Writing Plans

SkillMedia

Use when you have a spec or requirements for a multi-step task, before touching code or producing output. Turns a design into bite-sized, copy-pasteable tasks with exact paths and complete code.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Writing Plans skill

What this skill tells your AI

The instructions your AI receives, as published by invergent-ai/surogates in skills/process/writing-plans/SKILL.md and read by ahel’s review.

Overview

Write comprehensive implementation plans assuming the implementer has zero context for the codebase and questionable taste. Document everything they need: which files to touch for each task, complete code, testing commands, docs to check, how to verify. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.

Assume the implementer is a skilled developer but knows almost nothing about the toolset or problem domain. Assume they don't know good test design very well.

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

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

  • Your human partner's 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 output on its own.

File / Artifact 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.

For non-code work, substitute the analogous unit: document sections, dataset partitions, workflow steps. The principle is the same — clear boundaries, single responsibility.

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

Bite-Sized Task Granularity

Each step is one action (2-5 minutes):

  • "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 implementers:** REQUIRED SUB-SKILL: Use `subagent-driven-development` (if available, recommended) or `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]

---

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`

- [ ] **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: Write minimal implementation**

```python
def function(input):
    return expected
```

- [ ] **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"
```

No Placeholders

Every step must contain the actual content an implementer needs. These are plan failures — never write them:

  • "TBD", "TODO", "implement later", "fill in details"
  • "Add appropriate error handling" / "add validation" / "handle edge cases"
  • "Write tests for the above" (without actual test code)
  • "Similar to Task N" (repeat the code — the implementer may be reading tasks out of order)
  • Steps that describe what to do without showing how (code blocks required for code steps)
  • References to types, functions, or methods not defined in any task

Remember

  • Exact file paths always
  • Complete code in every step — if a step changes code, show the code
  • Exact commands with expected output
  • DRY, YAGNI, TDD, frequent commits

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 sub-agent dispatch.

  1. Spec coverage: Skim each section/requirement in the spec. Can you point to a task that implements it? List any gaps.
  2. Placeholder scan: Search your plan for red flags — any of the patterns from the "No Placeholders" section above. Fix them.
  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.

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.

Optional external review: For a high-stakes plan, dispatch a reviewer via delegate_task using the template at references/plan-reviewer-prompt.md.

Execution Handoff

After saving the plan, offer execution choice to your human partner:

"Plan complete and saved to .surogate/plans/<filename>.md. Two execution options:

1. Subagent-Driven (recommended if your platform supports it) — I dispatch a fresh sub-agent per task via delegate_task, review between tasks, fast iteration.

2. Inline Execution — Execute tasks in this session using executing-plans, batch execution with checkpoints.

Which approach?"

If Subagent-Driven chosen:

  • REQUIRED SUB-SKILL: Use subagent-driven-development via skill_view.
  • Fresh sub-agent per task + two-stage review.

If Inline Execution chosen:

  • REQUIRED SUB-SKILL: Use executing-plans via skill_view.
  • Batch execution with checkpoints for review.

Signals

GitHub stars
25
Forks
1
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
writing-plans-invergent-ai
Source
github.com/invergent-ai/surogates