CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

agent-memory-system

Persistent memory architecture for AI agents across sessions. Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context). Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory.

Quality

69%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

Quality

Content

42%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 reads as an architecture design document rather than an executable skill: it is long and padded, its core store/recall API and CLI commands have no implementations, and its maintenance workflows include unvalidated destructive cleanup. Internal structure is good and the file layout is concrete, but the content needs heavy trimming and real reference files.

Suggestions

Trim the per-memory-type "Benefits" lists and the episodic/procedural/semantic taxonomy explanations that Claude already knows, and cut the body to a concise overview with quick-reference tables.

Move the per-type JSON structures, hook scripts, and database (Pinecone/Neo4j) integration details into actual files under references/ (creating them) and link to them one level deep; fix or remove the dangling "see examples/" pointer.

Either provide real implementing scripts for storeMemory/recallMemory and the recall-episode/get-skill/query-knowledge commands, or replace them with direct file-based operations (Read/Write on .memory/ paths) that Claude can actually execute, and add a validation step (e.g., dry-run or backup) before the destructive find-delete cleanup in the consolidation script.

DimensionReasoningScore

Conciseness

The ~950-line body pads each memory type with repeated "Benefits" lists ("Avoid repeating mistakes", "Faster decision-making") and re-explains the standard episodic/procedural/semantic memory taxonomy Claude already knows. There is real novel content (the .memory/ layout, hooks, consolidation), which keeps it above the severely-verbose anchor 1, but several sections are noticeably padded.

2 / 5

Actionability

The central examples are pseudocode: `storeMemory({...})` and `recallMemory({...})` TypeScript calls reference an undefined API with no implementing scripts in a bundle, and `recall-episode --similar "implementing OAuth"`, `get-skill`, `query-knowledge` are CLI tools that do not exist anywhere. The hook bash scripts are semi-executable and the Python snippets rely on undefined helpers (`get_embedding`, `load_all_episodes`) plus a deprecated `pinecone.init` API, so guidance is concrete-looking but incomplete and non-executable.

3 / 5

Workflow Clarity

Sequences exist (store after events, recall before tasks, session start/end hooks, nightly consolidation), but validation checkpoints are absent. The consolidation script performs destructive batch operations ("find .memory/short-term/ -name \"*.json\" -mtime +7 -delete" and archiving episodes after 90 days) with no verification step, which caps workflow clarity at 3 per the destructive/batch rule.

3 / 5

Progressive Disclosure

Section headers and quick-reference tables provide some structure, but the ~950-line monolithic body inlines per-type JSON schemas and database-integration details that clearly belong in separate reference files, and no bundle files exist at all. The closing pointer "For integration examples, see examples/" references a nonexistent directory, leaving navigation incomplete but not buried, so this sits above the anchor-2 monolith.

3 / 5

Total

11

/

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: third-person voice, concrete four-component architecture summary, and an explicit "Use when..." clause with multiple natural trigger phrases. Minor deductions for component-style (rather than action-style) capability listing and a few missing common trigger variations.

DimensionReasoningScore

Specificity

"Persistent memory architecture for AI agents across sessions" names the domain, and "Episodic memory (past events), procedural memory (learned skills), semantic memory (knowledge graph), short-term memory (active context)" lists several specific concrete components. It falls short of 5 because it describes architectural components rather than a comprehensive list of concrete actions, and above 3 because coverage goes beyond 1-2 items.

4 / 5

Completeness

It clearly answers what ("Persistent memory architecture... Episodic... procedural... semantic... short-term memory") and explicitly answers when with concrete triggers ("Use when implementing cross-session persistence, skill learning, context preservation, personalization, or building truly adaptive AI systems with long-term memory"). This matches the anchor-5 pattern of explicit what-and-when with concrete trigger phrases.

5 / 5

Trigger Term Quality

"cross-session persistence, skill learning, context preservation, personalization", and "long-term memory" are natural phrases a user would plausibly say. Not 5 because common variations like "remember preferences", "agent memory", or "memory system" are missing; not 3 because coverage is genuinely good rather than partial.

4 / 5

Distinctiveness Conflict Risk

"Persistent memory architecture for AI agents across sessions" carves a clear agent-memory niche with distinct triggers, though "personalization" and "context preservation" have minor overlap risk with generic context-management skills. Not 5 due to that overlap; clearly above 3 since the memory-system framing is distinctive.

4 / 5

Total

17

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (958 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

14

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

skills/agent-memory-system/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.