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AC andrew-memory

Product-grade semantic memory layer for AI agents using LanceDB. Provides long-term memory with semantic search, Core Identity management, and conversation distillation. Use when: (1) Learning new facts about the user, (2) Searching for past context, (3) Maintaining consistent persona across sessions, (4) Extracting key memories from conversations.

ClawHub Agent Skills author: PanyuuGit v1.0.0 MIT-0 7 files body ≈ 667 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

PersonaAI and agentsInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
95
Quality 40%
82
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token package-lock.json:51
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…LRB+Iwx/uvwt…Jwj/5voteal+53jQ…wUw==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:67
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…xPU+DMt6…J8w==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:131
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…S4n+SYBL…DIg==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:163
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…u8g+OEVd…VlM/cyFY…Tgh/ShZZI9ed+ozEq+Ngt+rgmUs8tw==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:173
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…nOa+n+n5rE…eab/duDP…2Kw==",
      detector

    Files scanned: 7. 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 61/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 667 tokens
    • 100Running it twice. No mutating operations

    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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 350: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (2 code blocks)

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

    External checks

    ClawHub: clean
    This is a coherent long-term memory plugin, but it can persist user facts and send memory or conversation text to MiniMax when API mode is used.
    LLM: benign (high) · VirusTotal: · 29 May 2026