AC pipeline-architecture
採用 Pipeline Architecture（先宣告意圖、後統一執行）的專案，其業務邏輯讀寫流程的結構規範。同時支援 Python/FastAPI 與 TypeScript/Node.js 兩種實作，並提供 Payload CMS 基底專案的專屬補充。當專案已採用此架構、且任務涉及業務邏輯流程時使用，包含：新增 API endpoint、設計資料寫入流程、實作權限檢查、多步驟資料處理、跨系統寫入（DB、外部 API、裝置變數）、審計日誌，或任何「先決策後執行」的 workflow。看到 pipeline、step、query、mutation、scratch、persistence、StepCommit、StepStop、DataMutation、run_workflow、make_pipeline 等本架構專有名詞，或要在既有 pipeline 專案中修改業務邏輯時，讀取此 Skill 並依專案語言載入對應的 references 檔案；專案若以 Payload CMS 為基底（存在 payload.config.ts、collections/、custom endpoints、collection hooks），額外載入 references/payload-cms.md。不適用於一次性腳本、prototype、純前端、資料分析，或專案尚未採用此架構的情況；使用者明確指示其他做法時，以使用者指示為準。
採用 Pipeline Architecture（先宣告意圖、後統一執行）的專案，其業務邏輯讀寫流程的結構規範。同時支援 Python/FastAPI 與 TypeScript/Node.js 兩種實作，並提供 Payload CMS 基底專案的專屬補充。當專案已採用此架構、且任務涉及業務邏輯流程時使用，包含：新增…
As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 50/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 16 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 60 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3878 tokens
- 100Progress reporting. Reports progress
- low 15 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
- -249 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 614: enough signal without eating the budget
- +4Structure: 26 headings
- +3Step-by-step instructions: 60 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.