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agent-memory

Creates, queries agent expertise profiles in AGENT-EXPERTISE.md; increments file-familiarity counters after each task; ranks candidate agents by recency, task-area match. Use when deciding which agent should handle a file, checking who last worked on a module, recording task outcomes, or assigning work based on past performance.

67

Quality

81%

Does it follow best practices?

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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, highly actionable protocol body with excellent token efficiency and real validation checkpoints. Its weaknesses are the dangling reference to a nonexistent KNOWLEDGE-GRAPH.md, an assumed-but-undefined section-header convention, and the absence of recovery guidance when a checkpoint fails.

Suggestions

Create the referenced KNOWLEDGE-GRAPH.md (or inline its essential entity types/triggers into SKILL.md) so the body's only external reference resolves to a real bundle file.

Define the per-agent section header convention (e.g. '## <Agent name>') that the grep query and printf append both implicitly rely on, so entries land in the correct section.

Add a brief recovery step for failed validation checkpoints — e.g. what to do when the chosen agent has a conflicting Weak entry or the familiarity path no longer exists.

DimensionReasoningScore

Conciseness

The body is a lean ~55 lines of terse tables, format specs, and commands with zero padding or explanation of concepts Claude already knows ('Entry format: `Area | Evidence | Last Updated`', the trigger table). Every token earns its place, matching the lean anchor rather than merely 'efficient with minor trimming'.

5 / 5

Actionability

Guidance is mostly executable — the grep query, printf append, and awk counter increment are real commands — but `grep -A5 "## Developer"` assumes a section-header convention never defined, and the printf append does not show how the row lands under the correct agent's section. These minor gaps keep it below fully copy-paste-ready coverage of common cases.

4 / 5

Workflow Clarity

The sequence (query, delegate with context block, update on completion, append knowledge graph) is clear and backed by an explicit Validation Checkpoints section with before/after/pruning checks. However there is no error-recovery loop telling Claude what to do when a checkpoint fails, so it falls short of the feedback-loops anchor.

4 / 5

Progressive Disclosure

Sections are well organized and the knowledge-graph detail is correctly split out via a clearly signaled one-level reference ('See [KNOWLEDGE-GRAPH.md](./KNOWLEDGE-GRAPH.md)'), but that referenced file does not exist anywhere in the skill bundle — the single external reference is dangling, so navigation actually breaks. Structure is present yet organization is compromised by the broken reference, fitting the middle anchor.

3 / 5

Total

16

/

20

Passed

Description

83%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 description that explicitly answers both what and when, with concrete trigger scenarios and a clearly identified niche around AGENT-EXPERTISE.md. Its main weakness is incomplete coverage of the skill's full capability set and a few missing natural synonyms for delegation-style triggers.

DimensionReasoningScore

Specificity

Lists several concrete, artifact-anchored actions ('Creates, queries agent expertise profiles in AGENT-EXPERTISE.md', 'increments file-familiarity counters', 'ranks candidate agents by recency, task-area match'), but omits capabilities the skill body actually covers (pruning stale entries, knowledge-graph maintenance), so coverage has minor gaps rather than being comprehensive.

4 / 5

Completeness

Both what and when are explicit: the what lists concrete operations on named artifacts, and 'Use when deciding which agent should handle a file, checking who last worked on a module, recording task outcomes, or assigning work based on past performance' provides four concrete trigger scenarios. A 4 would require the when to be less explicit or specific than it is.

5 / 5

Trigger Term Quality

The 'Use when' clause supplies natural phrases users would say ('deciding which agent should handle a file', 'checking who last worked on a module', 'assigning work based on past performance'), but misses common synonyms such as 'delegate', 'route', or 'agent history', so coverage is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

'Agent expertise profiles in AGENT-EXPERTISE.md' and 'ranks candidate agents' carve out a distinct niche with dedicated triggers, but 'assigning work based on past performance' has moderate overlap risk with generic task-assignment or memory skills, so it is mostly rather than fully distinct.

4 / 5

Total

17

/

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

relative_links

Relative link issues: 1 missing

Warning

Total

15

/

16

Passed

Repository
monkilabs/opencastle
Reviewed

Table of Contents

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