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agent-memory-systems

Design tenant-scoped memory, retrieval and retention for agents. Use when planning RAG, conversation history, vector stores or remembering across sessions.

64

Quality

80%

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SecuritybySnyk

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tessl review fix ./skills/agent-memory-systems/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%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 concise, well-structured overview that defers appropriately to a real one-level-deep reference and gives concrete, actionable design guidance with an explicit testing checkpoint. The main gaps are the absence of a failed-test feedback loop in the workflow and the detailed guide being one monolithic file rather than split by topic.

Suggestions

Split the 890-line references/detailed-guide.md into focused files (e.g., tenant contract, chunking strategies, vector store selection) and reference them individually from the Detailed Guide section so focused loading does not require scanning one monolithic file.

Add an explicit feedback loop to the Procedure step 4, e.g., 'If cross-identity rejection or retrieval benchmarks fail, fix the namespace/consolidation policy and re-test before proceeding' — validation steps currently state what to test but not what to do on failure.

Trim the boilerplate Limitations entries ('Do not treat the output as a substitute for environment-specific validation...') to the ones that add skill-specific guidance, and consider inlining a minimal record schema example in step 3 to make the output spec concrete.

DimensionReasoningScore

Conciseness

The body is lean (~40 lines) and prescriptive — 'Choose semantic records for facts/preferences, episodic records for events...' instructs rather than explains — with every section earning its place. It falls short of anchor 5 mainly because the 'Limitations' section is boilerplate ('Do not treat the output as a substitute for environment-specific validation, testing, or expert review') that could be trimmed, placing it at anchor 4 ('efficient; minor instances... that could be trimmed').

4 / 5

Actionability

The Procedure gives concrete, executable instruction: 'Specify record IDs, source evidence, timestamps, namespace, retrieval limits and conflict/version policy' and 'Test cross-identity rejection, stale/conflicting records, bounded retrieval and failed writes with isolated fixtures', plus a worked Request/Output example. This is mostly actionable with minor gaps — no sample record schema or test fixture inline — matching anchor 4 rather than anchor 5's 'specific examples cover the common cases'.

4 / 5

Workflow Clarity

The five-step Procedure is clearly sequenced and includes an explicit validation checkpoint ('Test cross-identity rejection... Benchmark retrieval against held-out questions before expanding storage'), matching anchor 4's 'clear sequence with most checkpoints present'. It does not reach anchor 5 because there is no explicit feedback loop for error recovery — what to do when the cross-identity or retrieval-quality tests fail is left implicit.

4 / 5

Progressive Disclosure

The body is a lean overview with one clearly signaled, one-level-deep reference ([the detailed guide](references/detailed-guide.md), which exists) and named sections for focused loading ('tenant-scoped contract', 'storage/chunking sections') that map to real headers in the guide. However, the guide itself is a monolithic ~890-line single file mixing frameworks, vector stores, and chunking strategies rather than content split across focused files, so 'content appropriately split' is only partially met — anchor 4, not 5.

4 / 5

Total

16

/

20

Passed

Description

78%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, third-person description that clearly states what the skill does and when to use it, with natural trigger terms covering the agent-memory domain. Its main weaknesses are a single high-level action verb rather than several distinct concrete actions, and trigger terms (RAG, vector stores) that overlap with adjacent RAG skills.

DimensionReasoningScore

Specificity

The description names the domain and work areas ('Design tenant-scoped memory, retrieval and retention for agents') but 'Design' is a single high-level action verb covering three objects rather than several distinct concrete actions like the anchor-4 example ('Extracts text from PDF files, fills forms, converts pages to images'). It clearly exceeds anchor 2, which only names a domain with minimal actions, so anchor 3 is the best fit.

3 / 5

Completeness

It explicitly answers both: 'what' ('Design tenant-scoped memory, retrieval and retention for agents') and 'when' ('Use when planning RAG, conversation history, vector stores or remembering across sessions') with concrete trigger phrases, matching the anchor-5 example pattern exactly. Anchor 4 would require the 'when' to be less explicit or specific, which is not the case here.

5 / 5

Trigger Term Quality

'RAG, conversation history, vector stores or remembering across sessions' are natural phrases a user planning agent memory would actually say, giving good keyword coverage. It falls short of anchor 5's comprehensive synonym coverage because a few natural variations of this domain are missing (e.g., 'embeddings', 'long-term memory', 'chat history'), while clearly exceeding anchor 3's 'missing common variations'.

4 / 5

Distinctiveness Conflict Risk

'Tenant-scoped memory, retrieval and retention for agents' carves a mostly distinct niche, but the triggers 'RAG' and 'vector stores' are broad terms shared with general RAG-pipeline or vector-database skills, creating minor overlap risk with closely related skills. This matches anchor 4 better than anchor 5, which requires minimal conflict risk with distinct triggers.

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

sickn33/agentic-awesome-skills

Path

skills/agent-memory-systems/SKILL.md

Reviewed

Table of Contents

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