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BF mempalace-openclaw

MemPalace memory system for OpenClaw/XClaw/WorkBuddy. Archive AI conversations to local long-term storage with semantic search. Commands: /mem-arc (archive), /mem-sea (search), /mem-asave (auto-save daily). Based on https://github.com/milla-jovovich/mempalace. Triggers: user wants to save, search or back up AI conversation memories.

ClawHub Agent Skills author: Deveuper v1.6.0 MIT-0 30 files · 2 scripts body ≈ 1 232 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 35/100 · Will not run — References files that are not bundled: scripts/archive.ps1

ProcedureGitHubInfrastructuretype 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
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/archive.ps1
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
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: 30. 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")
  • warning missing-ref reference to a missing file: scripts/archive.ps1

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/archive.ps1
  • 0Tools and files. 1 referenced file(s) missing: scripts/archive.ps1
  • 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
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1232 tokens
  • 100Running it twice. No mutating operations
  • low 11 top-level sections: this looks like several domains in one skill

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 334: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 17 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This appears to be a real local memory/search skill, but it needs Review because it can persist broad conversation and project data, modify the Python environment, auto-save sessions, and make Wikipedia lookups despite local-only messaging.
LLM: suspicious (high) · VirusTotal: · 29 May 2026