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agentdb-memory-patterns

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

Quality

76%

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tessl review fix ./.claude/skills/agentdb-memory-patterns/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%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 reference-style skill with extensive executable CLI coverage and clear section organization, but it functions as an inline manual rather than an overview with reference files. Core memory-pattern code examples are not self-contained, workflows lack validation/feedback loops for database and batch operations, and promotional performance claims are duplicated across three sections.

Suggestions

Add validation checkpoints and feedback loops for database operations: after migration, consolidation, or batch inserts, instruct the user to verify results (e.g., run 'npx agentdb@latest stats ./agents.db' and compare expected pattern counts) before proceeding.

Move the plugin/algorithm catalog, reasoning-agent module list, and full CLI command reference into separate reference files (e.g., references/plugins.md, references/cli.md) and link them from SKILL.md so the main file is a concise overview.

Remove the duplicated performance claims (repeated in the intro, 'Performance Benchmarks', and 'Performance Characteristics') and make code examples self-contained by showing how to instantiate 'db', 'memory', and the embedding helper used across the memory-pattern snippets.

DimensionReasoningScore

Conciseness

Mostly efficient command/code blocks, but performance claims are repeated three times ('**Performance**: 150x-12,500x faster', 'Performance Benchmarks', 'Performance Characteristics') and the plugin list is duplicated between 'Create Learning Plugin' and 'Available Algorithms (9 Total)'. This exceeds the 'minor instances' of anchor 4 but doesn't reach the unnecessary-concept-explanation padding of anchor 2.

3 / 5

Actionability

CLI sections are copy-paste ready ('npx agentdb@latest init ./agents.db', 'claude mcp add agentdb npx agentdb@latest mcp', query/export/migrate commands), but several TypeScript snippets use undefined receivers and helpers ('db.storeMemory', 'memory.organize', 'computeEmbedding', 'queryEmbedding') and 'const result' is declared twice in one scope. Mostly executable with gaps concentrated in the illustrative pattern snippets — anchor 4 rather than 5.

4 / 5

Workflow Clarity

Quick starts provide sequences (init → mcp server → plugin; adapter → insertPattern → retrieveWithReasoning), but there are no validation checkpoints or feedback loops for database operations, batch inserts, destructive memory consolidation/pruning, or migration. Per the rubric's scoring note, database/batch operations without validation cap workflow_clarity at 3.

3 / 5

Progressive Disclosure

Well-headered single file with no bundle reference files; the 9-algorithm plugin catalog, 4 reasoning modules, and full CLI command reference are inlined where they belong in separate reference files. The only documentation pointer is a fragile 'node_modules/agentic-flow/docs/...' path plus external links — anchor 3, 'content that should be separate is inline'.

3 / 5

Total

13

/

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 clearly states both capabilities and explicit usage triggers in third person, anchored to a specific tool. Keyword coverage is good but misses a few natural user phrasings, and the broad agent/chat trigger contexts create minor overlap risk with related skills.

DimensionReasoningScore

Specificity

The description lists several specific capability areas — 'session memory, long-term storage, pattern learning, and context management' — matching the 'several specific actions; minor gaps' anchor. It falls short of 5 because these are feature noun-phrases rather than comprehensive concrete actions, and above 3 since more than 1-2 capabilities are named.

4 / 5

Completeness

Explicitly answers both 'what' (implement persistent memory patterns: session memory, long-term storage, pattern learning, context management) and 'when' ('Use when building stateful agents, chat systems, or intelligent assistants') with concrete trigger contexts, matching the anchor-5 example structure. Not 4 because the 'when' clause is already explicit and specific rather than merely adequate.

5 / 5

Trigger Term Quality

Good natural keyword coverage: 'memory', 'chat systems', 'agents', 'pattern learning', 'stateful agents' — comparable to the anchor-4 example 'PDF files, forms, document extraction'. Not 5 because common user phrasings like 'remember conversations', 'conversation history', or 'agent that learns' are missing.

4 / 5

Distinctiveness Conflict Risk

Anchored to a specific tool ('using AgentDB') creating a clear niche with mostly distinct triggers. Not 5 because 'stateful agents, chat systems, or intelligent assistants' could overlap with other agent-memory or chat-framework skills.

4 / 5

Total

17

/

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

ruvnet/ruflo

Path

.claude/skills/agentdb-memory-patterns/SKILL.md

Reviewed

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