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DD skill-distributor

将 AI 技能包（含 SKILL.md 的目录）先做 32 维生产级预检（frontmatter/slug/版本/脚本语法/敏感信息密钥扫描/提示注入/体积等静态门禁），通过后再以 GitHub 为单一事实源一键发布，并自动同步到 ClawHub、官方 MCP Registry、腾讯 SkillHub 及 Gitee/GitLab 等镜像平台。当用户要求"发布/分发/上架/同步技能"、"检测/测试技能质量/发布前检查/生产级验收"、"把技能推到 GitHub 和其他平台"、"建立技能分发流程"或需要给本地技能目录做版本化分发时使用。检测不过（P0/P1）默认阻断发布；仅显式触发时发布（本地一键命令），无任何自动发布/自动同步机制，密钥本地存储。

ClawHub Agent Skills author: won v1.1.3 MIT-0 6 files · 3 scripts body ≈ 0 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

将 AI 技能包（含 SKILL.md 的目录）先做 32 维生产级预检（frontmatter/slug/版本/脚本语法/敏感信息密钥扫描/提示注入/体积等静态门禁），通过后再以 GitHub 为单一事实源一键发布，并自动同步到 ClawHub、官方 MCP Registry、腾讯 SkillHub 及…

As a process D 38/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

ReferenceGitHubGitLabAI 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
D
61/100
safety, quality, tests
Safety 60%
100
Quality 40%
2
Run on models
none yet
Process rating
D
38/100
Unfinished process
Steps w 15
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.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error body-empty SKILL.md: empty instructions body
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "summary"

Process rating: all ten parameters 38/100

  • 0Steps. Prose only: no discrete steps
  • 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
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 0 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)
  • +4Structure: 0 headings, hard to scan
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -15SKILL.md body under 300 characters: nearly empty
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -33 of 3 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 326: enough signal without eating the budget
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 2.

External checks

ClawHub: suspicious
This is a legitimate skill-publishing helper, but it gives local configuration and tokens enough power to run high-impact publish actions without sufficient safeguards.
LLM: suspicious (high) · 18 Sept 2026