跳到正文
FunCoding

搜索

搜索文档、Skill 和 MCP

kimi-delegate

Delegate a coding task to the Kimi Code CLI (`kimi`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Kimi - phrasings like "have Kimi implement X", "delegate this to Kimi", "run it through Kimi Code", or "use Kimi to implement/fix/refactor" - or wants to run a queue of coding tasks through Kimi while staying the reviewer. DO NOT USE for tasks small enough to do inline, or when the user wants the code written directly without delegating.

代码质量与审查2.3kskills/kimi-delegate/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/amelnagdy/delegate-skills/kimi-delegate/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Kimi Delegate

You are the orchestrator. Hand a bounded coding task to a separate implementer - the Kimi Code CLI - then review what it produced and land it yourself. You write the brief and own the judgment; Kimi does the typing in its own session; you verify and commit.

The loop needs only a shell command and file access, so any comparable orchestrator can drive it.

When NOT to use this

  • The task is small enough to do inline; delegation overhead is not worth it.
  • The kimi CLI is not installed or authenticated.
  • You need a CLI-enforced read-only implementer. Headless Kimi has no read-only mode.

Prerequisites (check once)

  1. Install Kimi Code with brew install kimi-code on macOS/Linux, or use the native installer from the official Kimi Code documentation.
  2. Authenticate with kimi login (device-code flow, no TUI), or use /login in the TUI.
  3. Confirm kimi --version succeeds.
  4. Work in, or point --cd at, the target git repository.

Choose the model alias

Kimi uses default_model from its config.toml when --model is omitted. To choose another model alias, pass --model <alias from your kimi config>. Model aliases are user-defined config keys; use one the human has configured rather than inventing one.

The loop

Run these five steps per task. Steps 1, 4, and 5 require judgment; 2 and 3 are mechanical.

1. Write the brief

Kimi sees only the text you send plus what it can inspect in the workspace - no chat history or shared context. Include the goal, current state, what to change, what to leave untouched, the project's actual gates, and a report contract. Tell Kimi not to commit. Keep one task per brief. See references/writing-the-brief.md.

2. Dispatch

Use the bundled helper. It wraps Kimi's headless prompt mode, captures the structured event stream, and writes result.json. (<skill-dir> is the installed folder containing this SKILL.md.)

node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --cd /path/to/repo
# choose a configured model alias:       add --model <alias from your kimi config>
# resume the most recent session:        add --resume-last  (delta brief only)
# resume a specific session:             add --session <id> (delta brief only)
# hard time limit (watchdog):            add --timeout 2h  (the 30m default suits short runs; implementation briefs routinely need 1-2h)
# see all options:                       node .../relay.mjs --help

The child process's cwd pins the workspace. Use repeatable --add-dir flags only for extra workspace directories. The relay writes artifacts under the system temp dir by default and never commits. See references/dispatch-and-poll.md.

3. Wait for completion

The helper blocks until Kimi finishes. Run it with the orchestrator's background-command facility, or background it in the shell and poll for result.json. A pre-run usage error exits 2 and writes no result; a missing kimi exits 127 and writes status: "kimi_unavailable".

Trust process state and the working tree over a progress display. Completion means the process exited and result.json exists. Kimi's full report is the finalMessage field in result.json (also printed in full on stdout between the report markers).

4. Review - do not trust the self-report

Treat Kimi's final message and gate claims as claims:

  • Re-run the project's gates yourself.
  • Read the diff against the brief, starting with touchedFiles.
  • Run relevant guard skills if installed.
  • Round-trip migrations and grep for dangling references after removals or renames.

See references/review-and-land.md.

5. Land it

The implementer edits the working tree; the orchestrator commits. Commit only after the gates pass and the diff holds. If rework is needed, send a delta brief with --resume-last or --session <id>, then review again.

Autonomy and permissions

In headless -p mode, Kimi always runs in auto permission mode and never asks for approval. Kimi rejects --prompt combined with --yolo, --auto, or --plan, so the relay passes none of them and offers no --read-only or --full-access option. There is no CLI-enforced read-only mode: inspect touchedFiles and the diff after every run. That diff, not a flag, is the guarantee of what changed.

Authorization model

Delegation is something the human opts into. Once they have ("run this queue", "proceed"), committing verified, gate-passing work is the agreed contract. Two limits remain: surface, don't absorb (report Kimi's design decisions, defensible-but-unasked turns, and non-blocking nitpicks) and stop for scope changes (if correct completion needs going beyond the brief, ask instead of expanding the mandate). See references/review-and-land.md.

References

相似的 Skill

claude-api
anthropics/skills180k

claude-api

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

代码质量与审查

ponytail-review
DietrichGebert/ponytail158k

ponytail-review

Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-review.

代码质量与审查

code-review-and-quality
addyosmani/agent-skills103k

code-review-and-quality

Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch. Use when asked to review a diff or a pull request, even when the diff is pasted inline.

代码质量与审查

documentation-and-adrs
addyosmani/agent-skills103k

documentation-and-adrs

Records decisions and documentation. Use when you need to document an architecture decision (ADR) or the reasoning behind a design choice, when changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.

代码质量与审查

code-simplification
addyosmani/agent-skills103k

code-simplification

Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.

代码质量与审查

understand
Egonex-AI/Understand-Anything86k

understand

Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships

代码质量与审查