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CD self-reflection

自我复盘与持续改进技能。当用户要求"复盘"、"总结经验"、"记录教训"、 "自我提升"、"持续改进"、"错题本"、"学习日志"时触发。 主动在每次完成任务、犯错、学到新知后，将内容写入 reflections/。

ClawHub Agent Skills author: woodylan v1.0.1 MIT-0 6 files · 1 script body ≈ 670 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype 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
71/100
safety, quality, tests
Safety 60%
75
Quality 40%
66
Run on models
none yet
Process rating
D
49/100
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

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. 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
  • medium Dangerous commands cmd-persistence scripts/daily_reflect.py:17
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (string literal in code, not executed)
    PLIST_FILE = os.path.expanduser("~/Library/LaunchAgents/com.openclaw.daily-reflect.plist")
    code literal
  • medium Dangerous commands cmd-eval-dynamic scripts/daily_reflect.py:39
    Dynamic code execution from decoded/untrusted input
    os.system(f"launchctl unload '{PLIST_FILE}' 2>/dev/null")
  • medium Dangerous commands cmd-eval-dynamic scripts/daily_reflect.py:80
    Dynamic code execution from decoded/untrusted input
    result = os.system(f"launchctl load '{PLIST_FILE}' 2>/dev/null")
  • medium Dangerous commands cmd-persistence scripts/daily_reflect.py:80
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (quoted — discussed, not commanded)
    result = os.system(f"launchctl load '{PLIST_FILE}' 2>/dev/null")
    quoted
  • medium Dangerous commands cmd-persistence scripts/daily_reflect.py:84
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (string literal in code, not executed)
    print(f"⚠️ plist 已创建但 launchctl load 失败，请手动执行：launchctl load '{PLIST_FILE}'")
    code literal

Files scanned: 6. 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 49/100

  • 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
  • 40Consistency. Frontmatter name (self-reflection) differs from the folder (self-reflection-lan)
  • 100Tools and files. No external tools needed
  • 100Steps. 25 steps
  • 100Execution cost. Instruction body is 670 tokens
  • 100Running it twice. No mutating operations
  • low 15 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)
  • +3Description length 106: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -31 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This self-reflection skill stores local Markdown notes and can optionally set up daily reminders, but the behavior is mostly disclosed, local, and aligned with its stated purpose.
LLM: benign (high) · VirusTotal: · 29 May 2026