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 skills add Chemaclass/agnostic-ai --skill target-auditgit 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/skills/chemaclass/agnostic-ai/target-audit)<a href="https://agentmods.dev/skills/chemaclass/agnostic-ai/target-audit"><img src="https://agentmods.dev/badge/skills/chemaclass/agnostic-ai/target-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chemaclass/agnostic-ai/target-audit"><img src="https://agentmods.dev/badge/skills/chemaclass/agnostic-ai/target-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.01241 |
| Opus 5.5 | $0.00017 | $0.00496 |
| Sonnet 5.5 | $0.00009 | $0.00248 |
| Haiku 4.5 | $0.00004 | $0.00124 |
Grade A, and why
target-audit 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
target-audit
Check whether tools still read what agnostic-ai emits and identify released project capabilities that need a product decision. Complete the requested scope with fresh vendor evidence.
Scope and boundaries
- No arguments: all registered targets, one issue per confirmed finding.
- Target names: only those targets.
--no-file-issues: no GitHub writes or fixers, including when combined with--fix. Leave validated audit-maintenance edits in the working tree.--fix: file issues and open fix PRs. Never merge or enable auto-merge.--compare-models <model>: bounded challenge of breaking findings, spec candidates, and conflicting evidence. Continue if unavailable.
Default delivery includes confirmed issues and a PR for changed audit-maintenance files. Implementation PRs require --fix. Never create or edit site articles or their publication PRs; legacy articles are immutable dedupe history. Generic schema changes need a separate implementation decision.
All paths are relative to the repository root, including from nested generated skills. Edit canonical sources, never generated native copies.
1. Prepare and route
Follow .agnostic-ai/skills/target-audit/references/preparation.md for the window, baseline, manifest, metrics, fresh fetch, explicit resume, issue dedupe, and routing. Command syntax belongs to scripts/target-audit/manifest.md.
Freeze repository inputs, source inventory, and lock until finalization. Preserve validated reused assignments; remove them from pending work. Every requested target needs exactly one assignment and every requested source its own disposition. Reuse never implies Clean.
When no pending target needs deep review, record justified fast paths and skip triage and auditors. Reused findings, signals, or uncertain publication still require synthesis and delivery. Only a pure no-change run can skip issue indexing and signals before reporting and finalizing.
Otherwise follow .agnostic-ai/skills/target-audit/references/triage.md before dispatch. Deep review remains the default. All-built-in deep work uses offline triage; Jev cannot exempt it.
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/behavior-evidence.md 5.9 KB
- references/builtins.md 5.1 KB
- references/capability-intelligence.md 8.7 KB
- references/evidence-and-output.md 4.0 KB
- references/fetch-playbook.md 7.4 KB
- references/issues-and-fixes.md 7.2 KB
- references/metrics.md 7.1 KB
- references/preparation.md 4.7 KB
- references/report-and-finalize.md 4.5 KB
- references/review-reasons.md 3.7 KB
- references/scheduled-runs.md 1.9 KB
- references/sources.md 75 KB
- references/triage.md 3.3 KB
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.
- 3d ago Changed · -10 lines 25d7257e28ef
- 4d ago Changed · -69 lines d9271f332ec3
- 11d ago Changed · +16 lines 7ef817942e4b
- 13d ago Changed 96b9f40dc3b4
- 18d ago Changed · +1 tokens per session 1a301c537a0d
- 21d ago Changed · +3 lines 988a858eb932
- 27d ago Changed · +2 lines d43ea3f2a637
- 29d ago Changed · +5 lines e7f1a2576968
- 1mo ago Changed ff737041c218
- 1mo ago First seen · 120 lines · 42 tokens per session scan A 1d8721564d6b
target-audit is a skill published in the GitHub repository Chemaclass/agnostic-ai (25 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,241 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
eg-fix-bug
Fix a bug with Codex using problem doc, independent diagnosis, failing test, fix, review, and validation.
code_explorer
Explores the repository to locate primary source files, coupled UI components, and test files for bug reports or feature requests.
cvm-ai-doctor
A health-diagnosis workflow for servers, computers, virtual machines, and containers on Linux, macOS, or Windows.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
moai-lane-watchdog
One lane stall watchdog iteration: measure lane progress on three channels, classify the stall cause, apply the remedy or resolve the judgment through the decision ladder — instead of waiting on a reply — then record the outcome and report. Reads the unified --auto doctrine at…
debug
Debug issues in the MCP Gateway Registry using first-principles thinking. Invoke when something is broken, timing out, returning errors, or behaving unexpectedly. Forces structured root-cause analysis before any code change is proposed.