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- plan decompose orchestrate
plan decompose orchestrate
Transform flat plans into orchestrated task graphs with mandatory test-driven implementation and root-cause debugging.
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plan decompose orchestrate
Example session with this skill installed
I need to add a Redis cache and an API rate-limiter to our backend. วางแผนก่อน.
- Read your context and instructions
- Compiled the plan decompose orchestrate
I will decompose this into a task graph with an orchestration shape of
Parallel for the initial implementations, as the cache and rate-limiter reside in different modules.
Orchestration Plan: API Enhancements
| Task | Implement | Test | Review |
|---|---|---|---|
| A. Redis Cache | Add RedisCache decorator to GET /data | Verify cache hit/miss latency and TTL expiration | 1. Spec: Cache hit reduces DB load. 2. Quality: Connection pooling check. |
| B. Rate Limiter | Implement FixedWindow middleware in auth.ts | Simulation of 101 requests/min triggering 429 | 1. Spec: 429 returned on limit. 2. Quality: Memory leak check on keys. |
Orchestration Shape: Parallel
- Item A and B touch different files (
/services/cache.tsvs/middleware/auth.ts). - I will use isolated worktrees to execute these concurrently.
- If any test fails, I will halt and initiate a deep-down root-cause analysis.
Next steps
- Approve the decomposition triad above.
- I will initialize the
RedisCacheimplementation using a TDD approach. - Parallel execution will begin once the test environment is verified.
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About this skill
Stop shipping code that breaks in production because a step was rushed or a dependency was missed. This skill transforms flat to-do lists into high-integrity execution graphs by enforcing a strict implement-test-review triad for every single task. It eliminates the "it works on my machine" syndrome by requiring deterministic reproduction and root-cause analysis for every bug.
What it does
- Plan decomposition breaks every task into an explicit Implement, Test, and Review triad to ensure 100% completion.
- Dependency mapping analyzes task relationships to determine the optimal parallel or sequential execution shape.
- Deep-down debugging follows a 7-step root-cause protocol that prevents patching symptoms and stops recurring bugs.
- Two-pass review validates both functional spec-compliance and underlying code quality for every change.
- Orchestration management identifies when to use git worktrees for isolated parallel development to prevent file clobbering.
How it works
- Trigger the skill by asking to "plan first" or "วางแผนก่อน" before starting any multi-file implementation.
- Decompose the initial requirements into the implementation-test-review triad, mapping each to actual source code.
- Orchestrate the execution by defining which tasks run in parallel and which require sequential pipelines.
- Execute the plan, switching to the root-cause debugging protocol immediately if any test fails.
Frameworks & tools
Works with any language or framework. It integrates with git for worktree isolation and standard testing runners for deterministic validation.
Why this beats prompting it yourself
Standard LLM plans are usually just a list of features to build. This skill forces the agent to define how it will prove the code works and how it will review its own quality before a single line of code is written, drastically reducing technical debt.
Use cases
- Migrating complex logic across 2+ files where state dependencies are high.
- Investigating intermittent or flaky test failures that require a systematic trace.
- Designing new features from scratch using a TDD (Test-Driven Development) workflow.
- Managing multi-agent task distribution where parallel work might conflict.
Known limitations
Not recommended for trivial one-line edits or purely informational queries. Requires access to the codebase to verify signatures before planning.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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