audit-agent-tooling
Audit how agents actually use tools and skills from session transcripts, separate crash rate from correctness, identify repeated operations that should become…
Skill by emliunix
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/emliunix-audit-agent-tooling
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: security, agent-building
Reviews
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Node.js package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/audit-agent-tooling
What's inside
- Skill name
audit-agent-tooling- Description
- Audit how agents actually use tools and skills from session transcripts, separate crash rate from correctness, identify repeated operations that should become named routines or typed surfaces, and publish reproducible lessons. Use when mining agent sessions for tooling patterns, evaluating a guard or extension's reach, or deciding which lesson belongs in a durable skill or repository check. This is the measurement procedure, not the ordinary tool-use rulebook.
- License
- GPL-3.0
- Instructions
- 139 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (3)
- SKILL.md
- preflight-corpus.mjs · scripts/
- check-preflight.mjs · tests/
SKILL.md (first lines)
# Audit agent tooling Treat session transcripts as measurements, not anecdotes. The output is a small set of falsifiable rules, each with its population, command, result, and bound. This is not an agent ranking or a productivity score. Follow the `efficient-tool-use` skill for rules already earned. Its topic references are the durable rulebook; do not rediscover or silently overwrite an entry. ## 1. Freeze the corpus Record every transcript path, harness, session ID, file hash when stable, and read time. Live sessions change between reads, so a count without its read time is not reproducible. State the corpus and time window in every table. State the active window explicitly in every census, including whether `--since` was supplied or defaulted. Before reading the corpus, preflight it for the subject under audit: - a selected file or store with zero recognized subject records in the active window is refused, with the observed record-type census and the window; - a mixed store reports which record types were recognized and which were ignored; - an intentionally empty corpus requires an explicit opt-in, so a legitimate zero is distinguishable from an instrument that never saw the subject. A zero from a store whose subject records were never reached is a failed measurement, not evidence of absence. Use the focused boundary before reading a corpus: ```sh node skills/audit-agent-tooling/scripts/preflight-corpus.mjs \ --since <ISO> [--until <ISO>] <file-or-directory>... ``` The command emits the active window, recognized and ignored record-type censuses, and the per-file counts. It exits `1` with `REFUSED` when the selected corpus has zero conversation records in the window. `--allow-empty` is the explicit exception; it prints the opt-in in its receipt. Its focused cases and seeded removal-red are in `skills/audit-agent-tooling/tests/check-preflight.mjs`. Keep transcript source files read-only. Do not paste large transcript payloads into the report; reference the source and show only the relevant records or normalized rows. ## 2. Parse records, not text Extract assistant tool-call records and pair each with its tool result by ID. Count actual calls, not strings that mention a call. If the transcript format does not expose IDs or a complete record type, say so and reduce the claim. Use a small, rerunnable parser when the corpus has more than one session. Keep the parser next to the report or in the owning repository so another reader can reproduce the census. Searching before reading is a corpus-level lesson: search for the target statement first, then read only the matching region. ## 3. Classify by operation, then control for conflation At minimum, separate: - direct structured tool calls from shell calls; - reads used to answer a targeted question; - search-before-read; - inline interpreters and ad-hoc parsers; - poll loops; - output shaping such as `head`, `tail`, `cut`, and `fold`; - repeated invocation shapes after normalizing arguments; - skill loads and other explicit procedure activation. Do not attribute an outcome to syntax when one command or subcommand explains it. Split the data by command identity before naming a shape rule. Replicate a candidate rule on another corpus when one is available. Bucket non-zero or error results by their structured code. Semantic families such as "safe hold" and "transport failure" have different recovery paths and must not be collapsed under one word. ## 4. Keep the instrument inside its claim `is_error` measures crashes and refusals. It does not measure wrong answers. A low crash rate cannot establish that a pattern is safe, and a successful command cannot establish that its result is correct. If correctness is part of the question, use an instrument that checks the result against a known answer. State the residual risk when the corpus has no correctness signal. For a guard, extension, or wrapper, measure three separate things: 1. **Reach:** which harnesses, seats, and sessions can load it. 2. **Activation:** a real round trip, not only installation or source. 3. **Scope:** what it can change and what remains outside its boundary. A guard that cannot reach the failure path does not protect it, even when the guard itself works. ## 5. Convert the finding into the right artifact - Repeated invocation shape: save the operation as a named routine, wrapper, or command. The gap is often "the tool exists; the invocation shape does not." - Numeric or set-valued answer parsed from text: expose a typed or structured result and make required arguments explicit. This removes the parsing step where silent errors occur. - A high crash rate concentrated in one command: fix or document that command's contract instead of labeling the syntax unsafe. - A low crash rate: do not promote it to a safety rule without a correctness check. - A verified correction: replace the superseded rule in the matching `efficient-tool-use` topic and mark the correction in the report. Do not leave both versions active. Add a rule to the cookbook only when it changes a future decision and its evidence is reproducible. Keep single instances labeled as candidates until a
From emliunix/home.conf, GPL-3.0.
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Signals
- Stars
- 0
- Forks
- 1
- Open issues
- 3
- Last commit (this folder)
- Oct 7, 2026 (22 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- GPL-3.0
- Repository
- github.com/emliunix/home.conf
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
audit-agent-tooling skill. Agent Mart. https://agentmart.dev/skills/emliunix-audit-agent-tooling (last updated 2026-10-24)