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auto-claude-memory

Auto-Claude Graphiti memory system configuration and usage. Use when setting up memory persistence, configuring LLM/embedding providers, querying knowledge graph, or optimizing memory performance.

Quality

69%

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SecuritybySnyk

Low

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tessl review fix ./.claude/skills/auto-claude-memory/SKILL.md

The canonical home for this skill is auto-claude-memory in fernandezbaptiste/Skrillz

SKILL.md
Quality
Evals
Security

Quality

Content

50%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 delivers strong concrete configuration and command guidance, but it is noticeably verbose: six provider env blocks duplicate the existing (never-referenced) references/.env.example, conceptual narrative sections pad the token budget, and destructive database-reset steps lack validation checkpoints.

Suggestions

Replace the six inlined provider env blocks with a compact provider-selection table and point to references/.env.example (which already contains all of them), e.g. 'Full provider env vars: see references/.env.example (GRAPHITI section)'.

Cut the 'How Memory Works' and 'Memory in Action' narrative sections and the 'Effective Memory Use' platitudes — they restate concepts inferable from the config and tool tables.

Add validation around destructive steps: verify the backup exists before the mv-based database reset, and confirm the new OLLAMA_EMBEDDING_DIM matches the new model before running rm -rf on the embeddings directory.

DimensionReasoningScore

Conciseness

The ~370-line body inlines six full provider env blocks (~80 lines) that already exist in the bundled references/.env.example, plus conceptual padding Claude does not need: "How Memory Works", the "Memory in Action" session narrative, and platitudes like "Don't force memory storage" and "Query with natural language". This matches anchor 2 (several unnecessary explanations or padded sections); it is not 3 because the duplication with the existing reference file and the multiple narrative sections go beyond 'some unnecessary explanation', and the rubric penalizes verbosity even when accurate.

2 / 5

Actionability

Mostly executable, copy-paste-ready guidance: complete env blocks per provider, install commands ("ollama pull deepseek-r1:7b"), CLI queries ("python query_memory.py --search \"authentication\""), and troubleshooting commands (curl provider checks, backup/reset). Minor gaps keep it from 5: Python examples use unexplained placeholders ("get_graphiti_memory(spec_dir, project_dir)") with no concrete values, and the MCP tools are named in a table without usage syntax.

4 / 5

Workflow Clarity

Sections exist for enable → provider selection → database → querying → troubleshooting, and troubleshooting includes some verification commands, but there is no coherent sequenced setup walkthrough and no explicit checkpoints. The destructive database operations ("rm -rf ~/.auto-claude/memories/embeddings", the mv-based reset) lack validation loops (no backup verification, no dimension confirmation before wiping), so per the scoring notes workflow_clarity is capped at 3; it stays above 2 because verification commands do appear in the troubleshooting section.

3 / 5

Progressive Disclosure

The body has well-organized section headers and tables (some structure), but the 13KB references/.env.example — which already documents every GRAPHITI provider setting inlined in the body — is never referenced or signaled anywhere in SKILL.md, and content that clearly belongs in that separate file (the six provider config blocks) is inline. This matches anchor 3: references present but not clearly signaled, content that should be separate is inline; it is not 2 because the internal section structure is genuinely good, not minimal.

3 / 5

Total

12

/

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.

A solid description with an explicit and multi-trigger 'Use when' clause, natural keywords, and a distinctive named-system niche. The main weakness is the nominal, generic 'what' phrase ("configuration and usage"), which understates the skill's concrete capabilities.

DimensionReasoningScore

Specificity

The 'Use when' clause lists four concrete actions — "setting up memory persistence", "configuring LLM/embedding providers", "querying knowledge graph", "optimizing memory performance" — matching the anchor that lists several specific actions with minor gaps. It is not 5 because the lead 'what' phrase "memory system configuration and usage" is nominal and generic rather than concrete capability verbs, and capabilities like semantic search and MCP memory tools are not covered; it is above 3 because multiple specific actions are explicitly named.

4 / 5

Completeness

Both parts are present: the what ("Auto-Claude Graphiti memory system configuration and usage") and an explicit when ("Use when setting up memory persistence, configuring LLM/embedding providers, querying knowledge graph, or optimizing memory performance") with concrete trigger phrases. It falls short of 5 because the 'what' is a vague nominal phrase rather than clearly stating what the system does (persistent cross-session memory), while the anchor-5 exemplar pairs an equally explicit 'when' with an action-rich 'what'; it is well above 3 since the 'Use when' clause is fully explicit.

4 / 5

Trigger Term Quality

Natural, relevant phrases users would say are present: "memory persistence", "LLM/embedding providers", "knowledge graph", "memory performance", "Graphiti". Not 5 because common synonyms and variations are missing ("persistent memory", "remember across sessions", "semantic search", "recall", file paths like .env), and not 3 because the keywords go beyond a single generic term and map well onto real user requests.

4 / 5

Distinctiveness Conflict Risk

The named system ("Auto-Claude Graphiti memory system") plus distinct terms like "memory persistence" and "knowledge graph" carve out a clear niche. Minor overlap risk remains with sibling skills referenced in the body (auto-claude-setup for "setting up", auto-claude-optimization for "optimizing memory performance"), which keeps it from 5; it is clearly more distinct than anchor 3 since memory/Graphiti triggers would not fire for the sibling skills.

4 / 5

Total

16

/

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

.claude/skills/auto-claude-memory/SKILL.md

Reviewed

Table of Contents

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