跳到正文
FunCoding

搜索

搜索文档、Skill 和 MCP

cline-delegate

Delegate a coding task to the Cline coding agent CLI (`cline`) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to delegate implementation work to Cline - phrasings like "have Cline implement X", "delegate this to cline", "run it through Cline", or "use cline to implement/fix/refactor" - or wants to run a queue of coding tasks through Cline 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/cline-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/cline-delegate/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Cline Delegate

You are the orchestrator. Delegate a bounded coding task to a separate implementer - the Cline coding agent CLI - then review what it produced and land it yourself. You write the brief and own the judgment; the implementer makes changes in its own session in a clean working tree; 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 cline CLI is not installed or authenticated.
  • You require the relay to configure a sandbox. Cline exposes sandbox controls, but this relay leaves them to the CLI environment; use --plan when the run must be read-only.

Prerequisites (check once)

  1. Install cline (npm or bundled binary; the relay probes cline --version).
  2. Authenticate: run cline auth (interactive sign-in), or configure ANTHROPIC_API_KEY / an OpenAI-compatible base URL.
  3. Confirm cline --version succeeds.
  4. Work in, or point --cd at, the target git repository.

Choose the model (optional)

Cline picks a default model. To choose another, pass the separate --model <id> or --provider <name> (e.g. anthropic, openai-native, openrouter). The relay accepts letters, digits, and . _ : / - only (the value reaches a shell on Windows).

The loop

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

1. Write a brief

Cline sees only the text you send. It cannot read your conversation: the brief must stand alone with the goal, current state, what to change, what to leave untouched, the project's real gates, and a report contract. Keep each brief to a single task. Write it to a file and pass it as the relay's --brief. See references/writing-the-brief.md.

2. Dispatch

Use the bundled relay. It runs cline --json -v, streams the brief on stdin behind a fixed positional instruction, captures the JSON event stream, and writes result.json.

node "<skill-dir>/scripts/relay.mjs" --brief brief.txt --cd /path/to/repo
# choose a model / provider:        add --model <id>  --provider <name>
# read-only planning pass:          add --plan   (forces --auto-approve false)
# deny approval-required tools:     add --auto-approve false
# hard time limit (watchdog):        add --timeout 2h   (the 30m default suits brief runs; most implementation briefs should be 1-2h)
# see all options:                   node .../relay.mjs --help

The child's cwd pins the workspace. 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 relay blocks until cline 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 cline exits 127 and writes status: "cline_unavailable".

Completion means the process exited and result.json exists - trust process state and the working tree, not the progress display. Cline's final message is the finalMessage field of result.json.

4. Review - do not trust the self-report

  • Re-run the project's gates yourself.
  • Read the diff against the brief, starting with touchedFiles.
  • Run relevant guard skills if installed.

See references/review-and-land.md.

5. Land it

If the work is good, commit it. The relay never commits - the diff and result.json are the record; run git status and git diff first to confirm exactly what changed. If the group has a PR flow, make the commit and push a branch; let human review happen. If the diff is wrong or incomplete, re-dispatch a corrected brief in a fresh run and review again.

Autonomy and permissions

The relay explicitly passes Cline's --auto-approve, defaulting to true in act mode. Cline plan mode can request a switch to act mode, so --plan forces --auto-approve false; the relay rejects --plan --auto-approve true. That pair is the read-only gate. Cline also exposes sandbox through --data-dir / CLINE_SANDBOX, but the relay does not configure or override it. Plan-first for anything risky, then review the plan before a separate act-mode dispatch. Malformed or malicious briefs remain dangerous in act mode because commands run as the current user.

Authorization model

Delegation is something the human opts into. Once briefed, cline works as a tool you approved use of. The boundary is: do not accept conclusions from the self-report; verify everything on disk. For anything touching credentials, production data, or irreversible operations, stop and ask the human first instead of encoding it in a brief.

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