skills-win/langgraph-plan-review/SKILL.md

name, description

name

description

langgraph-plan-review

Use the LangGraph plan-review workflow from the current Claude Code session. Trigger when the user asks to create Taskxx plans, run the Claude/Codex review loop, generate or revise Notes/Taskxx.md, or use the LangGraph workflow described by Task00.md. This skill uses the current Claude Code environment for Claude steps and only invokes the Python LangGraph app for Codex review and discuss file numbering.

LangGraph Plan Review Workflow

Use this skill to run the LangGraph plan-review workflow. All scripts are bundled under the skill's own scripts\ directory (%USERPROFILE%\.claude\skills\langgraph-plan-review\scripts\) — no dependency on E:\LangGraph_Design\lab00.

运行方式：进程内驱动，无 dev server（2026-07 起）

本 skill 已去掉 langgraph dev 服务器。工作流由进程内 runner scripts\clean_run.py 直接驱动： 它 build_workflow() 编译图、用 SqliteSaver（scripts\.plan_review_state.sqlite）做跨调用持久化、 自己处理 interrupt/Command(resume=...)。不再需要 manage_dev_server.ps1 / run_langgraph.ps1 / run_workflow.py（这些是旧的 dev-server 路径，保留仅作参考，勿再使用）。

• 入口：用 langgraph conda 环境的 python 跑 clean_run.py：

  # CONDA_LANGGRAPH_PYTHON from ~\skills_env.md; resolves either conda layout.
  $PY  = @("$env:USERPROFILE\miniconda3\envs\langgraph\python.exe","$env:USERPROFILE\.conda\envs\langgraph\python.exe") | Where-Object { Test-Path $_ } | Select-Object -First 1
  $RUN = "$env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\clean_run.py"
  # 首次（到 write_plan 中断）：
  & $PY $RUN --task-id 10 --work-dir "<work_dir>" --notes-dir Notes --prompt "<原始请求>"
  # resume（写完/改完 Task10.md 后；resume 值 base64(JSON)）：
  $resume = [Convert]::ToBase64String([Text.Encoding]::UTF8.GetBytes('true'))
  & $PY $RUN --task-id 10 --work-dir "<work_dir>" --notes-dir Notes --resume-b64 $resume --codex-review-command-b64 $CMD --timeout 900
  

  每次调用推进到下一个 interrupt 或结束，打印一行 JSON：{"status":"waiting","action_required":{...}} 或 {"status":"done","has_serious_issue":...}。action_required 的处理同下（write_plan / decide / revise / write_rejection），resume 值：write_plan/revise/write_rejection 用 true；decide 用 {"accepts":true} 或 {"accepts":false,"reason":"..."}。

• codex 评审的两个必备条件（本次踩坑记录）：

  1. codex CLI 要足够新。config.toml 若配了较新模型（如 gpt-5.6-sol），旧 codex 会返回 400 requires a newer version of Codex。升级：npm i -g @openai/codex@latest（≥0.144.5 实测可用）。
  2. codex 子进程要能到 chatgpt.com。clean_run.py 会自动读 Windows 系统代理（HKCU Internet Settings 的 ProxyServer）设进 HTTP(S)_PROXY，让 codex 走代理；也可用 PLAN_REVIEW_PROXY 覆盖。
  3. codex 超时：review_task.py 的内层 codex 超时取 argv[5]（默认 300s，评审常不够）。用 --codex-review-command-b64 传一条末尾带超时秒数的命令（见 Workflow），并把 --timeout 设一致。

• 改了 workflow.py/agents.py/clean_run.py 后：无 dev server 缓存，下次 clean_run.py 直接生效。 若图结构改到与旧 checkpoint 不兼容，删 scripts\.plan_review_state.sqlite 重新开始该 thread。

Pre-flight: Environment Setup

Before running the workflow for the first time (or when unsure the environment is ready), verify that the required packages are installed in the langgraph conda env:

# Install langgraph dev server, SDK, and HTTP client
conda run -n langgraph pip install "langgraph-cli[inmem]" langgraph-sdk httpx

# Verify the SDK is importable
conda run -n langgraph python -c "from langgraph_sdk import get_sync_client; print('SDK OK')"

# Verify the langgraph dev command is available
conda run -n langgraph langgraph --version

Package	Purpose
langgraph-cli[inmem]	Provides the langgraph dev server command
langgraph-sdk	Python client used by run_workflow.py to submit runs
httpx	HTTP health-check in run_workflow.py

Codex auth (if not already set up): %USERPROFILE%\.codex\auth.json must contain a valid ChatGPT OAuth token. Run codex auth login if the file is missing.

Invariants

• Drive the graph only through the dev server (see the mandatory rule above): run_langgraph.ps1 → run_workflow.py → http://127.0.0.1:2024. Never import or run graph.py/workflow.py/agents.py in-process to execute the workflow — that bypasses the dev server and LangGraph Studio cannot track it.
• Use the current Claude Code session for all Claude Code work. Do not launch another claude process.
• Assume the current Claude Code session already has --dangerously-skip-permissions.
• Run Python through the recorded conda environment. run_langgraph.ps1 resolves conda from -CondaExe, CONDA_EXE, conda on PATH, or the user-profile miniconda fallback:

  <resolved conda.exe> run -n langgraph python
  

• Codex CLI is installed in the langgraph conda env as codex.cmd (path = the langgraph env from ~\skills_env.md CONDA_LANGGRAPH_PYTHON, with python.exe → codex.cmd). It must be invoked via cmd /c because .cmd files are not directly executable by Python subprocess on Windows.
• Codex auth uses ChatGPT OAuth tokens stored in %USERPROFILE%\.codex\auth.json. No API key is needed.
• run_langgraph.ps1 sets CODEX_HOME from -CodexHome, existing CODEX_HOME, or <UserProfile>\.codex.
• Always use review_task.py as the Codex review shim. Pass it via --codex-review-command as a JSON array with no spaces. This shim handles all Windows-specific codex invocation requirements: cmd /c, stdin prompt passing, --skip-git-repo-check, session resume, and explicit cwd.
• Keep all workflow files under <Claude launch work_dir>\Notes unless the user explicitly overrides -NotesDir.
• TaskId is the numeric suffix, such as 01; write the plan to Notes\Task01.md.
• discussxx-yy.md numbering is managed by the LangGraph app for Codex review files. If Claude rejects a review comment, create the next numbered discuss file manually before rerunning review.
• The workflow uses LangGraph interrupt() and persists state via the langgraph dev server.
• Each run_langgraph.ps1 invocation advances the graph to the next interrupt or completion. Use -Resume to continue the same thread after completing the requested Claude Code action.

Dev Server Lifecycle ⛔ DEPRECATED

已弃用（2026-07 起）。 本 skill 现用 clean_run.py 进程内驱动，不再需要 dev server。 下面这套 manage_dev_server.ps1 / langgraph dev / Studio 仅作历史参考，正常流程不要用。

The langgraph dev server must be running for the entire duration of a workflow session. Use manage_dev_server.ps1 to start it before the first run_langgraph.ps1 call and stop it after the workflow completes.

# Start (idempotent — safe to call even if already running)
$env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\manage_dev_server.ps1 start

# Stop (call once when workflow returns {"status":"done"})
$env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\manage_dev_server.ps1 stop

# Status check
$env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\manage_dev_server.ps1 status

• start waits up to 45 s for http://127.0.0.1:2024/ok to respond, then prints the Studio URL.
• stop kills the process by PID file and removes it; falls back to killing by port 2024 if the PID is stale.
• If the server is already running, start is a no-op.
• Server stdout/stderr logs are written to %TEMP%\langgraph_dev_server.stdout.log and %TEMP%\langgraph_dev_server.stderr.log for debugging.
• Studio URL: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024 — open in browser to track workflow execution in real time.

Workflow

注意：下列步骤是旧的 dev-server 流程（manage_dev_server.ps1 + run_langgraph.ps1）。 正常请用上方"运行方式：进程内驱动"节的 clean_run.py。以下 action 语义（write_plan / decide / revise / write_rejection 及其 resume 值）两种流程通用，可参考。

1. Determine TaskId from the user. If missing, ask for it.

2. Start the dev server (idempotent):

   $env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\manage_dev_server.ps1 start
   

   Wait for the "[dev-server] Ready" line in the output. Then immediately tell the user in your text response:

   • that the dev server is running, and
   • the Studio URL: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

   Example message to user:

   LangGraph dev server 已就绪。可在浏览器中打开 Studio 实时监控工作流： https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

3. Start or resume the LangGraph app:

   # Build the JSON array with ConvertTo-Json so Windows backslashes are escaped for you.
   # $task_path / $latest_discuss_path / etc. are Python Template placeholders — single-quote them literal.
   $PY = @("$env:USERPROFILE\miniconda3\envs\langgraph\python.exe","$env:USERPROFILE\.conda\envs\langgraph\python.exe") | Where-Object { Test-Path $_ } | Select-Object -First 1
   $review = "$env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\review_task.py"
   $codexCmd = ConvertTo-Json -Compress @($PY,$review,'$task_path','$latest_discuss_path','$codex_session_id','$work_dir')
   & "$env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\run_langgraph.ps1" `
     -TaskId <TaskId> `
     -Prompt "<original user request>" `
     -CodexReviewCommand $codexCmd `
     -Timeout 300
   

4. Inspect the returned JSON. If status is waiting, perform action_required in the current Claude Code session:

   • write_plan: write Task<TaskId>.md with project background, task objective, and concrete implementation plan, then resume with -Resume 'true'.
   • decide: read the discuss file and resume with -Resume '{"accepts":true}' or -Resume '{"accepts":false,"reason":"..."}'.
   • revise: revise Task<TaskId>.md, then resume with -Resume 'true'.
   • write_rejection: write the specified output_path, then resume with -Resume 'true'.

   Why review_task.py: Using the shim keeps all codex-specific logic in Python, and the JSON array stays simple.

   run_langgraph.ps1 base64-encodes the JSON command and resume payload before passing them through conda run, so normal PowerShell single-quoted JSON can be used. Windows paths inside the JSON string must use \\ (double backslash) to produce valid JSON (ConvertTo-Json does this for you), e.g. %USERPROFILE%\\.... run_langgraph.ps1 automatically sets PYTHONIOENCODING=utf-8 so prompts containing Unicode (Chinese, etc.) pass through correctly.

5. Inspect the command output after each resume.

   • Check "status" to determine next action: "waiting" means perform the next action_required; "done" means the workflow is complete.
   • If a new latest_discuss_path is present in the output, read that discuss file for context.

6. Stop when the workflow returns {"status":"done", ...}:

   $env:USERPROFILE\.claude\skills\langgraph-plan-review\scripts\manage_dev_server.ps1 stop
   

Rejection File Rule

When the action_required is write_rejection, the interrupt payload already contains the exact file path:

{"action": "write_rejection", "output_path": "Notes/discuss04-01.md", "reason_hint": "..."}

Write the rejection content directly to action_required.output_path:

# discuss<TaskId>-<yy>

## Claude Code Rejection

<reason>

Notes

• The LangGraph app automatically scans existing discuss files and writes the next Codex review file without overwriting old discussions.
• The app no longer accepts Claude command arguments; Claude actions happen in this current session.
• If codex is unavailable, report that the review step cannot run rather than simulating it.
• review_task.py passes the review prompt through stdin with - to avoid Windows command-line quoting issues with multiline prompts.
• review_task.py passes --skip-git-repo-check so the workflow can run from Claude launch directories that are not git repositories.
• review_task.py reads Codex final output via --output-last-message so intermediate JSONL progress messages are not treated as review findings.
• review_task.py persists the Codex session id through LangGraph state and uses codex exec resume on later review passes.
• review_task.py runs Codex with the original Claude Code launch working directory as cwd.
• Codex review output is normalized to JSON for agents.py: {"has_serious_issue":true/false,"comment":"...","codex_session_id":"..."}.
• If the dev server restarts, run_workflow.py detects that the stored thread no longer exists and creates a new thread automatically.
• run_workflow.py passes the codex command and timeout as config["configurable"] per run — no server restart needed when switching projects.