ctf-ai-ml

Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.

Quality

82%

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Critical

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Quality

Content

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

A lean, well-organized reference catalog with executable triage commands and clear file-pointing structure, but it is undermined by missing bundle files and lacks explicit validation checkpoints in its workflows. The progressive-disclosure design is sound in form but broken in substance because the referenced detail files are absent.

Suggestions

Add the three referenced supporting files (model-attacks.md, adversarial-ml.md, llm-attacks.md) to the bundle so the well-signaled one-level-deep references resolve instead of dangling.

Insert explicit validation checkpoints in the Quick Start triage (e.g., confirm the model loaded and printed keys before running the weight-comparison step, and verify the remote endpoint responded before interpreting prompt-injection output).

Include at least one complete, copy-paste-ready end-to-end attack snippet inline (e.g., a full FGSM/PGD loop) rather than only the formula plus a link, to lift actionability on the most common attack cases.

Dimension	Reasoning	Score
Conciseness	The body is lean — it gives formulas directly ('x_adv = x + eps * sign(grad_x(loss))', 'W_base + alpha * (B @ A)'), copy-paste commands, and one-line technique summaries with file links, without explaining concepts Claude already knows; it fits the 'lean and efficient; every token earns its place' anchor rather than the 4 anchor's 'minor over-explanation'.	5 / 5
Actionability	The Quick Start section is fully executable (torch/safetensors/transformers inspection, weight comparison, curl prompt-injection probe), but the headline attack techniques are presented only as formulas/descriptions with links to detail files rather than complete inline implementations, fitting 'mostly executable guidance; minor gaps' above the pseudocode 3 anchor.	4 / 5
Workflow Clarity	The Quick Start offers an ordered triage sequence and 'When to Pivot' provides routing, but there are no explicit validation/verification checkpoints or feedback loops for the attack processes, matching the 3 anchor ('sequence present but checkpoints missing or implicit') rather than the 4 anchor which requires most checkpoints present.	3 / 5
Progressive Disclosure	The structure is well-signaled with an 'Additional Resources' index and one-level-deep links to model-attacks.md, adversarial-ml.md, and llm-attacks.md, but those supporting files are not present in the bundle (no references/ dir, no other .md files exist), so the references dangle and the overview→detail promise is unfulfilled — below the 4 anchor whose 'minor gaps' assume the referenced files actually exist.	3 / 5
	Total	15 / 20 Passed

Description

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

A strong, specific description that clearly states what the skill does and when to use it, with comprehensive concrete technique triggers and a well-scoped niche. Its only weakness is trigger-term phrasing that favors technical jargon over the casual synonyms a less expert user might say.

Dimension	Reasoning	Score
Specificity	Lists many concrete actions ('attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking') — comprehensive coverage of the AI/ML CTF domain, matching the 'multiple specific concrete actions' anchor rather than the 4 anchor which expects minor gaps.	5 / 5
Completeness	Explicitly answers both 'what' ('Provides AI and machine learning techniques for CTF challenges') and 'when' ('Use when attacking ML models...') with concrete trigger phrases, matching the 5 anchor exactly rather than the 4 anchor where 'when' is only loosely specified.	5 / 5
Trigger Term Quality	Strong domain-specific trigger coverage (prompt injection, adversarial examples, model extraction, LLM jailbreaking, membership inference) that the target CTF audience would naturally say, but it leans technical and omits casual synonyms a user might phrase (e.g. 'AI challenge', 'ML hacking'), so it sits above the 3 anchor but short of the comprehensive-synonym 5 anchor.	4 / 5
Distinctiveness Conflict Risk	Scoped to a clear niche (AI/ML techniques for CTF challenges) with distinct triggers and minimal overlap with sibling skills; the body's 'When to Pivot' section further separates it from /ctf-crypto, /ctf-reverse, and /ctf-misc, fitting the 'clear niche with distinct triggers' anchor.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

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

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
relative_links	Relative link issues: 19 missing	Warning

	Total	14 / 16 Passed

Repository: ljagiello/ctf-skills
Path: ctf-ai-ml/SKILL.md
Commit: 36c72e5

Reviewed: 5 days ago

Table of Contents

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