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CC self-evolution-cn

多 agent 自我进化系统，自动记录学习、错误和功能需求，支持多 agent 统计和自动提升

Not recommendedcritical or high security findings
ClawHub Agent Skills author: cheney v2.1.1 MIT-0 21 files · 6 scripts body ≈ 1 092 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
C
72/100
safety, quality, tests
Safety 60%
71
Quality 40%
74
Run on models
none yet
Process rating
C
55/100
Has gaps
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
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. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 8

  • high Dangerous commands cmd-persistence scripts/setup.sh:191
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    (crontab -l 2>/dev/null; echo "$CRON_JOB") | crontab -
Medium and low: 7
  • medium Dangerous commands cmd-persistence scripts/setup.sh:187
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (detector / deny-list definition)
    crontab -l 2>/dev/null | grep -v "trigger-daily-review.sh" | crontab -
    detector
  • low Dangerous commands cmd-cron-mention references/openclaw-integration.md:82
    Mentions editing / listing crontab (quoted — discussed, not commanded)
    1. 检查 crontab：`crontab -l`
    quoted
  • low Dangerous commands cmd-cron-mention references/openclaw-integration.md:97
    Mentions editing / listing crontab
    crontab -l | grep trigger-daily-review
  • low Dangerous commands cmd-cron-mention scripts/setup.sh:187
    Mentions editing / listing crontab
    crontab -l 2>/dev/null | grep -v "trigger-daily-review.sh" | crontab -
  • low Dangerous commands cmd-cron-mention scripts/setup.sh:191
    Mentions editing / listing crontab
    (crontab -l 2>/dev/null; echo "$CRON_JOB") | crontab -
  • low Dangerous commands cmd-cron-mention scripts/setup.sh:258
    Mentions editing / listing crontab
    if crontab -l 2>/dev/null | grep -q "trigger-daily-review.sh"; then
  • low Dangerous commands cmd-cron-mention SKILL.md:37
    Mentions editing / listing crontab
    crontab -e  # 添加：0 0 * * * ~/.openclaw/skills/self-evolution-cn/scripts/trigger-daily-review.sh >> ~/.openclaw/skills/self-evolution-cn/logs/heartbeat-daily.log 2>&1

Files scanned: 21. 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 55/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 46 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1092 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

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 47: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 46 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (5 of 6)
  • +3All 6 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This skill is purpose-aligned self-improvement tooling, but it persistently records chat/tool data and can automatically change future agent behavior with broad local persistence.
LLM: suspicious (high) · VirusTotal: · 29 May 2026