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game-security-research-rigor

Verify game-security claims through primary-source checks, explicit trust boundaries, claim ledgers, reproducible evidence, and calibrated uncertainty. Use for attack/defense comparisons, community reports, enforcement-scope claims, telemetry quality, detector evaluation, owned-game-build diagnostics and sanitizer limits, untrusted instructions in retrieved sources, conflicting citations, or disagreement across README/wiki/description/archive layers. Separate observation, finding, attribution, and action; assess confounders, base rates, false positives, temporal validity, and source limitations before drawing consequential conclusions.

71

Quality

86%

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SecuritybySnyk

The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

81%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.

A dense, well-structured research-rigor methodology that assumes Claude's competence, gives concrete actionable guidance, and provides a clear sequenced workflow with validation checkpoints and feedback loops. The main room for improvement is light tightening of a few long bullet lists and optional splitting of reference-style material into a separate file.

Suggestions

Tighten the long bullet runs in 'Detector and telemetry evaluation' by grouping related metrics (e.g. error-rate metrics vs. repeated-testing effects) to improve scanability without losing content.

Consider moving the 'Source roles' table and the detailed detector-metric list into a references/ file referenced one level deep, leaving SKILL.md as a tighter overview.

Add a short 'Quick reference' summary of the four reasoning layers and the four conclusion labels (supported/suspicious/no signal/inconclusive) near the top so the core invariants are glanceable before the detailed workflow.

DimensionReasoningScore

Conciseness

Lean and purposeful throughout — it does not re-explain concepts Claude already knows and each section earns its place; a few dense bullet runs (e.g. detector-evaluation metrics) could be tightened slightly, keeping it just below a 5.

4 / 5

Actionability

For an instruction-only methodology skill the guidance is concrete and specific — exact metrics to report (FPR, FNR, precision, recall, calibration), explicit verification actions ('Confirm the URL or DOI resolves', 'Match title, authors, venue, and year'), and a claim-ledger field list — with only minor gaps versus fully executable code-style guidance.

4 / 5

Workflow Clarity

A clearly sequenced 7-step research workflow with explicit validation steps ('Verify every citation', 'Reproduce and validate'), a feedback loop ('If a gate fails, narrow the claim or return inconclusive'), and checklists (Quality gates, What tests establish), matching the top anchor.

5 / 5

Progressive Disclosure

Well-organized into clearly headed sections (Purpose, reasoning layers, Research workflow, Detector evaluation, Invariant checks, Governance, Quality gates, Source roles) with no nested references and no bundle files needed; one or two sections (e.g. the source-roles table) could arguably live in a reference file, so it sits just under 5.

4 / 5

Total

17

/

20

Passed

Description

92%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.

A strong, well-structured description that clearly states both capability and triggering conditions with concrete, domain-specific actions. It is highly specific and distinct; the only minor weakness is that some trigger terms lean technical rather than colloquial.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'citation verification, reproducible analysis, detector evaluation, validating technical claims, synthesizing README or wiki resources, comparing security techniques, assessing telemetry or models' — giving comprehensive coverage of what the skill does.

5 / 5

Completeness

Explicitly answers both what ('Guide for evidence-grounded game-security research, citation verification... detector evaluation') and when ('Use when validating technical claims... or deciding whether evidence supports an anti-cheat conclusion') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good coverage of natural trigger phrases ('validating technical claims', 'synthesizing README or wiki resources', 'assessing telemetry or models', 'anti-cheat conclusion'), though several are specialized domain jargon rather than the everyday synonyms a broad user base would say.

4 / 5

Distinctiveness Conflict Risk

Targets a clear niche — research-rigor guardrails for game-security / anti-cheat work — with distinct triggers and minimal overlap risk with general skills; voice is third person ('Guide for...').

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
gmh5225/awesome-game-security
Reviewed

Table of Contents

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