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add-memory-kind

Add a new business memory kind end-to-end. Pick the storage combination (Markdown / SQLite / LanceDB), pick the markdown strategy (daily-log / skill-named / single-file), then wire up the schema(s), repo(s), and writer(s).

Quality

84%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

The body is a strong, highly actionable wiring recipe: executable code for every step, exact file locations, explicit validation checkpoints, and a pitfalls table for error recovery. Its only weaknesses are minor verbosity in the index-schema rationale and inlining of the optional per-layer detail that could live in separate reference files.

DimensionReasoningScore

Conciseness

The body is dense and project-specific (combination tables, strategy table, pitfalls table, test-location table) with no explanation of concepts Claude already knows; only minor over-explanation, e.g. the PRIMARY_KEY/Milvus rationale paragraph in section 5.1, could be trimmed.

4 / 5

Actionability

Every step ships copy-paste-ready executable code with exact file paths (e.g. 'infra/persistence/markdown/_mds/<name>.py', '_writers/<name>.py', '_tables/<name>.py') plus a concrete end-to-end usage example for the common daily-log case.

5 / 5

Workflow Clarity

Clear sequenced decision flow (sections 1-2) followed by sub-numbered wiring steps (3.1-3.4, 4.1-4.4, 5.1-5.4), an explicit verification checklist ('make lint', 'make test', join-key and rebuild checks), and a pitfalls table giving symptom-to-fix error recovery loops.

5 / 5

Progressive Disclosure

Well-organized sections with clearly signaled one-level-deep background references (architecture.md, init-py-and-reexport.md, etc.), but the ~120 lines of optional SQLite and LanceDB wiring detail are inlined in SKILL.md rather than split into reference files, which keeps it below the top anchor for a skill of this length.

4 / 5

Total

18

/

20

Passed

Description

75%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.

The description is specific, action-oriented, and clearly differentiated, with concrete enumerated steps covering the full workflow. Its main weakness is the absence of any 'when to use' trigger guidance and limited synonym coverage of natural trigger phrases.

Suggestions

Append an explicit trigger clause, e.g. 'Use when adding a new persisted memory entity such as an Episode, Case, Skill, AtomicFact, Foresight, or Profile, or a custom kind.'

Include natural synonyms users might say — 'memory type', 'new entity', 'new record kind' — alongside the existing technical terms.

Name one or two concrete example kinds (Episode, Case) in the description so a user asking about a specific entity finds this skill.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions with enumerations: 'Pick the storage combination (Markdown / SQLite / LanceDB)', 'pick the markdown strategy (daily-log / skill-named / single-file)', and 'wire up the schema(s), repo(s), and writer(s)' — comprehensive coverage of the skill's workflow with no gaps.

5 / 5

Completeness

The 'what' is clear and specific (add a memory kind end-to-end via storage/strategy/wiring decisions), but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Good keyword coverage with natural project terms (Markdown, SQLite, LanceDB, daily-log, schema, repo, writer), but misses common variations such as 'memory type', 'new entity', or the concrete kind names (Episode, Case, Profile) that a user would more plausibly say.

4 / 5

Distinctiveness Conflict Risk

Clear niche — adding persisted business memory kinds in this specific persistence stack — with distinct technical triggers (LanceDB, daily-log, schema/repo/writer wiring) that no closely related skill would claim.

5 / 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: 4 suspicious

Warning

Total

15

/

16

Passed

Repository

EverMind-AI/EverOS

Path

.claude/skills/add-memory-kind/SKILL.md

Reviewed

Table of Contents

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