SKILLEMALL.ai
GitHub ↗ RU Sign in

BD openclaw-memories

Agent memory with ALMA meta-learning, LLM fact extraction, and full-text search. Observer calls remote LLM APIs (OpenAI/Anthropic/Gemini). ALMA and Indexer work offline.

ClawHub Agent Skills author: Artale v2.0.1 13 files body ≈ 478 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
B
86/100
safety, quality, tests
Safety 60%
95
Quality 40%
72
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 pnpm-lock.yaml:66
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…QYA+ISs0/2l3T9/kj42…aQT/dfNXWX/ZZCQ==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:138
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha512-6+gjmF…uh8+uw3mnrvgs+dSPQ…dZG+D4garKg==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:144
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…DUr+vOv8…A8A==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:211
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…Q8E+t5Jh…rfX+bKrFe+Xp5Y…DAA==}
  • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:335
    High-entropy token-like string (may be an id, hash or a credential)
    resolution: {integrity: sha5…GD3+82K6JgJlm/Y+KI92…no5+4jh9sw==}

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (openclaw-memories) differs from the folder (openclaw-memory-2)
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Execution cost. Instruction body is 478 tokens

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 169: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This memory skill appears purpose-built, but its Observer can send full conversation text and potentially the wrong environment API key to external LLM providers.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026