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SEVO — Agent 研发流水线。14 阶段全链路交付：需求规格 → 门禁评审 → 测试用例 → 验收编写 → 架构契约 → 编码实现 → 独立审计 → 回归验证 → 发布门禁 → 部署 → 终验 → 交付账本。

ClawHub Agent Skills author: yuchangxu v1.13.1 MIT-0 46 files body ≈ 321 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 38/100 · Will not run — References files that are not bundled: scripts/init.sh

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
81/100
safety, quality, tests
Safety 60%
98
Quality 40%
55
Run on models
none yet
Process rating
F
38/100
Will not run
References files that are not bundled: scripts/init.sh
Tools and files w 18
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.
  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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token projects/sevo-p0-test-fix/pipelines/fr-sevo-p0-test-fix-20260524-001.json:91
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    "uxDesignReason": "LLM 判定失败，按安全默认值处理：LLM request failed (403): {\"error\":{\"code\":\"\",\"message\":\"This token has no access to model gpt-4o-mini (request id: 2026…eSS
    fixturequoted
  • low Secrets in code secret-high-entropy-token projects/sevo-p0-test-fix/pipelines/fr-sevo-p0-test-fix-20260524-001.json:93
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    "archDesignReason": "LLM 判定失败，按安全默认值处理：LLM request failed (403): {\"error\":{\"code\":\"\",\"message\":\"This token has no access to model gpt-4o-mini (request id: 2026…oqe
    fixturequoted

Files scanned: 46. 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")
  • warning missing-ref reference to a missing file: scripts/init.sh

Process rating: all ten parameters 38/100

Will not run. References files that are not bundled: scripts/init.sh
  • 0Tools and files. 1 referenced file(s) missing: scripts/init.sh
  • 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
  • 40Result and completion. Does not say what the result is
  • 100Steps. 9 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 321 tokens

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)
  • +3Description length 108: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -47 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 9 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.

External checks

ClawHub: suspicious
SEVO appears to be a real agent-development pipeline, but its artifacts describe automatic configuration changes, agent repair loops, scheduled scans, and publish/deploy actions that users should review before enabling.
LLM: suspicious (medium) · 28 May 2026