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 commands/basher83/agent-auditor/validate-auditgit clone --depth 1 https://github.com/basher83/agent-auditorWhat 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 | $0.00015 | $0.00388 |
| Opus 5 | $0.00008 | $0.00194 |
| Sonnet 5 | $0.00003 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
validate-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 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
Skill Validate Audit
Run comprehensive skill audit using skill-auditor agent (non-blocking).
Usage
/meta-claude:skill:validate-audit <skill-path>
What This Does
Invokes the skill-auditor agent to perform comprehensive analysis against official Anthropic specifications:
- Structure validation
- Content quality assessment
- Best practice compliance
- Progressive disclosure design
- Frontmatter completeness
Note: This is non-blocking validation - provides recommendations even if prior validation failed.
Instructions
Your task is to invoke the skill-auditor agent using the Agent tool to audit the skill at $ARGUMENTS.
Call the Agent tool with the following prompt:
I need to audit the skill at $ARGUMENTS for compliance with official Claude Code specifications.
Please review:
- SKILL.md structure and organization
- Frontmatter quality and completeness
- Progressive disclosure patterns
- Content clarity and usefulness
- Adherence to best practices
Provide a detailed audit report with recommendations.
Expected Output
The agent will provide:
- Overall assessment (compliant/needs improvement)
- Specific recommendations by category
- Best practice suggestions
- Priority levels for improvements
Error Handling
Always succeeds - audit is purely informational.
Even if the skill has validation failures, the audit provides debugging feedback.
Examples
Audit a new skill:
/meta-claude:skill:validate-audit plugins/meta/meta-claude/skills/docker-master
# Output: Comprehensive audit report with recommendations
Audit after fixes:
/meta-claude:skill:validate-audit plugins/meta/meta-claude/skills/docker-master
# Output: Updated audit showing improvements
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 · 77 lines · 15 tokens per session scan A 2356de3517b6
validate-audit is a command published in the GitHub repository basher83/agent-auditor (5 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 388 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.