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Writing Plans

Jesse Vincent (obra)CommunityCreated by Jesse Vincent (obra)

  • Skill
  • Workflow
  • Software development
  • MIT
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From the author

Use when you have a spec or requirements for a multi-step task, before touching code
— the description by Jesse Vincent (obra)

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Writing Plans provides a rigorous framework for translating technical specifications into actionable, test-driven implementation plans. It ensures that complex features are broken down into manageable, independently testable tasks before any code is written. By focusing on clear interfaces, defined file responsibilities, and explicit test-driven steps, this skill helps agents and engineers maintain consistency, reduce ambiguity, and ensure that all requirements are covered by specific, verifiable tasks.

What it does

  • Maps out file structures and component responsibilities based on project requirements.
  • Decomposes complex features into granular, test-driven tasks.
  • Defines clear interfaces and data contracts between tasks.
  • Enforces a standardized plan document format including architecture, tech stack, and global constraints.
  • Provides a self-review checklist to ensure spec coverage and type consistency.
  • Facilitates handoff to execution agents with clear implementation strategies.

When to use it

  • When you have a specification or set of requirements for a multi-step task.
  • Before starting any coding work on a new feature.
  • When you need to ensure that complex, multi-subsystem tasks are broken down into testable units.
  • When you want to minimize ambiguity for the agent or engineer implementing the code.

Advantages

  • Reduces the risk of implementation drift by locking in design decisions early.
  • Ensures every requirement is mapped to a specific, testable task.
  • Promotes clean, modular code through focused file responsibility.
  • Standardizes the documentation of technical plans for better team alignment.
  • Provides a clear path for both subagent-driven and native execution models.

Usage examples

“I have a spec for a new user authentication module. Can you help me plan the implementation?”
The agent will initiate the writing-plans workflow, creating a structured markdown file in the designated directory that outlines the architecture, tasks, and test steps required for the authentication module.
“We need to integrate a new payment gateway. Use the writing-plans skill to prepare the work.”
The agent will decompose the integration into bite-sized tasks, defining the necessary file changes, interface signatures, and verification steps, ending with a request for the user to select an execution method.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/obra/writing-plans/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip writing-plans.zip -d .agents/skills

Documentation by Jesse Vincent (obra)

Writing Plans

Overview

Write implementation plans for an engineer who has not seen this codebase or this spec. Assume they write idiomatic code in the project's language once they know the exact interface and the exact test, and that they will make a reasonable choice wherever the plan leaves one open. What they cannot know is what you decided: which files, which names and signatures, which values from the spec, which tests prove each task. Document those. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.

Announce at start: "I'm using the writing-plans skill to create the implementation plan."

Context: If working in an isolated worktree, it should have been created via the superpowers:using-git-worktrees skill at execution time.

Save plans to: docs/superpowers/plans/YYYY-MM-DD-<feature-name>.md

  • (User preferences for plan location override this default)

Scope Check

If the spec covers multiple independent subsystems, it should have been broken into sub-project specs during brainstorming. If it wasn't, suggest breaking this into separate plans — one per subsystem. Each plan should produce working, testable software on its own.

File Structure

Before defining tasks, map out which files will be created or modified and what each one is responsible for. This is where decomposition decisions get locked in.

  • Design units with clear boundaries and well-defined interfaces. Each file should have one clear responsibility.

  • You reason best about code you can hold in context at once, and your edits are more reliable when files are focused. Prefer smaller, focused files over large ones that do too much.

  • Files that change together should live together. Split by responsibility, not by technical layer.

  • In existing codebases, follow established patterns. If the codebase uses large files, don't unilaterally restructure - but if a file you're modifying has grown unwieldy, including a split in the plan is reasonable.

This structure informs the task decomposition. Each task should produce self-contained changes that make sense independently.

Task Right-Sizing

A task is the smallest unit that carries its own test cycle and is worth a fresh reviewer's gate. When drawing task boundaries: fold setup, configuration, scaffolding, and documentation steps into the task whose deliverable needs them; split only where a reviewer could meaningfully reject one task while approving its neighbor. Each task ends with an independently testable deliverable.

Step Granularity

Each step is one action with a checkable result:

  • "Write the failing test" - step

  • "Run it to make sure it fails" - step

  • "Implement the minimal code to make the test pass" - step

  • "Run the tests and make sure they pass" - step

  • "Commit" - step

Plan Document Header

Every plan MUST start with this header:

# [Feature Name] Implementation Plan

> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.

**Goal:** [One sentence describing what this builds]

**Architecture:** [2-3 sentences about approach]

**Tech Stack:** [Key technologies/libraries]

**Spec:** [path to the spec/design doc this plan implements — the plan
argues from the spec, so the spec travels with it; executors read both]

## Global Constraints

[The spec's project-wide requirements — version floors, dependency limits,
naming and copy rules, platform requirements — one line each, with exact
values copied verbatim from the spec. Every task's requirements implicitly
include this section.]

## Review Focus

[The five input classes or failure modes the spec implies but no task's
tests exercise that are most likely to bite a person using this software
— one line each, naming the input or condition and the behavior a
reasonable person would expect, most likely first. The spec is a vision
document: it says what the software must do, not everything it will
meet, and its silence on an input is not permission for that input to
break the program. Write the list here, once, with the spec in front of
you. Then, for each line, add the test that pins it to the task that
owns the code, in that task's own step style.]

---

Task Structure

### Task N: [Component Name]

**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py:123-145`
- Test: `tests/exact/path/to/test.py`

**Interfaces:**
- Consumes: [what this task uses from earlier tasks — exact signatures]
- Produces: [what later tasks rely on — exact function names, parameter
  and return types. A task's implementer sees only their own task; this
  block is how they learn the names and types neighboring tasks use.]

- [ ] **Step 1: Write the failing test**

```python
def test_specific_behavior():
    result = function(input)
    assert result == expected
```

- [ ] **Step 2: Run test to verify it fails**

Run: `pytest tests/path/test.py::test_name -v`
Expected: FAIL with "function not defined"

- [ ] **Step 3: Implement `function(input: InputType) -> ResultType` in `exact/path/to/file.py`**

One line on the approach when the signature and the test leave a choice
(which library call, which data structure); a code block only for an
algorithm they do not determine.

- [ ] **Step 4: Run test to verify it passes**

Run: `pytest tests/path/test.py::test_name -v`
Expected: PASS

- [ ] **Step 5: Commit**

```bash
git add tests/path/test.py src/path/file.py
git commit -m "feat: add specific feature"
```

What a Step Contains

A step is done when the implementer can write exactly one reasonable thing from it. That is the whole requirement: unambiguous, not complete. Each kind of step carries what makes it unambiguous and nothing more:

  • A test step: the test's name and its assertions, as code, with the spec's exact values in them.

  • A code step: the exact signature (name, parameters, return type), the file it lives in, and the specific values the spec pins. The implementer writes the body. A body appears only for an algorithm the signature and tests do not determine, or for exact copy the spec fixes.

  • A verification step: the command to run and the output that means it passed.

  • A reference to another task: that task's Interfaces block says what to use; the plan does not repeat that task's code.

A plan is the set of decisions the implementer cannot make alone. A plan longer than the code it describes has written the code instead. Lines that decide nothing ("TBD", "handle edge cases", "add appropriate validation", "write tests for the above", a type or function no task defines) are the opposite failure, and the self-review catches both.

Self-Review

After writing the complete plan, look at the spec with fresh eyes and check the plan against it. This is a checklist you run yourself — not a subagent dispatch.

1. Spec coverage: Skim each section/requirement in the spec. Can you point to a task that implements it? List any gaps.

2. Step scan: Every step must let the implementer write exactly one reasonable thing, and no step may carry more than that: a line that decides nothing is a gap, a function body the signature and tests already determine is a transcript. Fix both.

3. Type consistency: Do the types, method signatures, and property names you used in later tasks match what you defined in earlier tasks? A function called clearLayers() in Task 3 but clearFullLayers() in Task 7 is a bug.

4. Review Focus: For each input class or failure mode the spec implies, is there a task whose tests exercise it? The five uncovered ones most likely to bite a person go in the Review Focus section, and each line there gets its test added to the owning task. An empty section means you checked and found none, not that you skipped the check.

5. Proportion: Compare the plan's length to the spec's. A plan several times longer than the spec it implements is a transcript of the program, not a plan. If code blocks are most of the document, replace bodies with signatures, test names and assertions, and check that each step is still unambiguous.

If you find issues, fix them inline. No need to re-review — just fix and move on. If you find a spec requirement with no task, add the task.

Execution Handoff

After saving and self-reviewing the plan, link it for your human partner to read. If they have already explicitly supplied an execution method, ask them to review the plan and confirm it captures what they want; wait for that review before implementation, then use the preserved method. Otherwise, ask them to review the plan and choose an execution method before implementation.

When no execution method has already been supplied:

"Plan complete and saved to docs/superpowers/plans/<filename>.md. Please review the plan. Which execution approach would you prefer?

  • Subagent-driven - A fresh subagent implements each task and a fresh reviewer checks it before the next one starts, then a whole-branch review at the end. Most thorough; costs a fresh context per task and per review.

  • Native - I implement every task myself in this session, the way this harness runs work, then one fresh reviewer on the most capable model checks the whole branch. Cheapest and fastest; no independent review until the end. Runs well with a mid-tier session model, since the plan carries the design.

For this plan I recommend <one of the two>, because <one sentence from the plan: how much the tasks depend on each other's interfaces, how many there are, what a shipped mistake would cost>. Does the plan capture what you want, and which approach should we use?"

When an execution method has already been supplied:

"Plan complete and saved to docs/superpowers/plans/<filename>.md. Please review the plan. Does it capture what you want?"

If Subagent-driven chosen:

  • REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development

If Native chosen:

  • REQUIRED SUB-SKILL: Use superpowers:executing-plans

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Python

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Jesse Vincent (obra)
Publisher
Jesse Vincent (obra) (Community)
Source
obra/superpowers · skills/writing-plans · commit 22f0b46
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Jesse Vincent (obra) or Jesse Vincent (obra).

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download writing-plans.zip