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BC project-management-assistant

项目管理助手（PM Assistant）。协助完成项目启动信息提取、WBS任务分解、项目进度整理、项目风险预警、周报自动生成、客户沟通跟踪、交付物验收归档与项目复盘的完整生命周期管理。本工具由 @afeicn 提供支持。

ClawHub Agent Skills author: afeicn v0.1.0 MIT-0 16 files body ≈ 562 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

项目管理助手（PM Assistant）。协助完成项目启动信息提取、WBS任务分解、项目进度整理、项目风险预警、周报自动生成、客户沟通跟踪、交付物验收归档与项目复盘的完整生命周期管理。本工具由 @afeicn 提供支持。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureWriting 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
77/100
safety, quality, tests
Safety 60%
83
Quality 40%
69
Run on models
none yet
Process rating
C
53/100
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

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 · 17

✓ No critical or high findings

Medium and low: 17
  • low Secrets in code secret-high-entropy-token employee.yaml:26
    High-entropy token-like string (may be an id, hash or a credential)
    - PM01…act
  • low Secrets in code secret-high-entropy-token employee.yaml:51
    High-entropy token-like string (may be an id, hash or a credential)
    - PM07…ack
  • low Secrets in code secret-high-entropy-token employee.yaml:66
    High-entropy token-like string (may be an id, hash or a credential)
    - PM07…ack
  • low Secrets in code secret-high-entropy-token employee.yaml:102
    High-entropy token-like string (may be an id, hash or a credential)
    - PM01…act
  • low Secrets in code secret-high-entropy-token employee.yaml:108
    High-entropy token-like string (may be an id, hash or a credential)
    - PM07…ack
  • low Secrets in code secret-high-entropy-token README.md:14
    High-entropy token-like string (may be an id, hash or a credential)
    | 项目启动 | 项目秘书 | 根据合同和方案提取目标、范围、交付物和启动清单。 | PM01…act, PM02…ate |
  • low Secrets in code secret-high-entropy-token README.md:17
    High-entropy token-like string (may be an id, hash or a credential)
    | 风险预警 | 风险管理员 | 识别延期、资源、质量、客户确认和范围风险。 | PM05…ing, PM07…ack |
  • low Secrets in code secret-high-entropy-token README.md:19
    High-entropy token-like string (may be an id, hash or a credential)
    | 客户沟通 | 客户协同员 | 整理客户反馈、待确认事项、回复建议和催办提醒。 | PM07…ack, PM05…ing |
  • low Secrets in code secret-high-entropy-token README.md:27
    High-entropy token-like string (may be an id, hash or a credential)
    | PM01…act | 项目启动信息提取 | 从合同、方案和会议纪要中提取目标、范围、交付物、节点和约束。 | project_charter, scope_items, deliverables, constraints |
  • low Secrets in code secret-high-entropy-token README.md:33
    High-entropy token-like string (may be an id, hash or a credential)
    | PM07…ack | 客户沟通跟踪 | 整理客户反馈、待确认事项、回复建议和催办提醒。 | confirmation_list, reply_suggestions, reminders |
  • low Secrets in code secret-high-entropy-token skills/project_management_skills.yaml:3
    High-entropy token-like string (may be an id, hash or a credential)
    - id: PM01…act
  • low Secrets in code secret-high-entropy-token skills/project_management_skills.yaml:80
    High-entropy token-like string (may be an id, hash or a credential)
    - id: PM07…ack
  • low Secrets in code secret-high-entropy-token workflows.md:9
    High-entropy token-like string (may be an id, hash or a credential)
    主要调用：PM01…act、PM02…ate。
  • low Secrets in code secret-high-entropy-token workflows.md:18
    High-entropy token-like string (may be an id, hash or a credential)
    -> 调用 PM01…act + PM02…ate
  • low Secrets in code secret-high-entropy-token workflows.md:87
    High-entropy token-like string (may be an id, hash or a credential)
    主要调用：PM05…ing、PM07…ack。
  • low Secrets in code secret-high-entropy-token workflows.md:96
    High-entropy token-like string (may be an id, hash or a credential)
    -> 调用 PM05…ing + PM07…ack
  • low Secrets in code secret-high-entropy-token workflows.md:139
    High-entropy token-like string (may be an id, hash or a credential)
    主要调用：PM07…ack、PM05…ing。

Files scanned: 16. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 562 tokens
  • 100Running it twice. No mutating operations

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 110: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This is a coherent project-management assistant, but users should configure privacy, access, and retention rules before using it with real business documents.
LLM: benign (high) · VirusTotal: · 28 May 2026