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/agent-use

@3323bc9
by Tristan Manchestertristanmanchester/agent-skills3 stars
2
Run it yourself

Audit or design agent-facing product capabilities: action/context parity, discoverable contracts, safe mutations, verifiable outcomes, and recovery. Use for explicit agent-readiness reviews of apps, repos, CLIs, APIs, MCP/A2A integrations, or Agent Skills; not every general code or documentation task.

Use this Skill: https://skilld.dev/gh/tristanmanchester/agent-skills/agent-use

Nothing lands on disk. Nothing to clean up.

Fork this Skill

Edit a local copy. It keeps the author and licence.

README.md

≈427 tokens on demand. Your agent reads this file only when SKILL.md points to it.

agent-use

Audit and design products around tasks an agent can actually discover, perform, verify, and recover. Start at SKILL.md; load the domain references only when they match the product surface.

The collection retains its action/context parity methods, capability maps, heuristic scanners, templates, and evaluation seeds. Heuristic scores and old outputs in examples/ are not protocol conformance or agent-success evidence.

Current helper interfaces

Run from this installed skill directory, with Python 3.10+ and PyYAML >=6.0.3,<7 available for validation:

python scripts/validate_agent_assets.py --skill-dir .
python -m unittest discover -s tests -v
python scripts/generate_agent_assets.py --output /private/existing-parent/new-drafts \
  --project-name "Example" --base-url https://example.com --surface a2a

Validation is static and does not execute inspected scripts or produce bytecode. Scaffolding previews by default. Add --write to create only the selected drafts in a new private directory; review and separately publish them when appropriate. No old --run-help, --py-compile, --surface all, --root, --force, or --dry-run alias is supported by these updated helpers.

The current A2A card targets 1.0. Local MCP/skills-index templates remain proposals, not universal discovery standards. The example's security declaration must match real server enforcement before publication. Full protocol/client runtime tests are separate from the bundled structural/scaffold regressions.

The existing scanner test entrypoint remains python scripts/test_agent_use_scanners.py. It tests heuristic signals with synthetic inputs, not live interoperability.

Source: SKILL.md on GitHub

Skill behaviors

skilld matched fixed text patterns in SKILL.md and file names. Patterns miss obfuscated code.

skilld run checks every file with the same patterns. It asks for approval before it loads a Skill with a behavior marked Needs approval.

Third-party checks

1 warning1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill 'agent-use' is a comprehensive toolkit for auditing and designing agent-ready systems. It includes documentation, templates, and Python scripts for scanning repositories and websites. Security analysis found no malicious intent; identified network and command execution patterns are aligned with the skill's primary purpose as an auditing tool.

  • Socket1mo

    1 alert: gptAnomaly

  • Snyk1mo

    Risk: LOW · No issues

Provenance

Signed by skilld at 3323bc9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Steadyupdated last month

Capability

metadata
{
  "version": "3.0.0",
  "reviewed": "2026-09-13"
}
Other metadata
compatibility
Local helper scripts require Python 3.10+; structural validation also requires PyYAML 6.0.3 or later in major 6. Network and execution checks need separately authorised tools and environments.

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