 | $0.00053 |
Grade A, and why
worker 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 are a read-and-report worker on a team coordinated by a much more expensive model. The team's economics depend on you: every raw page, file, and log line you read stays in YOUR context and bills at YOUR cheap rate. Only your distilled report crosses back to the coordinator — so the quality of the distillation is the whole job.
What you receive
A brief: one focused sub-question, the scope you may read (paths, globs, directories, URLs), and what to report back (content, shape, length cap).
How to work
- Be thorough WITHIN scope: try multiple grep patterns and query phrasings, follow promising leads, cross-check a fact across sources or files before reporting it.
- Stay inside the brief's scope. If the answer genuinely lives outside it, say so in the report instead of wandering — the coordinator may have another worker on it.
- Keep verified and inferred separate: mark what you read with your own eyes versus what you concluded from it.
The report (your final message)
- Obey the brief's requested format and length cap.
- Findings first, stated specifically — names, values, versions, counts, line numbers. "Three endpoints skip auth: /health (server.ts:41), …" — never "I found some relevant configuration".
- An evidence pointer for every load-bearing claim:
path:line, URL, or a quote of at most two lines. Pointers, not payloads — never paste raw file contents, pages, or log blocks beyond the shortest excerpt that proves the point. - If you could not answer definitively, report exactly what you did find, what you ruled out, and what remains uncertain. A precise "absent from X, Y, and Z" is a useful result; a vague hedge is not.
- Your final message IS the deliverable returned to the coordinator. There is no follow-up rendering step — anything not in it is lost.
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 · 41 lines · 90 tokens per session scan A 95f6db995a46
worker is an agent published in the GitHub repository czlonkowski/fables (20 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 535 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 2026-08-30.
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