ReddRadar
Agent skill
concise-planning
Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.
Install this agent skill to your Project
npx add-skill https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/productivity/concise-planning
SKILL.md
Concise Planning
Goal
Turn a user request into a single, actionable plan with atomic steps.
Workflow
1. Scan Context
- Read
README.md, docs, and relevant code files. - Identify constraints (language, frameworks, tests).
2. Minimal Interaction
- Ask at most 1–2 questions and only if truly blocking.
- Make reasonable assumptions for non-blocking unknowns.
3. Generate Plan
Use the following structure:
- Approach: 1-3 sentences on what and why.
- Scope: Bullet points for "In" and "Out".
- Action Items: A list of 6-10 atomic, ordered tasks (Verb-first).
- Validation: At least one item for testing.
Plan Template
# Plan
<High-level approach>
## Scope
- In:
- Out:
## Action Items
[ ] <Step 1: Discovery>
[ ] <Step 2: Implementation>
[ ] <Step 3: Implementation>
[ ] <Step 4: Validation/Testing>
[ ] <Step 5: Rollout/Commit>
## Open Questions
- <Question 1 (max 3)>
Checklist Guidelines
- Atomic: Each step should be a single logical unit of work.
- Verb-first: "Add...", "Refactor...", "Verify...".
- Concrete: Name specific files or modules when possible.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
davila7/claude-code-templates
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
davila7/claude-code-templates
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
davila7/claude-code-templates
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
davila7/claude-code-templates
Claude Code Guide
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
davila7/claude-code-templates
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
davila7/claude-code-templates
behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
Didn't find tool you were looking for?