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BD tiangong-skill

天工.skill(Tiangong Skill)智能体设计师。当用户需要设计、创建或优化AI智能体/Agent，或基于真实人物蒸馏思维框架创建人物Skill时使用。支持两种范式：人物蒸馏（由内而外，复刻心智模型）与岗位型专家（由外而内，定义岗位职责）。目标：创建专业领域专家角色，具备清晰人设和扎实交付力。

ClawHub Agent Skills author: ebandao v1.0.1 MIT-0 22 files body ≈ 2 586 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

天工.skill(Tiangong Skill)智能体设计师。当用户需要设计、创建或优化AI智能体/Agent，或基于真实人物蒸馏思维框架创建人物Skill时使用。支持两种范式：人物蒸馏（由内而外，复刻心智模型）与岗位型专家（由外而内，定义岗位职责）。目标：创建专业领域专家角色，具备清晰人设和扎实交付力。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureAI and agentstype 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
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
63
Run on models
none yet
Process rating
D
46/100
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

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: 22. 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 "trigger_words"
  • note frontmatter-key unknown frontmatter key "domain"
  • note frontmatter-key unknown frontmatter key "emoji"

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 56 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2586 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)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • -235 emoji in the instructions: noise for the model
  • -32 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 153: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (7 of 9)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: clean
This is a coherent skill-building toolkit with disclosed local output creation and validation scripts; the main caution is broad auto-trigger wording, not hidden or destructive behavior.
LLM: benign (high) · VirusTotal: · 15 Aug 2026