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plan-writing
Use when you have a spec or requirements for a multi-step task, before touching code
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Use when you have a spec or requirements for a multi-step task, before touching code
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Scan design/ for backlog/todo artifacts, check if work has been merged into the codebase, and auto-rename completed ones to done-. Use for periodic hygiene, after merging branches, or to audit design artifact status.
Use when writing Dagger pipelines in Python, building containerized CI/CD with the Dagger Python SDK, creating Dagger modules/functions/objects, integrating services (PostgreSQL, Redis) in Dagger, using Dagger LLM/agent features, or running Dagger in GitHub Actions.
Use when creating specifications, implementation plans, or tools for a feature - enforces consistent naming across related artifacts
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use this skill when the user wants to run hotdata CLI commands, query the Hotdata API, list workspaces, list connections, create connections, list tables, manage datasets, execute SQL queries, inspect query run history, search tables, manage indexes, manage sandboxes, manage workspace context and the data model via the context API (`hotdata context`), or interact with the hotdata service. Activate when the user says "run hotdata", "query hotdata", "list workspaces", "list connections", "create a connection", "list tables", "list datasets", "create a dataset", "upload a dataset", "execute a query", "search a table", "list indexes", "create an index", "list query runs", "list past queries", "query history", "list sandboxes", "create a sandbox", "run a sandbox", "workspace context", "pull context", "push context", "data model", or asks you to use the hotdata CLI.
Use when building, deploying, or debugging applications on Modal (modal.com) — serverless cloud compute for Python. Covers container images, GPU functions, web endpoints, volumes, secrets, scheduling, distributed training, calling deployed functions, and scaling patterns.
| name | plan-writing |
| description | Use when you have a spec or requirements for a multi-step task, before touching code |
Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.
Assume they are a skilled developer, but know almost nothing about our toolset or problem domain. Assume they don't know good test design very well.
Announce at start: "I'm using the plan-writing skill to create the implementation plan."
Context: This should be run in a dedicated worktree (created by brainstorming skill), but the plan artifact itself must be written to the primary checkout's design/ directory.
brainstorming before writing the plan.superpowers: prefixes).done- immediately after saving the plan. This indicates planning is complete for that spec, not that implementation is complete.done- at creation time. Plans stay backlog-/todo- until implementation work is actually completed.Save plans to: <PRIMARY_REPO_ROOT>/design/backlog-YYYYMMDDHHMM-<feature_name>-plan-01.md by default
todo- only when the user explicitly says the artifact is ready for immediate execution.PRIMARY_REPO_ROOT="$(dirname "$(git rev-parse --git-common-dir)")"DESIGN_DIR="$PRIMARY_REPO_ROOT/design"worktrees/*/design/ or .worktrees/*/design/.Plan indexing protocol:
-01 for the first plan artifact-01, create a follow-up plan with the next index (-02, -03, ...)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.
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.
This structure informs the task decomposition. Each task should produce self-contained changes that make sense independently.
Each step is one action (2-5 minutes):
Every plan MUST start with this header:
# [Feature Name] Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use subagent-driven-development (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 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"
```
When a plan involves web research, API exploration, or external content:
Why this matters: The executing-plans skill uses a PreToolUse hook that re-injects plan content into context before every tool call. Untrusted content in the plan gets amplified on each action, creating a prompt injection surface with repeated exposure. Keep external data out of files that hooks auto-read.
If a plan task requires research output as input, reference the findings location rather than inlining the content:
- [ ] **Step 3: Implement parser based on API response format**
See: findings in `tmp/api_exploration.md` for response schema
REQUIRED SUB-SKILL, REQUIRED BACKGROUND)After writing the complete plan:
Review loop guidance:
After saving the plan, offer execution choice:
"Plan complete and saved to <PRIMARY_REPO_ROOT>/design/<filename>-plan-01.md (or next indexed plan if this is a follow-up). Two execution options:
1. Subagent-Driven (recommended) - I dispatch a fresh subagent per 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:
If Inline Execution chosen:
After creating a plan from an existing spec:
done- (same timestamp/objective stem, all matching -spec-*.md files).backlog-/todo- until implementation completes.Example:
backlog-202604130000-example_feature-spec-01.md → done-202604130000-example_feature-spec-01.mdbacklog-202604130000-example_feature-plan-01.md stays backlog-...