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agentic-actions-auditor

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.

68

Quality

83%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-crafted, highly actionable security audit skill with clear methodology, concrete commands, and excellent progressive disclosure structure. The workflow is logically sequenced with appropriate validation checkpoints and error handling. The main weakness is moderate verbosity in some sections (rationalizations, when-to-use lists) that could be tightened without losing clarity, though the complexity of the domain somewhat justifies the length.

DimensionReasoningScore

Conciseness

The skill is thorough but includes some unnecessary verbosity. The 'When to Use' / 'When NOT to Use' sections are somewhat redundant with the methodology itself. The 'Rationalizations to Reject' section, while useful, explains concepts at length that could be more concise. The methodology steps are well-structured but could be tightened in places (e.g., the URL parsing table and bash safety rules are detailed but appropriate for the complexity). Overall mostly efficient but not maximally lean.

2 / 3

Actionability

The skill provides highly concrete, executable guidance throughout: specific `gh api` commands with exact syntax, a precise table of action references with matching rules, detailed field names to capture per action type, a structured vector detection table with specific patterns to check, and a complete report format with section ordering. The audit methodology is step-by-step with clear instructions at each stage.

3 / 3

Workflow Clarity

The 5-step methodology is clearly sequenced with each step building on the previous one. Validation checkpoints are present: Step 1 has an early exit if no workflows found, Step 2 has an early exit if no AI actions found, Step 0 includes explicit error handling for auth and 404 errors. The cross-file resolution has a depth limit. The report structure in Step 5 includes severity judgment criteria and interaction cross-references. The bash safety rules provide explicit guardrails against dangerous operations.

3 / 3

Progressive Disclosure

The skill uses excellent progressive disclosure with a clear overview methodology in the main file and well-signaled one-level-deep references to detailed materials: `references/foundations.md` for the attacker model, individual `vector-{a..i}-*.md` files for detection heuristics, `action-profiles.md` for per-action security details, and `cross-file-resolution.md` for resolution procedures. The main file contains enough context to understand each step while deferring implementation details to reference files. However, no bundle files were provided to verify these references exist.

3 / 3

Total

11

/

12

Passed

Description

82%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong, specific description that clearly identifies its domain (GitHub Actions security for AI agent integrations) and names concrete tools and actions. Its main weakness is the lack of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The description uses proper third-person voice and avoids vague language.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to review or audit GitHub Actions workflows for security issues, especially those involving AI agents or code generation tools in CI/CD pipelines.'

Consider adding common user phrasings like 'workflow security review', 'CI security audit', or 'prompt injection in CI/CD' to improve trigger matching.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'audits GitHub Actions workflows', 'security vulnerabilities in AI agent integrations', names specific tools (Claude Code Action, Gemini CLI, OpenAI Codex, GitHub AI Inference), and 'detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines'.

3 / 3

Completeness

The 'what' is clearly answered (audits workflows for security vulnerabilities, detects attack vectors), but there is no explicit 'Use when...' clause or equivalent trigger guidance telling Claude when to select this skill. Per rubric guidelines, this caps completeness at 2.

2 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'GitHub Actions', 'security', 'vulnerabilities', 'AI agent', 'Claude Code Action', 'Gemini CLI', 'OpenAI Codex', 'CI/CD pipelines', 'attack vectors'. These cover the domain well and match how users would describe this need.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche: specifically targets GitHub Actions workflow security for AI agent integrations in CI/CD. The combination of security auditing + AI agents + GitHub Actions + named tools makes it very unlikely to conflict with other skills.

3 / 3

Total

11

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
sickn33/antigravity-awesome-skills
Reviewed

Table of Contents

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