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BF periodic-reflection

周期性反思报告生成工具。用于自动化生成结构化的自我进化反思报告，支持多场景（EvoMap 发布、Agent 进化、DevOps 运维等）。 **触发场景：** - 用户要求生成周期性反思报告 - 需要量化指标对比和版本追踪 - 需要数据驱动的优化决策 - 用户提到"反思"、"复盘"、"进化报告"、"周期性总结" - 需要固化优化成果和 changelog

ClawHub Agent Skills author: jpengcheng523-netizen v1.0.0 MIT-0 11 files body ≈ 946 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 33/100 · Will not run — References files that are not bundled: scripts/metrics-collector.js

ProcedureInfrastructureAI and agentsWriting and documentstype 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
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
71
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: scripts/metrics-collector.js
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Dangerous commands cmd-cron-mention README.md:23
    Mentions editing / listing crontab
    crontab -e
  • low Dangerous commands cmd-cron-mention RELEASE.md:45
    Mentions editing / listing crontab
    crontab -e
  • low Dangerous commands cmd-cron-mention SKILL.md:73
    Mentions editing / listing crontab
    crontab -e

Files scanned: 11. 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/metrics-collector.js

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: scripts/metrics-collector.js
  • 0Tools and files. 1 referenced file(s) missing: scripts/metrics-collector.js
  • 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
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 946 tokens
  • low 10 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
  • -216 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 179: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This skill is local and not destructive, but its scheduled auto mode can repeatedly generate reports using hardcoded healthy metrics instead of real monitoring data.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026