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AC task-decomposition

任务分解器。把一句目标/需求自动拆成可执行的层级化工作分解结构（WBS）：阶段 → 任务 → 步骤，附依赖关系、工时估算（S/M/L）与建议执行顺序。纯规则、本地可跑，把"我要做 X"变成"第 1 步做 A、第 2 步做 B"。当用户需要"把这个目标拆成任务""帮我规划步骤""做个 WBS""decompose this goal""制定执行计划"时调用。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 5 files body ≈ 262 tokens Open the sourceclawhub.ai↗ analyzed 35 h ago

任务分解器。把一句目标/需求自动拆成可执行的层级化工作分解结构（WBS）：阶段 → 任务 → 步骤，附依赖关系、工时估算（S/M/L）与建议执行顺序。纯规则、本地可跑，把"我要做 X"变成"第 1 步做 A、第 2 步做 B"。当用户需要"把这个目标拆成任务""帮我规划步骤""做个 WBS""decompose…

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

ProcedureSoftware developmenttype 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
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 · 0

✓ No critical or high findings

Files scanned: 5. 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 "agent_created"
  • note frontmatter-key unknown frontmatter key "visibility"

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. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 262 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 179: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (3 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: clean
This skill is a local task-planning helper with optional local learning files; its persistence is disclosed and user-invoked, though users should avoid recording sensitive notes.
LLM: benign (high) · VirusTotal: · 14 Aug 2026