workflow-patterns

wshobson/agents · updated Apr 8, 2026

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$npx skills add https://github.com/wshobson/agents --skill workflow-patterns
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skill.md

Workflow Patterns

Guide for implementing tasks using Conductor's TDD workflow, managing phase checkpoints, handling git commits, and executing the verification protocol that ensures quality throughout implementation.

When to Use This Skill

  • Implementing tasks from a track's plan.md
  • Following TDD red-green-refactor cycle
  • Completing phase checkpoints
  • Managing git commits and notes
  • Understanding quality assurance gates
  • Handling verification protocols
  • Recording progress in plan files

TDD Task Lifecycle

Follow these 11 steps for each task:

Step 1: Select Next Task

Read plan.md and identify the next pending [ ] task. Select tasks in order within the current phase. Do not skip ahead to later phases.

Step 2: Mark as In Progress

Update plan.md to mark the task as [~]:

- [~] **Task 2.1**: Implement user validation

Commit this status change separately from implementation.

Step 3: RED - Write Failing Tests

Write tests that define the expected behavior before writing implementation:

  • Create test file if needed
  • Write test cases covering happy path
  • Write test cases covering edge cases
  • Write test cases covering error conditions
  • Run tests - they should FAIL

Example:

def test_validate_user_email_valid():
    user = User(email="[email protected]")
    assert user.validate_email() is True

def test_validate_user_email_invalid():
    user = User(email="invalid")
    assert user.validate_email() is False

Step 4: GREEN - Implement Minimum Code

Write the minimum code necessary to make tests pass:

  • Focus on making tests green, not perfection
  • Avoid premature optimization
  • Keep implementation simple
  • Run tests - they should PASS

Step 5: REFACTOR - Improve Clarity

With green tests, improve the code:

  • Extract common patterns
  • Improve naming
  • Remove duplication
  • Simplify logic
  • Run tests after each change - they should remain GREEN

Step 6: Verify Coverage

Check test coverage meets the 80% target:

pytest --cov=module --cov-report=term-missing

If coverage is below 80%:

  • Identify uncovered lines
  • Add tests for missing paths
  • Re-run coverage check

Step 7: Document Deviations

If implementation deviated from plan or introduced new dependencies:

  • Update tech-stack.md with new dependencies
  • Note deviations in plan.md task comments
  • Update spec.md if requirements changed

Step 8: Commit Implementation

Create a focused commit for the task:

git add -A
git commit -m "feat(user): implement email validation

- Add validate_email method to User class
- Handle empty and malformed emails
- Add comprehensive test coverage

Task: 2.1
Track: user-auth_20250115"

Commit message format:

  • Type: feat, fix, refactor, test, docs, chore
  • Scope: affected module or component
  • Summary: imperative, present tense
  • Body: bullet points of changes
  • Footer: task and track references

Step 9: Attach Git Notes

Add rich task summary as git note:

git notes add -m "Task 2.1: Implement user validation

Summary:
- Added email validation using regex pattern
- Handles edge cases: empty, no @, no domain
- Coverage: 94% on validation module

Files changed:
- src/models/user.py (modified)
- tests/test_user.py (modified)

Decisions:
- Used simple regex over email-validator library
- Reason: No external dependency for basic validation"

Step 10: Update Plan with SHA

Update plan.md to mark task complete with commit SHA:

- [x] **Task 2.1**: Implement user validation `abc1234`

Step 11: Commit Plan Update

Commit the plan status update:

git add conductor/tracks/*/plan.md
git commit -m "docs: update plan - task 2.1 complete

Track: user-auth_20250115"

Phase Completion Protocol

When all tasks in a phase are complete, execute the verification protocol:

Identify Changed Files

List all files modified since the last checkpoint:

git diff --name-only <last-checkpoint-sha>..HEAD

Ensure Test Coverage

For each modified file:

  1. Identify corresponding test file
  2. Verify tests exist for new/changed code
  3. Run coverage for modified modules
  4. Add tests if coverage < 80%

Run Full Test Suite

Execute complete test suite:

pytest -v --tb=short

All tests must pass before proceeding.

Generate Manual Verification Steps

Create checklist of manual verifications:

## Phase 1 Verification Checklist

- [ ] User can register with valid email
- [ ] Invalid email shows appropriate error
- [ ] Database stores user correctly
- [ ] API returns expected response codes

WAIT for User Approval

Present verification checklist to user:

Phase 1 complete. Please verify:
1. [ ] Test suite passes (automated)
2. [ ] Coverage meets target (automated)
3. [ ] Manual verification items (requires human)

Respond with 'approved' to continue, or note issues.

Do NOT proceed without explicit approval.

Create Checkpoint Commit

After approval, create checkpoint commit:

git add -A
git commit -m "checkpoint: phase 1 complete - user-auth_20250115

Verified:
- All tests passing
- Coverage: 87%
- Manual verification approved

Phase 1 tasks:
- [x] Task 1.1: Setup database schema
- [x] Task 1.2: Implement user model
- [x] Task 1.3: Add validation logic"

Record Checkpoint SHA

Update plan.md checkpoints table:

## Checkpoints

| Phase   | Checkpoint SHA | Date       | Status   |
| ------- | -------------- | ---------- | -------- |
| Phase 1 | def5678        | 2025-01-15 | verified |
| Phase 2 |                |            | pending  |

Quality Assurance Gates

Before marking any task complete, verify these gates:

Passing Tests

  • All existing tests pass
  • New tests pass
  • No test regressions

Coverage >= 80%

  • New code has 80%+ coverage
  • Overall project coverage maintained
  • Critical paths fully covered

Style Compliance

  • Code follows style guides
  • Linting passes
  • Formatting correct

Documentation

  • Public APIs documented
  • Complex logic explained
  • README updated if needed

Type Safety

  • Type hints present (if applicable)
  • Type checker passes
  • No type: ignore without reason

No Linting Errors

  • Zero linter errors
  • Warnings addressed or justified
  • Static analysis clean

Mobile Compatibility

If applicable:

  • Responsive design verified
  • Touch interactions work
  • Performance acceptable

Security Audit

  • No secrets in code
  • Input validation present
  • Authentication/authorization correct
  • Dependencies vulnerability-free

Git Integration

Commit Message Format

<type>(<scope>): <subject>

<body>

<footer>

Types:

  • feat: New feature
  • fix: Bug fix
  • refactor: Code change without feature/fix
  • test: Adding tests
  • docs: Documentation
  • chore: Maintenance

Git Notes for Rich Summaries

Attach detailed notes to commits:

git notes add -m "<detailed summary>"

View notes:

git log --show-notes

Benefits:

  • Preserves context without cluttering commit message
  • Enables semantic queries across commits
  • Supports track-based operations

SHA Recording in plan.md

Always record the commit SHA when completing tasks:

- [x] **Task 1.1**: Setup schema `abc1234`
- [x] **Task 1.2**: Add model `def5678`

This enables:

  • Traceability from plan to code
  • Semantic revert operations
  • Progress auditing

Verification Checkpoints

Why Checkpoints Matter

Checkpoints create restore points for semantic reversion:

  • Revert to end of any phase
  • Maintain logical code state
  • Enable safe experimentation

When to Create Checkpoints

Create checkpoint after:

  • All phase tasks complete
  • All phase verifications pass
  • User approval received

Checkpoint Commit Content

Include in checkpoint commit:

  • All uncommitted changes
  • Updated plan.md
  • Updated metadata.json
  • Any documentation updates

How to Use Checkpoints

For reverting:

# Revert to end of Phase 1
git revert --no-commit <phase-2-commits>...
git commit -m "revert: rollback to phase 1 checkpoint"

For review:

# See what changed in Phase 2
git diff <phase-1-sha>..<phase-2-sha>

Handling Deviations

During implementation, deviations from the plan may occur. Handle them systematically:

Types of Deviations

Scope Addition Discovered requirement not in original spec.

  • Document in spec.md as new requirement
  • Add tasks to plan.md
  • Note addition in task comments

Scope Reduction Feature deemed unnecessary during implementation.

  • Mark tasks as [-] (skipped) with reason
  • Update spec.md scope section
  • Document decision rationale

Technical Deviation Different implementation approach than planned.

  • Note deviation in task completion comment
  • Update tech-stack.md if dependencies changed
  • Document why original approach was unsuitable

Requirement Change Understanding of requirement changes during work.

  • Update spec.md with corrected requirement
  • Adjust plan.md tasks if needed
  • Re-verify acceptance criteria

Deviation Documentation Format

When completing a task with deviation:

- [x] **Task 2.1**: Implement validation `abc1234`
  - DEVIATION: Used library instead of custom code
  - Reason: Better edge case handling
  - Impact: Added email-validator to dependencies

Error Recovery

Failed Tests After GREEN

If tests fail after reaching GREEN:

  1. Do NOT proceed to REFACTOR
  2. Identify which test started failing
  3. Check if refactoring broke something
  4. Revert to last known GREEN state
  5. Re-approach the implementation

Checkpoint Rejection

If user rejects a checkpoint:

  1. Note rejection reason in plan.md
  2. Create tasks to address issues
  3. Complete remediation tasks
  4. Request checkpoint approval again

Blocked by Dependency

If task cannot proceed:

  1. Mark task as [!] with blocker description
  2. Check if other tasks can proceed
  3. Document expected resolution timeline
  4. Consider creating dependency resolution track

TDD Variations by Task Type

Data Model Tasks

RED: Write test for model creation and validation
GREEN: Implement model class with fields
how to use workflow-patterns

How to use workflow-patterns on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add workflow-patterns
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill workflow-patterns

The skills CLI fetches workflow-patterns from GitHub repository wshobson/agents and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/workflow-patterns

Reload or restart Cursor to activate workflow-patterns. Access the skill through slash commands (e.g., /workflow-patterns) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.541 reviews
  • Pratham Ware· Dec 24, 2024

    workflow-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Alexander Haddad· Dec 20, 2024

    I recommend workflow-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Isabella Chawla· Dec 16, 2024

    Keeps context tight: workflow-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Naina Khan· Dec 12, 2024

    workflow-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aanya Kapoor· Dec 4, 2024

    Registry listing for workflow-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Naina Choi· Nov 23, 2024

    Useful defaults in workflow-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Amina Okafor· Nov 11, 2024

    workflow-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aanya Khanna· Nov 7, 2024

    workflow-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Xiao Jackson· Oct 26, 2024

    Solid pick for teams standardizing on skills: workflow-patterns is focused, and the summary matches what you get after install.

  • Naina Perez· Oct 14, 2024

    I recommend workflow-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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