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BF 灵枢·AI全栈构建师

灵枢·AI全栈构建师。v1.0专业全栈开发指导，集成Agent工作流、Vibe Coding方法论、2026最新技术栈、Harness Engineering驾驭工程（四大护栏+五大上下文模式）。覆盖前端、后端、移动应用、游戏开发、架构设计，提供最佳实践与代码模板。持续蒸馏进化，成为您可信赖的全栈开发伙伴。

ClawHub Agent Skills author: 迪迪 v1.0.0 MIT-0 80 files body ≈ 1 913 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 28/100 · Will not run — References files that are not bundled: references/tech_stack_recommendations.md

ProcedureSoftware developmentInfrastructuretype 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
83/100
safety, quality, tests
Safety 60%
99
Quality 40%
59
Run on models
none yet
Process rating
F
28/100
Will not run
References files that are not bundled: references/tech_stack_recommendations.md
Tools and files w 18
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.
  2. The text references files that are not there: add them or drop the references.
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 Secrets in code secret-high-entropy-token references/security_best_practices.md:449
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    algorithm: 'AEAD…tic'
    quoted

Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: references/tech_stack_recommendations.md
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 28/100

Will not run. References files that are not bundled: references/tech_stack_recommendations.md
  • 0Tools and files. 1 referenced file(s) missing: references/tech_stack_recommendations.md
  • 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
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (灵枢·AI全栈构建师) differs from the folder (full-stack-architect)
  • 100Steps. 71 steps
  • 100Execution cost. Instruction body is 1913 tokens
  • low 10 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
  • -273 emoji in the instructions: noise for the model
  • -38 of 8 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 71 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 20)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.

External checks

ClawHub: suspicious
This appears to be a real full-stack development assistant, but it needs Review because it bundles broad code execution, external AI-provider calls, and persistent file-writing behavior without enough scoping or user controls.
LLM: suspicious (high) · 28 May 2026