Awesome-omni-skill wave-planner

Transform project issues into execution-ready implementation plans with risk prediction, wave-based organization, specialist agents, and TDD workflow

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/backend/wave-planner" ~/.claude/skills/diegosouzapw-awesome-omni-skill-wave-planner && rm -rf "$T"
manifest: skills/backend/wave-planner/SKILL.md
source content

Wave Planner

Transform project issues into execution-ready implementation plans with wave-based organization, specialist agent assignments, token estimates, and TDD workflow integration.


Behavioral Classification

Type: Guided Decision

This skill asks for your input on key decisions, then executes based on your choices.

Decision Points:

  1. Which scope? (P0 only, P0+P1, All issues)
  2. Architecture decisions requiring clarification
  3. Create ADRs for decisions?
  4. Risk mitigation strategies

After decisions are made, artifact generation proceeds automatically.


Quick Start

# Explicit invocation
/wave-planner Phase 3 Live Data

# Or use natural language (proactive trigger)
"Plan the implementation for the security hardening project"
"Break down the API redesign into waves"
"Create an implementation plan for PROJ-100"

What This Skill Does

  1. Checks for existing artifacts (plans, configs, projects) - MANDATORY
  2. Discovers issues from your PM tool (Linear, GitHub, Jira)
  3. Surfaces architecture decisions requiring clarification
  4. Predicts blockers and fail cases, generates mitigations
  5. Organizes issues into waves based on shared context
  6. Estimates tokens using dynamic codebase analysis
  7. Assigns specialist agents via LLM inference
  8. Generates execution-ready artifacts (or updates existing)

Pre-Flight Checks (MANDATORY)

Before wave execution, verify the environment. See preflight.md for the full script.

Quick check:

CheckCommandExpected
MCP Server
grep claude-flow .mcp.json
Found
Docker
docker ps | grep <project-name>
Running
Git-crypt
git-crypt status
Unlocked
Linear
linear issues list --limit 1
Returns issues

Artifact Discovery (MANDATORY)

ALWAYS check for existing artifacts before creating new ones. Skipping discovery causes duplicate plans, wasted effort, and confusion.

Discovery Checklist

# 1. Check for existing implementation plans
ls docs/execution/ | grep -i "<project-name>"

# 2. Check for existing hive-mind configs
ls .claude/hive-mind/ | grep -i "<project-name>"

# 3. Check Linear for existing project
npx tsx ~/.claude/skills/linear/scripts/linear-ops.ts list-initiatives | grep -i "<project-name>"
linear projects list | grep -i "<project-name>"

# 4. Check for related ADRs
ls docs/adr/ | grep -i "<topic>"

When Duplicates Are Found

ScenarioAction
Plan exists, not startedReview and update existing
Plan exists, partially executedContinue from current wave
Plan exists, completedArchive and create new if scope changed
Config exists, not executedUse existing config
Linear project existsReuse, don't create duplicate

Planning Workflow

Phase 1: Discovery

The skill detects your PM tool and fetches project issues:

Found 12 issues in "Phase 3 Live Data":
- 2 P0-Critical
- 4 P1-High
- 6 P2-Medium

Which scope should we plan?
A) P0-Critical only (2 issues)
B) P0 + P1-High (6 issues)
C) All issues (12 issues)

Phase 2: Architecture Decisions

Decisions are surfaced one at a time to reduce cognitive load:

PROJ-101 requires an architecture decision:

**Where should security scan results be stored?**

A) Separate security_scans table
B) JSON column on skills table
C) Dedicated security schema (Recommended)

Should I create an ADR for this decision?

Phase 3: Risk Analysis

The skill analyzes issues for potential blockers and fail cases:

Identified Risks:

1. **External Dependency Risk** (PROJ-101)
   - Issue: Feature relies on third-party API
   - Mitigation: Add fallback or cache layer

2. **Breaking Change Risk** (PROJ-102)
   - Issue: Schema changes may break existing clients
   - Mitigation: Version schema, add migration script

Risk Categories: External Dependency, Breaking Change, Integration, Performance, Security

Phase 4: Wave Organization

Issues are grouped by shared code context to minimize agent context-switching:

Wave 1: Foundation (~81K tokens)
├── PROJ-101: Error handling
├── PROJ-102: Configuration setup
└── Agent: backend-developer

Wave 2: Feature Implementation (~95K tokens)
├── PROJ-103: Core feature
├── PROJ-104: API endpoints
└── Agent: backend-developer

Phase 5: Artifact Generation

ArtifactLocation
Implementation Plan
docs/execution/{project}-implementation-plan.md
ADRs
docs/adr/{number}-{slug}.md
Hive Configs
.claude/hive-mind/{project}-wave-{n}.yaml
PM Sub-IssuesCreated for code review findings

Execution

After planning, execute waves. See execution.md for full details.

Quick start:

# Execute Wave 1
./claude-flow swarm "Execute Wave 1" \
  --config .claude/hive-mind/{project}-wave-1.yaml \
  --strategy development \
  --mode hierarchical

Wave Completion Checklist (from execution.md):

  1. Run code review:
    docker exec <container-name> npm run audit:standards
  2. Fix findings OR create Linear sub-issues
  3. Update Linear issue status to Done
  4. Commit with Co-Authored-By
  5. Create PR if wave is complete

Reference Documentation

For detailed information, see reference.md:

  • Output artifact formats (implementation plan, hive configs, ADRs)
  • Token estimation calculations and budgets
  • Agent assignment logic and triggers
  • TDD workflow (red-green-refactor)
  • PM tool adapters
  • Example planning session

Configuration

# ~/.claude/skills/wave-planner/config.yaml
wave-planner:
  defaults:
    max_waves: 6
    max_issues_per_wave: 5
    token_budget_per_wave: 150000

  adapters:
    preferred: auto

  estimation:
    confidence_threshold: medium
    review_cycle_count: 2

  artifacts:
    create_adrs: true
    create_hive_configs: true
    create_sub_issues: true

Environment

# PM tool credentials (via adapter)
LINEAR_API_KEY=lin_api_xxx
GITHUB_TOKEN=ghp_xxx

Skill Composition

This skill orchestrates:

SkillUsage
linear
PM operations (fetch, update, create)
hive-mind
Generate execution configs
governance
Code review after commits

If a required skill is unavailable, the skill will prompt for installation.


Troubleshooting

"Linear skill not available"

Install the Linear skill to

~/.claude/skills/linear/

"Cannot detect PM tool"

Specify explicitly:

/wave-planner --adapter linear "Project Name"

"Token estimate seems high"

Check confidence level. Low confidence estimates include padding for unknowns. Refine issue descriptions or break into smaller issues.


Related Skills

  • linear - PM operations
  • hive-mind - Swarm execution (local skill, available after installation)
  • governance - Code review

Changelog

v1.4.0 (2026-01-23)

  • Refactor: Decompose into sub-files for progressive disclosure
  • Extract pre-flight checks to preflight.md
  • Extract execution details to execution.md
  • Extract reference documentation to reference.md
  • Add Behavioral Classification section (ADR-025)
  • Main SKILL.md reduced from 785 to ~350 lines

v1.3.0 (2026-01-23)

  • New: Artifact Discovery phase (MANDATORY before creation)
  • Check for existing implementation plans before creating
  • Check for existing hive-mind configs before creating
  • Check Linear for existing projects/issues
  • Discovery workflow with options to review, update, or archive

v1.2.0 (2026-01-21)

  • New: Pre-Flight Checks section
  • New: Automated Code Review Trigger configuration
  • Enhanced: Wave Completion Checklist with Linear enforcement
  • Added "The Deferred Issue Rule" - no deferral without Linear ticket

v1.1.0 (2026-01-21)

  • New: Risk Analysis phase
  • Predict blockers and fail cases before wave organization
  • Auto-generate mitigations for identified risks
  • Risk register added to implementation plan output

v1.0.0 (2026-01-21)

  • Initial release
  • Linear adapter support
  • Dynamic token estimation
  • TDD workflow integration
  • Hive mind config generation