cfn-task-planning
Classify tasks, initialize structured configs with scope boundaries, decompose complex tasks
14 stars
Best use case
cfn-task-planning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Classify tasks, initialize structured configs with scope boundaries, decompose complex tasks
Teams using cfn-task-planning should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/cfn-task-planning/SKILL.md --create-dirs "https://raw.githubusercontent.com/masharratt/claude-flow-novice/main/.claude/skills/cfn-task-planning/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cfn-task-planning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cfn-task-planning Compares
| Feature / Agent | cfn-task-planning | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Classify tasks, initialize structured configs with scope boundaries, decompose complex tasks
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
## What it does
Three-phase planning: (1) classify by type/complexity → agent specializations, (2) initialize config with scope boundaries, deliverables, acceptance criteria, (3) decompose large tasks into subtasks within tool budgets.
## When to use (4 triggers)
1. Starting CFN Loop Task Mode → Generate scope config before spawning
2. Analyzing complexity → Select right agents and iteration thresholds
3. Breaking down epics → Sequential subtasks when one agent can't complete
4. Scope contracts → Establish in/out-of-scope before implementation
## When NOT to use (4 anti-patterns)
1. Well-defined and scoped → Go straight to spawning
2. Real-time classification during execution → Planning phase only
3. CLI mode with Redis → CLI stores in Redis, this is for Task Mode configs
4. Simple single-step → Skip for trivial changes
## How to use
Step 1 Classify: `./classify-task.sh "Create REST API..." --format=json`
Step 2 Init: `./init-config.sh --task-id cfn-phase-123 --task-description "..." --mode standard`
Step 3 Decompose: `./decompose-task.sh --task-id ... --description "..." --complexity high`
## Parameters
- **classify**: TASK_DESCRIPTION, --format (json/simple)
- **init**: --task-id, --task-description, --mode (mvp/standard/enterprise)
- **decompose**: --task-id, --description, --tool-budget, --complexity
## Expected output
- **Classify**: `{task_type, complexity, keywords_matched, suggested_agents}`
- **Init**: `.cfn/task-configs/task-{id}.json` with scope, agents, thresholds, acceptance criteria
- **Decompose**: JSON array of subtasks with deliverables, tool_budget, estimated_effort
## Real-world example
"Add JWT auth" → classify backend → init config with deliverables `[src/auth/jwt.ts, tests/]` + acceptance criteria → spawn agents with known scopeRelated Skills
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