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tune-enemy-ai

Build, debug, balance, or test combat enemy AI for playable action games. Use for aggro, target selection, navigation, spacing, attack choices, telegraphs, retreats, boss behavior, behavior-state machines, and deterministic AI regression tests.

75

Quality

94%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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 lean, well-structured instruction-only skill: every line is domain-specific and actionable, and it closes with concrete verification guidance. The only gaps are the absence of worked examples (a sample state machine or fixture) and any error-recovery loop for when tests fail.

Suggestions

Actionability: add one compact worked example — e.g., a sample state-transition specification (state, prerequisites, exit conditions, dwell time) or a small deterministic fixture snippet — so the abstract guidance has a concrete anchor.

Workflow_clarity: add a brief failure-handling step, such as what to inspect and re-run when a regression fixture fails after a balance change (narrow the failing transition, reproduce deterministically, re-run the fixture and the browser encounter).

Workflow_clarity: make the fixture list explicitly checkable (e.g., present the eleven scenarios as a bullet checklist) so coverage of the decision surface can be verified against it directly.

DimensionReasoningScore

Conciseness

At ~29 lines every sentence carries project-specific guidance ("minimum dwell time", "Do not derive them from rendered pose", "Assert transitions and outcomes, not only final positions") with zero padding or explanation of concepts Claude already knows; it fully assumes the model's competence.

5 / 5

Actionability

The guidance is concrete and specific — named states, enumerated fixture scenarios ("target acquisition, target loss, obstruction, path failure..."), and named anti-patterns ("instant turn-and-hit", "recovery spam") — but as an instruction-only skill it stops short of fully executable material: no example state-transition spec or sample test skeleton to anchor the common cases.

4 / 5

Workflow Clarity

Sections flow coherently from modeling to testing, the perception/intent/motion section is an explicit numbered sequence, and validation is explicit ("Assert transitions and outcomes", "Run a real browser encounter after automated tests"), but there is no error-recovery feedback loop (what to do when a fixture fails or a balance change regresses behavior).

4 / 5

Progressive Disclosure

The skill is under 50 lines with no external references needed, and it is organized into four clear, well-scoped sections with a one-line thesis ("Make enemy choices legible, bounded, and reproducible") — matching the simple-skill exception where well-organized sections alone earn the top score.

5 / 5

Total

18

/

20

Passed

Description

100%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.

An exemplary description: concrete actions, an explicit trigger clause, natural domain vocabulary, and a sharply defined niche. It reads like the reference good examples — what and when are both answered in two tight sentences with no padding.

DimensionReasoningScore

Specificity

"Build, debug, balance, or test combat enemy AI" names four concrete actions on a specific domain, matching the anchor for multiple specific concrete actions with comprehensive coverage; the enumerated sub-areas (target selection, telegraphs, retreats, boss behavior) close the gaps that would justify a 4.

5 / 5

Completeness

It explicitly answers both what ("Build, debug, balance, or test combat enemy AI for playable action games") and when ("Use for aggro, target selection, ...") with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

"aggro, target selection, navigation, spacing, attack choices, telegraphs, retreats, boss behavior, behavior-state machines, and deterministic AI regression tests" covers the natural phrases a game developer would actually say, with synonym-level breadth; no meaningful natural terms are missing.

5 / 5

Distinctiveness Conflict Risk

"combat enemy AI for playable action games" is a clear niche with distinct triggers (aggro, telegraphs, boss behavior, state machines), making conflict with adjacent skills (general testing, rendering, pathfinding-only skills) minimal.

5 / 5

Total

20

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
MengTo/Skills
Reviewed

Table of Contents

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