work-on-issue

作者: huggingface

開始處理 GitHub issue。提取需求,建立工作樹,設置 TDD 工作流程。

npx skills add https://github.com/huggingface/openenv --skill work-on-issue

/work-on-issue

Start focused work on a GitHub issue using TDD workflow.

EXECUTE THESE STEPS NOW

When this skill is invoked, you MUST execute these steps immediately. Do NOT just describe what will happen - actually do it.

Step 1: Parse Issue Number

Extract the issue number from $ARGUMENTS:

  • Remove # prefix if present
  • The issue number is: $ARGUMENTS

Step 2: Spawn Issue Worker Agent

Use the Task tool to spawn the issue-worker agent:

Task tool:
  subagent_type: issue-worker
  prompt: "Read GitHub issue #<NUMBER> and extract:
    1. Goal - what the user wants to achieve
    2. Acceptance criteria - specific testable requirements
    3. Edge cases and constraints
    4. Suggested PR split if complex"

Wait for the agent to return requirements.

Step 3: Create Worktree

After receiving requirements, run this command:

.claude/scripts/worktree-create.sh issue-<NUMBER>-<short-description>

Where <short-description> is 2-3 words from the goal (e.g., add-mcp-tools).

Step 4: Activate TDD Enforcement

Activate TDD enforcement in the new worktree. This uses tdd-state.sh's direct-execution mode so it works in a single Bash call:

cd .worktrees/issue-<NUMBER>-<short-description> && bash .claude/hooks/tdd-state.sh activate <NUMBER>

This writes .tdd-session.json to the worktree root, which all hooks check. Without this step, hooks would not block direct edits.

Step 5: Create Todos

Use TodoWrite to create a todo for EACH acceptance criterion:

TodoWrite:
  todos:
    - content: "Test: <acceptance criterion 1>"
      status: pending
      activeForm: "Testing <criterion 1>"
    - content: "Test: <acceptance criterion 2>"
      status: pending
      activeForm: "Testing <criterion 2>"
    ...

Step 6: Begin TDD Cycle

Immediately invoke /write-tests for the first todo.

DO NOT stop and wait for user input. Start the TDD cycle now.


When to Use

  • Starting work on a GitHub issue
  • You want TDD enforcement (opt-in via this skill)
  • You want isolated work (no branch switching)

When NOT to Use

  • Quick exploration (just stay in main repo)
  • Already in a worktree for this issue
  • Issue doesn't exist in GitHub

Workflow Overview

/work-on-issue #42
    ↓
Step 1: Parse "42" from arguments
    ↓
Step 2: Spawn issue-worker → get requirements
    ↓
Step 3: Create worktree issue-42-<name>
    ↓
Step 4: Activate TDD enforcement (.tdd-session.json)
    ↓
Step 5: Create todos from acceptance criteria
    ↓
Step 6: Invoke /write-tests → begin TDD cycle

Important

This skill runs in the MAIN conversation context (not forked) because it needs to:

  1. Spawn the issue-worker agent and receive its results
  2. Run worktree-create.sh script
  3. Create todos that persist in the conversation
  4. Invoke /write-tests to continue the workflow

The issue-worker agent runs in a forked context and returns requirements.

來自 huggingface 的更多技能

sync-models
huggingface
將 chat-ui 的模型配置與 HuggingFace 路由器同步——為新模型新增描述、標記具備推理能力的模型、為 32B 以上的模型啟用 artifacts…
custom-blocks
huggingface
Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable…
self-review
huggingface
Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against…
hf-cloud-sagemaker-production-defaults
huggingface
建立一個啟用自動擴展、CloudWatch 警報和標籤功能的 SageMaker 端點(即時或非同步)。每當即將建立…時,請使用此技能。
hf-cloud-serving-image-selection
huggingface
為 SageMaker 模型部署選擇合適的服務容器,並找出其目前的映像 URI。每當要將模型部署到…時,請使用此技能。
Hugging Face Cli
huggingface
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
Hugging Face Datasets
huggingface
在 Hugging Face Hub 上建立與管理資料集。支援初始化儲存庫、定義配置/系統提示、串流更新資料列,以及基於 SQL 的資料集查詢/轉換。設計與 HF MCP 伺服器搭配使用,以實現完整的資料集工作流程。
Hugging Face Evaluation
huggingface
在 Hugging Face 模型卡中新增與管理評估結果。支援從 README 內容中提取評估表格、從 Artificial Analysis API 匯入分數,以及使用 vLLM/lighteval 執行自訂模型評估。可搭配 model-index 中繼資料格式使用。