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Multi engine support (#7)
* multi-agent core * Fix job.yml path references in deepwork_jobs - Update define step to create job.yml in .deepwork/jobs/[job_name]/ instead of deepwork/[job_name]/ - Update implement step to verify job.yml location instead of copying from work directory - Update job.yml quality criteria to check correct path - Update README.md to use correct command names (deepwork_jobs.define, deepwork_jobs.refine) - Sync changes to all installed files and generated commands * more cleanup * style: auto-format code with ruff * ci: remove auto-format job, add format check to tests (#8) Remove the separate format job that auto-committed formatting fixes, which caused issues with GitHub Actions not re-running after the auto-merge. Instead, add format checks as a step in the tests job that will fail if code is not properly formatted. Co-authored-by: Claude <noreply@anthropic.com> --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com>
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‎.claude/commands/deepwork_jobs.define.md‎

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4. **Concise Summary**: Is the summary under 200 characters and descriptive?
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5. **Rich Description**: Does the description provide enough context for future refinement?
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6. **Valid Schema**: Does the job.yml follow the required schema (name, version, summary, steps)?
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7. **File Created**: Has the job.yml file been created in `deepwork/[job_name]/job.yml`?
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7. **File Created**: Has the job.yml file been created in `.deepwork/jobs/[job_name]/job.yml`?
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If ANY criterion is not met, continue working to address it.
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If ALL criteria are satisfied, include `<promise>QUALITY_COMPLETE</promise>` in your response.
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Only after you have complete understanding, create the `job.yml` file:
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**File Location**: `deepwork/[job_name]/job.yml`
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**File Location**: `.deepwork/jobs/[job_name]/job.yml`
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(Where `[job_name]` is the name of the NEW job you're creating, e.g., `deepwork/competitive_research/job.yml`)
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(Where `[job_name]` is the name of the NEW job you're creating, e.g., `.deepwork/jobs/competitive_research/job.yml`)
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**Format**:
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```yaml
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Claude: Great! Creating the job.yml specification now...
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[Creates deepwork/competitive_research/job.yml with the complete spec]
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[Creates .deepwork/jobs/competitive_research/job.yml with the complete spec]
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✓ Job specification created!
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**File created:**
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- deepwork/competitive_research/job.yml
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- .deepwork/jobs/competitive_research/job.yml
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**Next step:**
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Run `/deepwork_jobs.implement` to generate the instruction files for each step based on this specification.
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### job.yml
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The complete YAML specification file (example shown in Step 4 above).
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The complete YAML specification file (example shown in Step 5 above).
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**Location**: `deepwork/[job_name]/job.yml`
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**Location**: `.deepwork/jobs/[job_name]/job.yml`
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(Where `[job_name]` is the name of the new job being created)
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4. **Concise Summary**: Is the summary under 200 characters and descriptive?
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5. **Rich Description**: Does the description provide enough context for future refinement?
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6. **Valid Schema**: Does the job.yml follow the required schema (name, version, summary, steps)?
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7. **File Created**: Has the job.yml file been created in `deepwork/[job_name]/job.yml`?
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7. **File Created**: Has the job.yml file been created in `.deepwork/jobs/[job_name]/job.yml`?
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If ANY criterion is not met, continue working to address it.
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If ALL criteria are satisfied, include `<promise>QUALITY_COMPLETE</promise>` in your response.

‎.claude/commands/deepwork_jobs.implement.md‎

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### Step 1: Read and Validate the Specification
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1. **Locate the job.yml file**
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- Read `deepwork/[job_name]/job.yml` from the define step (Where `[job_name]` is the name of the new job that was created in the define step)
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- Read `.deepwork/jobs/[job_name]/job.yml` from the define step (Where `[job_name]` is the name of the new job that was created in the define step)
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- Parse the YAML content
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2. **Validate the specification**
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This alignment ensures the AI agent knows exactly what will be validated and can self-check before completing.
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### Step 4: Copy job.yml to Job Directory
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### Step 4: Verify job.yml Location
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Copy the validated `job.yml` from the work directory to `.deepwork/jobs/[job_name]/job.yml`:
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```bash
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cp deepwork/[job_name]/job.yml .deepwork/jobs/[job_name]/job.yml
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```
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Verify that `job.yml` is in the correct location at `.deepwork/jobs/[job_name]/job.yml`. The define step should have created it there. If for some reason it's not there, you may need to create or move it.
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### Step 5: Sync Commands
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‎.deepwork/jobs/deepwork_jobs/job.yml‎

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4. **Concise Summary**: Is the summary under 200 characters and descriptive?
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5. **Rich Description**: Does the description provide enough context for future refinement?
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6. **Valid Schema**: Does the job.yml follow the required schema (name, version, summary, steps)?
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7. **File Created**: Has the job.yml file been created in `deepwork/[job_name]/job.yml`?
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7. **File Created**: Has the job.yml file been created in `.deepwork/jobs/[job_name]/job.yml`?
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If ANY criterion is not met, continue working to address it.
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If ALL criteria are satisfied, include `<promise>QUALITY_COMPLETE</promise>` in your response.

‎.deepwork/jobs/deepwork_jobs/steps/define.md‎

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Only after you have complete understanding, create the `job.yml` file:
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**File Location**: `deepwork/[job_name]/job.yml`
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**File Location**: `.deepwork/jobs/[job_name]/job.yml`
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(Where `[job_name]` is the name of the NEW job you're creating, e.g., `deepwork/competitive_research/job.yml`)
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(Where `[job_name]` is the name of the NEW job you're creating, e.g., `.deepwork/jobs/competitive_research/job.yml`)
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**Format**:
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```yaml
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Claude: Great! Creating the job.yml specification now...
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[Creates deepwork/competitive_research/job.yml with the complete spec]
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[Creates .deepwork/jobs/competitive_research/job.yml with the complete spec]
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✓ Job specification created!
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**File created:**
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- deepwork/competitive_research/job.yml
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- .deepwork/jobs/competitive_research/job.yml
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**Next step:**
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Run `/deepwork_jobs.implement` to generate the instruction files for each step based on this specification.
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### job.yml
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The complete YAML specification file (example shown in Step 4 above).
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The complete YAML specification file (example shown in Step 5 above).
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**Location**: `deepwork/[job_name]/job.yml`
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**Location**: `.deepwork/jobs/[job_name]/job.yml`
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(Where `[job_name]` is the name of the new job being created)
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‎.deepwork/jobs/deepwork_jobs/steps/implement.md‎

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### Step 1: Read and Validate the Specification
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1. **Locate the job.yml file**
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- Read `deepwork/[job_name]/job.yml` from the define step (Where `[job_name]` is the name of the new job that was created in the define step)
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- Read `.deepwork/jobs/[job_name]/job.yml` from the define step (Where `[job_name]` is the name of the new job that was created in the define step)
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- Parse the YAML content
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2. **Validate the specification**
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This alignment ensures the AI agent knows exactly what will be validated and can self-check before completing.
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### Step 4: Copy job.yml to Job Directory
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### Step 4: Verify job.yml Location
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Copy the validated `job.yml` from the work directory to `.deepwork/jobs/[job_name]/job.yml`:
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```bash
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cp deepwork/[job_name]/job.yml .deepwork/jobs/[job_name]/job.yml
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```
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Verify that `job.yml` is in the correct location at `.deepwork/jobs/[job_name]/job.yml`. The define step should have created it there. If for some reason it's not there, you may need to create or move it.
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### Step 5: Sync Commands
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‎.github/workflows/validate.yml‎

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pull_request:
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branches: ["*"]
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permissions:
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contents: write
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jobs:
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format:
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tests:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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with:
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ref: ${{ github.head_ref }}
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token: ${{ secrets.GITHUB_TOKEN }}
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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- name: Install dependencies
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run: uv sync --extra dev
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- name: Run ruff formatting
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run: uv run ruff format src/ tests/
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- name: Run ruff linting with auto-fix
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run: uv run ruff check --fix src/ tests/
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- name: Re-run formatting after auto-fix
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run: uv run ruff format src/ tests/
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- name: Verify all issues are fixed
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- name: Check formatting (ruff)
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run: |
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uv run ruff check src/ tests/
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uv run ruff format --check src/ tests/
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- name: Check for changes
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id: check_changes
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run: |
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if [[ -n "$(git status --porcelain)" ]]; then
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echo "has_changes=true" >> $GITHUB_OUTPUT
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else
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echo "has_changes=false" >> $GITHUB_OUTPUT
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fi
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- name: Commit and push formatting changes
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if: steps.check_changes.outputs.has_changes == 'true'
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run: |
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git config --local user.email "github-actions[bot]@users.noreply.github.com"
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git config --local user.name "github-actions[bot]"
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git add -A
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git commit -m "style: auto-format code with ruff"
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git push
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tests:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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with:
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version: "latest"
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Install dependencies
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run: uv sync --extra dev
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uv run ruff check src/ tests/
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run: uv run pytest tests/ -v

‎README.md‎

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DeepWork is a tool for defining and executing multi-step workflows with AI coding assistants like Claude Code, Google Gemini, and GitHub Copilot. It enables you to decompose complex tasks into manageable steps, with clear inputs, outputs, and dependencies.
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## Status: Phase 1 MVP Complete ✅
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**Version**: 0.1.0
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**Test Coverage**: 166 tests passing
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### What's Implemented
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- ✅ Job definition parsing and validation
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- ✅ Job registry for tracking installed workflows
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- ✅ Multi-platform support (Claude Code, Gemini, Copilot)
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- ✅ Jinja2-based skill file generation
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- ✅ Git integration for work branch management
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- ✅ CLI with `install` command
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- ✅ Core skills: `deepwork.define` and `deepwork.refine`
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## Installation
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### Prerequisites
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This will:
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- Create `.deepwork/` directory structure
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- Initialize job registry
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- Generate core DeepWork skills
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- Generate core DeepWork jobs
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- Install DeepWork jobs for your AI assistant
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- Configure hooks for your AI assistant to enable policies
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## Quick Start
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### 1. Define a Workflow
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Use Claude Code (or your AI assistant) to define a new job:
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### 1. Define a Job
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Jobs are multi-step workflows where each Step has clear input and output artifacts, making them easier to manage effectively.
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The process of defining a job itself is actually a DeepWork job. You can see it at `.deepwork/jobs/deepwork_jobs/`.
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To start the process, just run the first Step in the job:
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```
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/deepwork.define
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/deepwork_jobs.define
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- Name your workflow
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- Name your job
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- Define steps with inputs/outputs
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- Specify dependencies between steps
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It will also prompt you to go on the the next Step in the job.
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### 2. Execute Steps
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Run individual steps of your workflow:
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Run individual steps of your job:
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/deepwork_jobs.refine
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## Example: Competitive Research Workflow
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## Documentation
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- **[Architecture](doc/architecture.md)**: Complete design specification
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- **[Template Review](doc/TEMPLATE_REVIEW.md)**: Skill template documentation
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- **[Status](STATUS.md)**: Implementation progress
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- **[Next Steps](NEXT_STEPS.md)**: Future development roadmap
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│ │ ├── registry.py # Job registry management
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‎claude.md‎

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## Key Files to Reference
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- `doc/architecture.md` - Comprehensive architecture documentation
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- `readme.md` - High-level project overview
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- `README.md` - High-level project overview
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- `shell.nix` - Development environment setup
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## Development Guidelines
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3. **Performance**: Job import completes in <10 seconds
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4. **Extensibility**: New AI platforms can be added in <2 days
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5. **Quality**: 90%+ test coverage, zero critical bugs
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## Questions or Issues?
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- See `doc/architecture.md` for detailed design documentation
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- Check `readme.md` for high-level concepts
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- Reference implementation phases for current work focus

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