基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/arisng/github-copilot-fc --skill kimi-fleet-mini命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | kimi-fleet-mini |
| description | Multi-iteration parallel subagent orchestrator for Kimi Code CLI |
| type | flow |
| metadata | {"author":"arisng","version":"0.3.0"} |
Dispatch subagents in parallel waves to complete complex work.
flowchart TD
BEGIN([BEGIN]) --> INIT[Initialize Session]
INIT --> PLAN[Create Master Plan]
PLAN --> WAVE{Execute Wave}
WAVE -->|Tasks ready| DISPATCH[Dispatch Subagents]
WAVE -->|All done| REVIEW[Review Results]
DISPATCH --> COLLECT[Collect Results]
COLLECT --> WAVE
REVIEW --> VALIDATE{Validation Pass?}
VALIDATE -->|Issues found| FIX[Create Fix Tasks]
FIX --> WAVE
VALIDATE -->|Clean| UI_TEST[UI Testing]
UI_TEST -->|Failures| FIX
UI_TEST -->|All pass| COMMIT[Atomic Commits]
COMMIT --> DOCUMENT[Document Insights into Wiki]
DOCUMENT --> END([END])
Agent tool with run_in_background=truecoder, explore, or plan subagent types appropriatelyTrigger: After VALIDATE passes with no remaining issues and the task has a visual/browser-facing component. Skip this step for pure back-end or CLI tasks.
Apply the playwright-cli skill to exercise the UI exactly as a real human user would.
All screenshots are persisted into the current Kimi Code session folder:
~/.kimi/sessions/<workspace-hash>/<current-session-id>/.fleet/screenshots/
Resolve the path at runtime before taking any screenshots:
# Kimi Code exposes the session root via environment variable
SCREENSHOT_DIR="${KIMI_SESSION_DIR}/.fleet/screenshots"
mkdir -p "$SCREENSHOT_DIR"
If KIMI_SESSION_DIR is unavailable, derive it manually:
# Find the most-recently modified session directory
SESSION_ROOT=~/.kimi/sessions
SCREENSHOT_DIR=$(ls -dt "$SESSION_ROOT"/**/* 2>/dev/null | head -1)/.fleet/screenshots
mkdir -p "$SCREENSHOT_DIR"
<step-number>-<action-slug>-<YYYYMMDDTHHmmss>.png
Examples: 01-initial-load-20260402T143000.png, 03-submit-form-20260402T143015.png
http://localhost:3000).playwright-cli open http://localhost:3000
playwright-cli screenshot
# Then move/copy to screenshot dir with proper name
snapshot to read element refs, then click / fill / select to interact.snapshot before and after each significant action.playwright-cli screenshot
cp screenshot.png "$SCREENSHOT_DIR/02-after-login-$(date +%Y%m%dT%H%M%S).png"
playwright-cli console
playwright-cli network
eval to read DOM state and confirm expected values are rendered:
playwright-cli eval "document.querySelector('h1').textContent"
| Result | Condition | Next step |
|---|---|---|
| All pass | No console errors, all assertions green, screenshots show expected UI | → COMMIT |
| Failures found | Console errors, unexpected UI state, broken interactions | Create FIX tasks → loop back to WAVE |
Screenshots saved under .fleet/screenshots/ are not committed to git. They remain in the Kimi session folder as ephemeral evidence for the current run. The DOCUMENT step may reference their filenames in the explanation wiki page.
Trigger: After VALIDATE passes with no remaining issues.
Apply the git-atomic-commit skill (with git-commit-scope-constitution as its companion):
.github/git-scope-constitution.md.
git-commit-scope-constitution to generate it from the repository structure and git history before proceeding.git status --short and diff each changed file.git-atomic-commit. Every file gets a type(scope) label before any grouping begins.type(scope): subject + optional body.Autonomous mode: Execute all commits automatically without user prompts; surface a summary when done.
Trigger: After COMMIT completes successfully.
Apply the diataxis + diataxis-categorizer skills to capture what was learned:
Capture content that would benefit future iterations or agents:
| Source | Target Diátaxis category | Examples |
|---|---|---|
| Reusable procedures discovered during the task | How-to guide | "How to configure X", "How to run Y pipeline" |
| Technical facts, APIs, config options | Reference | Data model schemas, CLI flag lists |
| Conceptual understanding gained | Explanation | Why the architecture is structured this way |
| Step-by-step learning journeys | Tutorial | First-time setup walkthrough |
Lessons learned and gotchas always go into an Explanation page (or appended to an existing one).
Use the .docs/ structure:
.docs/
├── tutorials/ # Learning-oriented lessons
├── how-to/ # Task-oriented guides
├── reference/ # Technical descriptions
└── explanation/ # Conceptual discussions + lessons learned
Apply the diataxis-categorizer three-rule heuristic to determine the correct sub-folder:
<category>/<domain>/ folder exists, place the file there.diataxis-categorizer to find or create the correct sub-folder path..md file using the appropriate template from the diataxis skill.python skills/diataxis/scripts/generate_index.py (if available) to refresh .docs/index.md.docs(...) commit (do not mix with code commits from the COMMIT step).Drive a Machina state machine to a deterministic outcome on behalf of an AI agent. USE WHEN: a task is governed by a state machine (a machine definition JSON with states, transitions, guards, actions, tools, checks, scenarios); executing a workflow that must be deterministic and auditable; running a skill whose SKILL.md declares a `machina:` machine; firing events, checking status, satisfying evidence checks, verifying run integrity with `check`, or producing a terminal report for a machine-driven run; escalating a STUCK run to a human conductor. DO NOT USE FOR: authoring or scoring machine definitions (use machina-authoring); modifying the Machina simulator app or its engine (use machina-simulator); implementing or modifying the driver/runtime tooling such as `scripts/machine-driver.py` (engine development) unless you are a maintainer of this skill upgrading the driver itself; general diagramming or XState/SCXML authoring.
Author valid, high-scoring state machines in Machina machine JSON format (spec v3.0.0 / v2.0.0 / v1.0.0). USE WHEN: writing or generating a machine definition (states, transitions, guards, actions, context, scenarios); modeling a real workflow (order fulfillment, refunds, signup, retries) as a Machina state machine; fixing or upgrading a machine JSON for validation or higher compliance score; explaining validation failures or low scores; running the bundled machina-validator.py CLI to validate, score, or generate gaps/scenarios; adding retry guards or cycle protection; or preparing machines for the compliance scorer ("Excellent" ≥90). DO NOT USE FOR: modifying the Machina simulator app, its engine, UI, or SPEC_REGISTRY (use machina-simulator-maintenance), debugging machine-validator.py scripts, XState config authoring, SCXML documents, or general diagramming.
Spawn a new isolated Copilot CLI sub-session from a main Copilot CLI or VS Code session. Use when you need to programmatically create, resume, or chain Copilot CLI sessions with explicit control over session ID, custom agent, model, BYOK provider profile, permissions, and working directory. Triggers: "invoke copilot", "spawn copilot", "copilot sub-session", "programmatic copilot cli", "programmatic copilot subprocess", "copilot cli session id", "resume copilot session", "chain copilot sessions", "isolated copilot session", "subagent copilot cli", "task copilot cli".