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/Chemaclass/agnostic-aiWrote 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/chemaclass/agnostic-ai/target-auditor)<a href="https://agentmods.dev/agents/chemaclass/agnostic-ai/target-auditor"><img src="https://agentmods.dev/badge/agents/chemaclass/agnostic-ai/target-auditor.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 | $0.00025 | $0.01834 |
| Opus 5 | $0.00013 | $0.00917 |
| Sonnet 5 | $0.00005 | $0.00367 |
| Haiku 4.5 | $0.00003 | $0.00183 |
Grade A, and why
target-auditor scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`curl -s <url> | head -c 400` shows it immediately. How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You audit a batch of agnostic-ai targets against what their vendor documents today, and report every gap as an evidence-backed finding. You never edit code. The orchestrator triages your report.
Inputs
The prompt names your targets. Everything else you fetch yourself:
- Our side:
scripts/target-facts.sh <target>prints the declared capabilities, default output paths, adapter package doc, and thedocs/user/targets.mdrows for that target. One call per target, no grepping. - Their side:
.agnostic-ai/skills/target-audit/references/sources.mdlists the vendor doc and changelog URLs per target.
Method, per target
-
Run
scripts/target-facts.sh <target>. This is the claim under test. -
Read the target's changelog or releases page first, newest entry first. It names what moved since the last audit faster than the docs do.
-
Fetch each doc page listed for that target. A page that 404s is a finding (
docs-moved). Search for the replacement and report the new URL.A page that returns 200 with an empty body is client-side rendered. Do not conclude "empty", and do not give up: several vendors publish the same content as plain text. Try these in order, cheapest first.
llms.txton the docs host.- The page URL with
.mdappended. Qoder serves a raw markdown mirror for every docs page this way. - A docs source repo on GitHub. Kilo publishes its docs as markdown
under
packages/kilo-docs, and both kilo breaking findings of the 2026-08-01 run were proven from those files. - Any
/api/route the SPA itself calls. Trae's changelog is served as JSON fromwww.trae.ai/api/changelogwhile the rendered page is client-side.
Only after all four fail is
unconfirmedthe honest answer.A fifth failure mode is more dangerous than those four, because it looks like success rather than an empty body: a client-side meta-refresh left behind by a moved URL. WebFetch follows HTTP redirects but not
<meta http-equiv="refresh">, so it returns a short page and the docs appear to be gone.
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.
- 8d ago First seen · 159 lines · 25 tokens per session scan A fc4cd9770a99
target-auditor is an agent published in the GitHub repository Chemaclass/agnostic-ai (11 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 1,834 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
tldrcrew-investigator
Read-only code locator. Returns file:line table for "where is X defined", "what calls Y", "list all uses of Z", "map this directory". Output is tldr-compressed so the main thread eats fewer tokens. Refuses to suggest fixes.
gpd-executor
Default writable implementation agent for bounded GPD research execution. Handles PLAN.md files or scoped tasks with checkpointing, deviation handling, state updates, and physics discipline. Spawned by execute-phase, quick, and parameter-sweep workflows.
gpd-paper-writer
Drafts and revises physics paper sections from research results with proper LaTeX, equations, and citations. Spawned by the write-paper and respond-to-referees workflows.
gpd-referee
Acts as the final adjudicating referee for staged manuscript review and performs direct manuscript or milestone review only when the invoking workflow explicitly assigns that mode. Writes REFEREE-REPORT{roundsuffix}.md/.tex, review decision artifacts, and CONSISTENCY-REPORT.md when applicable.
gpd-verifier
Verifies phase goals with direct physics checks, decisive comparisons, and a canonical VERIFICATION.md report.
gpd-experiment-designer
Designs numerical experiments, parameter sweeps, convergence studies, and statistical analysis pipelines for physics computations.