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conversation-memory

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

44

Quality

46%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/conversation-memory/SKILL.md

The canonical home for this skill is conversation-memory in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

22%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 thin, incomplete scaffold: persona padding, truncated and empty sections, placeholder solutions, and no executable guidance or validation. It needs concrete implementation detail and a real workflow before it can be actioned.

Suggestions

Replace the persona narrative with concrete implementation guidance — code or config for short-term/long-term/entity stores and the retrieval-consolidation flow.

Complete the truncated 'core principles' list and fill the empty Anti-Pattern sections with what to avoid and why, or remove them.

Add a validation/verification checkpoint for the cross-user isolation and bounded-growth risks flagged in the Sharp Edges table (e.g. an isolation test and a lifecycle/eviction check), and rewrite the placeholder '// ...' solutions as actual steps.

Replace the generic 'When to Use' sentence with concrete trigger conditions tied to the description.

DimensionReasoningScore

Conciseness

The opening persona narrative ('You're a memory systems specialist who has built AI assistants that remember users across months...') and the conceptual aside ('memory is not just storage—it's about retrieval, relevance, and context') are padding Claude does not need, matching the 'noticeably verbose; several unnecessary explanations or padded sections' anchor.

2 / 5

Actionability

Patterns are vague one-liners ('Store and update facts about entities', 'Include relevant memories in prompts') with no code or commands, and the sharp-edges 'solutions' are placeholder comments ('// Implement memory lifecycle management') rather than executable guidance, fitting 'minimal concrete guidance; high-level hints but missing the specific steps.'

2 / 5

Workflow Clarity

There is no coherent multi-step workflow — the only numbered list is truncated mid-sentence ('1. Memory types differ—short-term, lo') — and despite the sharp-edges table flagging destructive/critical risks (cross-user memory access) there are no validation checkpoints, matching the 'steps missing or incoherent; no sequence; no validation' anchor.

1 / 5

Progressive Disclosure

The body is sectioned (Capabilities, Patterns, Anti-Patterns, Sharp Edges, Related Skills) but several sections are empty shells (Anti-Patterns has only ❌ headers with no content) and there are no references at all; it has some structure but is incompletely organized, matching the 'some structure but could be better organized' anchor.

3 / 5

Total

8

/

20

Passed

Description

70%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 reasonably specific with a clear Use-when trigger list and a distinct niche, though it enumerates memory categories rather than concrete actions. It is a competent but not exemplary description.

Suggestions

Lead the 'what' with concrete verbs (e.g. 'Store, retrieve, and consolidate conversation memories across short-term, long-term, and entity stores') instead of listing category nouns.

Add common synonyms and a file/format hook to the trigger list (e.g. 'remember what I said', 'memory store', 'conversation history') to push trigger coverage toward 5.

Keep the existing explicit 'Use when:' clause — it is the strongest part of the description.

DimensionReasoningScore

Specificity

Names the domain ('persistent memory systems for LLM conversations') and enumerates concrete sub-types ('short-term, long-term, and entity-based memory'), but lists categories rather than concrete actions like store/retrieve/consolidate, so it sits at the 'names domain and 1-2 concrete actions' anchor rather than the comprehensive 5.

3 / 5

Completeness

It has both a 'what' (persistent memory systems with tiered/entity types) and an explicit 'when' (a Use when clause with multiple triggers); not a 5 because the 'what' is a category enumeration rather than the crisp action verbs in the 5 anchor.

4 / 5

Trigger Term Quality

The 'Use when:' clause supplies several natural user phrases ('conversation memory, remember, memory persistence, long-term memory, chat history') giving good keyword coverage, though a few common synonyms are missing, matching the 'good keyword coverage; a few natural terms missing' anchor.

4 / 5

Distinctiveness Conflict Risk

'Persistent memory systems for LLM conversations' carves a mostly-distinct niche with dedicated triggers, with only minor overlap risk against closely related skills like rag-implementation or context-window-management, matching the 'mostly distinct; minor overlap' anchor.

4 / 5

Total

15

/

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.

Validation15 / 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
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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