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BD clawmem

Lightweight memory management system for OpenClaw with 3-tier retrieval (L0/L1/L2), automatic lifecycle monitoring, and advanced search. Saves 60-80% on token costs while providing efficient memory storage and retrieval.

ClawHub Agent Skills author: ClawMem.com v0.1.0 MIT-0 12 files body ≈ 1 092 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

ProcedureInfrastructuretype 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%
97
Quality 40%
68
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

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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Exfiltration read-dotenv README-CN.md:81
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.md:81
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv SKILL.md:32
    Reads a .env file
    cp .env.example .env

Files scanned: 12. 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. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (clawmem) differs from the folder (claw-mem)
  • 100Tools and files. No external tools needed
  • 100Steps. 21 steps
  • 100Execution cost. Instruction body is 1092 tokens
  • low 12 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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 220: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
ClawMem appears to be a real memory tool, but it automatically records and keeps detailed agent activity locally without enough user control or privacy safeguards.
LLM: suspicious (high) · VirusTotal: · 29 May 2026