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AC memoria-persistente-openclaw

Persistent memory system for AI agents inspired by Letta/MemGPT. Three-layer memory architecture (Core, Archival, Recall) that lets agents remember across sessions. Use when: (1) agent needs to recall user preferences, context, or decisions from past conversations, (2) starting a new session and needing context continuity, (3) user says 'remember this', 'you should know', or 'like last time', (4) agent detects important information worth persisting, (5) periodic memory maintenance during heartbeats. NOT for: simple Q&A with no persistence need.

ClawHub Agent Skills author: renateparma v1.0.0 MIT-0 8 files body ≈ 1 167 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 58/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (memoria-persistente-openclaw) differs from the folder (memoria-persistente-agentes)
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 36 steps
    • 100Execution cost. Instruction body is 1167 tokens
    • 100Running it twice. Mutating operations check current state

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 550: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (7 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    This persistent-memory skill is not visibly malicious, but it can store personal and session history broadly without clear consent, review, deletion, or workspace-boundary controls.
    LLM: suspicious (high) · 28 May 2026