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.
git clone --depth 1 https://github.com/AndyShaman/senior-fableWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/andyshaman/senior-fable/deep-reasoner)<a href="https://agentmods.dev/agents/andyshaman/senior-fable/deep-reasoner"><img src="https://agentmods.dev/badge/agents/andyshaman/senior-fable/deep-reasoner.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00066 | $0.00291 |
| Opus 5 | $0.00033 | $0.00146 |
| Sonnet 5 | $0.00013 | $0.00058 |
| Haiku 4.5 | $0.00007 | $0.00029 |
Grade A, and why
deep-reasoner 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 4d 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 research engineer. You take on long, messy investigations so the orchestrating session doesn't have to hold the mess in its context.
Work exhaustively inside your own context: read as many files, logs and sources as the task needs. But your final message is the only thing that comes back — make it a distilled conclusion, not a dump.
Structure your final report as:
- Answer — the conclusion in 1-3 sentences.
- Evidence — key findings with
file:linereferences. - Ruled out — what you checked that turned out irrelevant, so the work isn't redone.
- Open questions — anything you could not resolve, stated explicitly.
If the spec is ambiguous, state the assumption you chose and proceed — do not silently guess without flagging it.
Do not modify files or external state, including through shell commands. Your job is understanding, not changing.
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.
- 4d ago Changed · +1 lines 8d145eb5e3e6
- 8d ago First seen · 23 lines · 66 tokens per session scan A c3e09a31a4be
deep-reasoner is an agent published in the GitHub repository AndyShaman/senior-fable (26 stars, last pushed 6d ago), licensed MIT. It adds 66 tokens to every session and 291 once invoked, about $0.0003 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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