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bdi-mental-states

This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.

68

Quality

81%

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SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

The content is highly actionable with executable examples in several languages and a clear validated workflow, and its references are properly structured one level deep. Its main weaknesses are verbosity from repeated rationale clauses and a monolithic main body that retains material which could live in the reference files.

Suggestions

Trim the recurring 'because...' justification clauses that explain why each pattern exists; assume Claude can follow the modeling intent from the examples, or consolidate the rationale into one short 'Design principles' note.

Move the inline Guidelines, Competency Questions, and/or Examples into a reference file to make SKILL.md a leaner overview, keeping only the essential Core Concepts and the six-pass workflow inline.

Tighten the Guidelines section, which largely restates patterns already demonstrated in Core Concepts, to avoid duplicated token cost.

DimensionReasoningScore

Conciseness

The body is mostly efficient with dense executable Turtle/SPARQL/Python/Prolog examples and no basic-concept filler, but every section pads a 'because...' rationale clause and the Guidelines section restates patterns already shown, so it could be tightened; this fits the 'mostly efficient but could be tightened' anchor rather than the lean level 3.

2 / 3

Actionability

Provides fully executable, copy-paste-ready code in multiple languages (Turtle, SPARQL, Python, Prolog) with concrete property names and patterns, matching the 'fully executable code/commands; copy-paste ready' anchor.

3 / 3

Workflow Clarity

'Build a BDI Model in Six Passes' is a clearly sequenced workflow with an explicit validation checkpoint (pass 6: 'Validate with competency questions') and feedback guidance, matching the anchor for clear sequence with explicit validation steps.

3 / 3

Progressive Disclosure

References are well-signaled and one-level-deep with 'Read when:' guidance and all four referenced files verified to exist, but the SKILL.md itself is a long monolithic body (~370 lines) with Core Concepts, Examples, Gotchas, Guidelines, and Competency Questions inline that could be split out, fitting the 'some structure but content that should be separate is inline' anchor.

2 / 3

Total

10

/

12

Passed

Description

85%

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 and complete, clearly stating both what the skill does and when to use it with a distinct BDI niche. Its main weakness is trigger-term quality, where several keywords are technical jargon rather than natural user phrasing.

Suggestions

Replace jargon triggers like 'rational agency traces' and 'neuro-symbolic AI integration' with terms a user would actually say, such as 'agent reasoning', 'why an agent acted', or 'symbolic AI'.

Add common variation keywords (e.g., 'BDI agents', 'cognitive architecture', 'belief-desire-intention') alongside the formal terms to broaden natural triggering.

DimensionReasoningScore

Specificity

Lists multiple specific concrete concepts/actions: 'modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations', matching the anchor for listing multiple specific concrete actions.

3 / 3

Completeness

Explicitly answers both what ('modeling agent mental states with BDI concepts...') and when ('This skill should be used when...'), satisfying the explicit-trigger anchor for both what AND when.

3 / 3

Trigger Term Quality

Includes relevant terms ('beliefs, desires, intentions', 'BDI ontologies') but mixes in technical jargon users rarely say ('rational agency traces', 'neuro-symbolic AI integration') and omits common variations, fitting the 'some relevant keywords but missing common variations' anchor.

2 / 3

Distinctiveness Conflict Risk

BDI mental-state modeling is a clear, specialized niche with distinct triggers unlikely to fire for adjacent skills, matching the 'clear niche with distinct triggers' anchor.

3 / 3

Total

11

/

12

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
muratcankoylan/Agent-Skills-for-Context-Engineering
Reviewed

Table of Contents

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