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FDE Skill——帮 FDE（前线部署工程师）更好完成企业 AI 落地的方法论 Skill。约束 Agent 行为、审计每次变更、沉淀经验。 底层实现叫约束层——一个层五种能力：注入·审计·回溯·沉淀·进化。FORGE 自迭代工具链是内部开发工具。 内置持续优化模式（sustain），自动读 audit 报告趋势生成优化报告。

ClawHub Agent Skills author: KongFangXun v1.4.7 MIT-0 27 files body ≈ 1 642 tokens Open the sourceclawhub.ai↗ analyzed 33 h ago

FDE Skill——帮 FDE（前线部署工程师）更好完成企业 AI 落地的方法论 Skill。约束 Agent 行为、审计每次变更、沉淀经验。 底层实现叫约束层——一个层五种能力：注入·审计·回溯·沉淀·进化。FORGE 自迭代工具链是内部开发工具。 内置持续优化模式（sustain），自动读 audit…

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI 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
86/100
safety, quality, tests
Safety 60%
99
Quality 40%
67
Run on models
none yet
Process rating
D
45/100
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-pipe-to-shell harness/entry-gate.md:20
    Downloads and executes remote code from an unrecognised host (pipe to shell) (documentation table row; documentation of a security skill)
    | bash | `command -v bash` | 不可 `rm -rf /`/删非项目文件/改系统配置/`curl\|bash` | ✅ | ⚠️ | ❌ | ✅/❌/N/A |
    tablesecurity skill

Files scanned: 23. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "image"
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "scenarios"
  • note frontmatter-key unknown frontmatter key "not_when"
  • note frontmatter-key unknown frontmatter key "solves"

Process rating: all ten parameters 45/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1642 tokens
  • 100Progress reporting. Reports progress
  • low 12 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
  • +4No input/output examples
  • -213 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 166: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 19 items
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
The skill is broadly disclosed as an enterprise AI deployment and governance system, but it asks users to enable high-impact automation through under-scoped shell, install, persistence, and MCP mechanisms.
LLM: suspicious (high) · 11 Sept 2026