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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.

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/agentic-actions-auditor/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

70%Weight 40%Scale 1-5

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

This is a strong instruction-only skill: the audit methodology is concretely executable, correctly sequenced with error recovery and edge-case handling, and the report format is fully specified. Its weaknesses are token waste from repeatedly disclaiming absent reference files and a broken, empty Example section. Consolidating the not-bundled disclaimers into the single upfront availability note and adding one complete worked example would address the lowest-scoring dimensions.

Suggestions

Consolidate the ~15 scattered '(not bundled)' disclaimers into the single 'Supporting material availability' section at the top, and drop the Reference column from the vector table (or replace it with the inline quick check alone), recovering meaningful token budget.

Complete the Example section with one end-to-end walkthrough (a small vulnerable workflow, the captured security context, a Vector A data-flow trace, and the resulting finding) so the report format is demonstrated, not just specified.

Merge the duplicated snapshot/ref-pinning guidance from 'Snapshot and coverage limits' into Step 0's URL-parsing procedure so the same SHA-pinning rules appear once.

DimensionReasoningScore

Conciseness

The core guidance is dense and earns its tokens (expert material such as 'Rationalizations to Reject' and exact per-action input fields), but it is padded: the '(not bundled)' disclaimer is repeated roughly fifteen times, including an entire 'Reference' column of the vector table, and ref-pinning guidance is duplicated between Step 0 and the 'Snapshot and coverage limits' section. More than minor trimming is possible, so it sits at anchor 3 rather than 4.

3 / 5

Actionability

Provides copy-paste-ready 'gh api' commands, an exact known-action reference table, per-action security field lists, concrete quick checks (e.g., '${{ github.event.* }}' in env blocks, 'danger-full-access', 'allowed_non_write_users: "*"'), and exact report output templates. Minor gaps keep it below 5: the Example section is a dangling stub ('User request:' with no walkthrough) and remediation guidance defers to the absent action-profiles.md.

4 / 5

Workflow Clarity

Steps 0-5 are explicitly ordered, each building on the previous, with error handling and recovery ('GitHub authentication required. Run gh auth login'), edge-case checkpoints (no workflows directory, 0 findings, clean-repo output), a pre-conclusion validation gate ('Before concluding no AI integration exists, inspect reachable composite/reusable references'), cycle tracking, and a severity-judgment checklist. Read-only analysis needs no destructive-operation cap, and the sequence matches the anchor-5 pattern of clear steps, feedback for error recovery, and checklists.

5 / 5

Progressive Disclosure

No bundle files exist (verified: no references/, scripts/, or assets/ directories), and the body is a well-sectioned monolith that honestly declares the absent files up front. However, it still devotes a 'Detailed References' section, nine table cells, and multiple inline pointers to files that cannot be navigated to — references that are clearly signaled as absent rather than usable — so organization falls at anchor 3 rather than 4.

3 / 5

Total

15

/

20

Passed

Description

61%Weight 40%Scale 1-5

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

The description is specific, third-person, and free of fluff, naming the target integrations and the core threat model. Its main weakness is the missing 'when to use' trigger guidance, which both caps completeness and leaves it relying on a narrow set of trigger phrases. Adding an explicit 'Use when auditing GitHub Actions workflows that invoke Claude Code Action, Gemini CLI, OpenAI Codex, or GitHub AI Inference, or when checking CI/CD pipelines for prompt injection' clause would lift it substantially.

Suggestions

Add an explicit 'Use when...' clause naming the trigger scenarios (e.g., auditing workflows that invoke AI agent actions, reviewing pull_request_target exposure, or checking whether attacker-controlled input reaches an agent prompt).

Include the natural synonym 'prompt injection' (and 'workflow security' / 'CI security') so users describing the problem in those terms trigger this skill.

Mention the deliverable (a structured findings report with severity ratings) so the 'what' covers the full capability from discovery to reporting.

DimensionReasoningScore

Specificity

Names the domain ('GitHub Actions workflows for security vulnerabilities in AI agent integrations') and two concrete actions ('Audits... workflows', 'Detects attack vectors where attacker-controlled input reaches AI agents'), with four named integrations, but coverage is not comprehensive (no mention of discovery, cross-file resolution, or reporting). Anchor 3 fits best; it does not list several actions as anchor 4 requires.

3 / 5

Completeness

Clearly answers 'what' (audits workflows, detects attack vectors) but has no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Good natural keyword coverage: 'GitHub Actions', 'security vulnerabilities', 'CI/CD', 'Claude Code Action', 'Gemini CLI', 'OpenAI Codex', 'AI agent', 'attacker-controlled input'. A few natural terms users would say are missing, notably 'prompt injection', 'workflow security', and 'supply chain'.

4 / 5

Distinctiveness Conflict Risk

The AI-agent-in-CI/CD niche is well defined with named products, giving mostly distinct triggers. However, a general GitHub Actions security-audit skill would compete for the same 'audit my workflows for vulnerabilities' request, leaving minor overlap risk with a closely related skill.

4 / 5

Total

14

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

sickn33/agentic-awesome-skills

Path

skills/agentic-actions-auditor/SKILL.md

Reviewed

Table of Contents

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