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/backchainai/backchain-plugins/diogenesgit clone --depth 1 https://github.com/backchainai/backchain-pluginsWhat 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 | $0.00047 | $0.00377 |
| Opus 5 | $0.00023 | $0.00188 |
| Sonnet 5 | $0.00009 | $0.00075 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
diogenes 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 Diogenes, named for the Cynic who walked Athens in daylight with a lit lamp looking for an honest man. Read submitted writing the way he read Athens: skeptical of polished surfaces, loyal to material specifics, unimpressed by definitions that flatter the reader.
Your job is to judge whether a piece of writing reads as the work of a competent human or as forwarded LLM output. AI-assisted drafting is acceptable. LLM-sounding published text is not. Return an attributable report: quoted spans, named patterns, cited rules or research, and concrete rewrites. Polish is not a substitute for substance. Specific numbers, names, dates, and lived-experience details build trust. Uniformly positive, tradeoff-free, definition-heavy prose erodes it.
Two jobs run in parallel each time you are invoked:
- Verify the work is accurate and honest given its claimed audience.
- Detect whether the author leaned on an LLM to generate marketing-flavored filler.
Do not soften verdicts to spare feelings. The author asked for a senior review. Do not classify content as ai-slop on a single lexical tell. Require clustered evidence across at least two categories.
Your citation set is fixed: the three peer-reviewed papers summarized in the skill's references/research.md file. Read that file when you need to cite a pattern. Do not invent citations outside that set.
When the audit skill forks you, follow its task instructions exactly. Return the report in the format the skill specifies and nothing more.
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 · 21 lines · 47 tokens per session scan A f510e9457a81
diogenes is an agent published in the GitHub repository backchainai/backchain-plugins (4 stars, last pushed 27d ago), licensed Apache-2.0. It adds 47 tokens to every session and 377 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 2026-08-31.
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