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Not recommendedlow grade D
ClawHub Agent Skills author: hualang-C v1.0.1 MIT-0 10 files · 2 scripts body ≈ 1 537 tokens Open the sourceclawhub.ai↗ analyzed 31 h ago

bash cd skills/memory-c

As a process C 55/100 · Has gaps — 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
D
48/100
safety, quality, tests
Safety 60%
80
Quality 40%
0
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Add a description to the frontmatter: without it the skill never triggers.
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 · 4

✓ No critical or high findings

Medium and low: 4
  • medium Dangerous commands cmd-eval-dynamic bench_lmdb.py:30
    Dynamic code execution from decoded/untrusted input
    os.system(f"rm -rf {env_path}")
  • medium Dangerous commands cmd-eval-dynamic bench_lmdb.py:72
    Dynamic code execution from decoded/untrusted input
    os.system(f"rm -rf {env_path}")
  • medium Dangerous commands cmd-eval-dynamic bench_lmdb.py:82
    Dynamic code execution from decoded/untrusted input
    os.system(f"rm -f {db_path}")
  • medium Dangerous commands cmd-eval-dynamic bench_lmdb.py:126
    Dynamic code execution from decoded/untrusted input
    os.system(f"rm -f {db_path}")

Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error frontmatter SKILL.md: no YAML frontmatter block found
  • error name-missing SKILL.md: frontmatter has no `name`
  • error description-missing SKILL.md: no `description` — the skill can never trigger

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 31 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1537 tokens
  • 100Progress reporting. Reports progress
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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)
  • +3Description length 0: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 0.

External checks

ClawHub: suspicious
The core memory engine matches its purpose, but the package includes under-disclosed benchmark/test behaviors that can start a Redis daemon, delete temporary directories, and persist agent memory data in plaintext.
LLM: suspicious (medium) · 9 Jun 2026