Creator · obra
Last updated · Sep 1, 2026
Use when you have a spec or requirements for a multi-step task, before touching code
Creator · obra
Last updated · Sep 1, 2026
Use when you have a spec or requirements for a multi-step task, before touching code
Creator · obra
Last updated · Sep 1, 2026
Use when you have a spec or requirements for a multi-step task, before touching code
Creator · obra
Last updated · Sep 1, 2026
Use when you have a spec or requirements for a multi-step task, before touching code
Install targets
Codex install prompt
Install the "writing-plans" agent skill from https://github.com/obra/superpowers/tree/main/skills/writing-plans. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when you have a spec or requirements for a multi-step task, before touching code After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"obra-writing-plans","task":"Install writing-plans","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add obra/superpowers --skill writing-plans
Maintenance
fresh
5d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
280K
95/100 Quality · 84/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
280K GitHub stars
Repo activity
280K stars, 25K forks
Maintenance
5d since push
License
MIT
Install
npx skills add obra/superpowers --skill writing-plans
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add obra/superpowers --skill writing-plansDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/obra-writing-plans/install
Agent should check
Copy prompt
Task: Use writing-plans in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/obra-writing-plans/install
Install command: npx skills add obra/superpowers --skill writing-plans
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/obra-writing-plans/install
LLM text format
/api/skills/obra-writing-plans/install?format=text
Find alternatives
/api/skills/search?q=writing-plans&limit=3
Agent prompt
Use writing-plans for this task. Review https://www.openagentskill.com/api/skills/obra-writing-plans/install, then install with: npx skills add obra/superpowers --skill writing-plansRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/obra-writing-plans
LLM text
/api/registry/manifest/obra-writing-plans?format=text
Install alias
/api/registry/install/obra-writing-plans
Recommend
/api/registry/recommend?task=Use%20writing-plans%20in%20an%20agent%20workflow&limit=3
Agent fit
Coding agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Coding agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS280K GitHub stars
Stars/forks activity
PASS280K stars, 25K forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Workflow fit
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code ---
# Writing Plans
## Overview
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 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.
## 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:**
```markdown # [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.]
--- ```
## Task Structure
````markdown ### 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: 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 engineer 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 engineer 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
## 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. 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.
## Execution Handoff
After saving the plan, offer execution choice:
**"Plan complete and saved to `docs/superpowers/plans/<filename>.md`. 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:** - **REQUIRED SUB-SKILL:** Use superpowers:subagent-driven-development - Fresh subagent per task + two-stage review
**If Inline Execution chosen:** - **REQUIRED SUB-SKILL:** Use superpowers:executing-plans - Batch execution with checkpoints for review
Source provenance
Decision snapshot
280,439 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for writing-plans, ready for a manual X post.
Before you hand an agent source-backed research, give it a repeatable starting point. writing-plans: Use when you have a spec or requirements for a multi-step task, before touching code 280.4K stars https://www.openagentskill.com/skills/obra-writing-plans?ref=x
Listing + install path for writing-plans: https://www.openagentskill.com/skills/obra-writing-plans?ref=x Install: npx skills add obra/superpowers --skill writing-plans
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to obra but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/obra-writing-plans/audit)
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)obra
@obra
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsInstall targets
Codex install prompt
Install the "writing-plans" agent skill from https://github.com/obra/superpowers/tree/main/skills/writing-plans. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when you have a spec or requirements for a multi-step task, before touching code After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"obra-writing-plans","task":"Install writing-plans","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add obra/superpowers --skill writing-plans
Maintenance
fresh
5d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
280K
95/100 Quality · 84/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
280K GitHub stars
Repo activity
280K stars, 25K forks
Maintenance
5d since push
License
MIT
Install
npx skills add obra/superpowers --skill writing-plans
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add obra/superpowers --skill writing-plansDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/obra-writing-plans/install
Agent should check
Copy prompt
Task: Use writing-plans in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/obra-writing-plans/install
Install command: npx skills add obra/superpowers --skill writing-plans
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/obra-writing-plans/install
LLM text format
/api/skills/obra-writing-plans/install?format=text
Find alternatives
/api/skills/search?q=writing-plans&limit=3
Agent prompt
Use writing-plans for this task. Review https://www.openagentskill.com/api/skills/obra-writing-plans/install, then install with: npx skills add obra/superpowers --skill writing-plansRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/obra-writing-plans
LLM text
/api/registry/manifest/obra-writing-plans?format=text
Install alias
/api/registry/install/obra-writing-plans
Recommend
/api/registry/recommend?task=Use%20writing-plans%20in%20an%20agent%20workflow&limit=3
Agent fit
Coding agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Coding agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS280K GitHub stars
Stars/forks activity
PASS280K stars, 25K forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Workflow fit
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code ---
# Writing Plans
## Overview
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 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.
## 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:**
```markdown # [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.]
--- ```
## Task Structure
````markdown ### 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: 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 engineer 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 engineer 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
## 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. 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.
## Execution Handoff
After saving the plan, offer execution choice:
**"Plan complete and saved to `docs/superpowers/plans/<filename>.md`. 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:** - **REQUIRED SUB-SKILL:** Use superpowers:subagent-driven-development - Fresh subagent per task + two-stage review
**If Inline Execution chosen:** - **REQUIRED SUB-SKILL:** Use superpowers:executing-plans - Batch execution with checkpoints for review
Source provenance
Decision snapshot
280,439 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for writing-plans, ready for a manual X post.
Before you hand an agent source-backed research, give it a repeatable starting point. writing-plans: Use when you have a spec or requirements for a multi-step task, before touching code 280.4K stars https://www.openagentskill.com/skills/obra-writing-plans?ref=x
Listing + install path for writing-plans: https://www.openagentskill.com/skills/obra-writing-plans?ref=x Install: npx skills add obra/superpowers --skill writing-plans
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to obra but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/obra-writing-plans/audit)
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)obra
@obra
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsInstall targets
Codex install prompt
Install the "writing-plans" agent skill from https://github.com/obra/superpowers/tree/main/skills/writing-plans. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when you have a spec or requirements for a multi-step task, before touching code After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"obra-writing-plans","task":"Install writing-plans","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add obra/superpowers --skill writing-plans
Maintenance
fresh
5d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
280K
95/100 Quality · 84/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
280K GitHub stars
Repo activity
280K stars, 25K forks
Maintenance
5d since push
License
MIT
Install
npx skills add obra/superpowers --skill writing-plans
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add obra/superpowers --skill writing-plansDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/obra-writing-plans/install
Agent should check
Copy prompt
Task: Use writing-plans in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/obra-writing-plans/install
Install command: npx skills add obra/superpowers --skill writing-plans
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/obra-writing-plans/install
LLM text format
/api/skills/obra-writing-plans/install?format=text
Find alternatives
/api/skills/search?q=writing-plans&limit=3
Agent prompt
Use writing-plans for this task. Review https://www.openagentskill.com/api/skills/obra-writing-plans/install, then install with: npx skills add obra/superpowers --skill writing-plansRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/obra-writing-plans
LLM text
/api/registry/manifest/obra-writing-plans?format=text
Install alias
/api/registry/install/obra-writing-plans
Recommend
/api/registry/recommend?task=Use%20writing-plans%20in%20an%20agent%20workflow&limit=3
Agent fit
Coding agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Coding agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS280K GitHub stars
Stars/forks activity
PASS280K stars, 25K forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Workflow fit
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code ---
# Writing Plans
## Overview
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 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.
## 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:**
```markdown # [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.]
--- ```
## Task Structure
````markdown ### 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: 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 engineer 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 engineer 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
## 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. 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.
## Execution Handoff
After saving the plan, offer execution choice:
**"Plan complete and saved to `docs/superpowers/plans/<filename>.md`. 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:** - **REQUIRED SUB-SKILL:** Use superpowers:subagent-driven-development - Fresh subagent per task + two-stage review
**If Inline Execution chosen:** - **REQUIRED SUB-SKILL:** Use superpowers:executing-plans - Batch execution with checkpoints for review
Source provenance
Decision snapshot
280,439 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for writing-plans, ready for a manual X post.
Before you hand an agent source-backed research, give it a repeatable starting point. writing-plans: Use when you have a spec or requirements for a multi-step task, before touching code 280.4K stars https://www.openagentskill.com/skills/obra-writing-plans?ref=x
Listing + install path for writing-plans: https://www.openagentskill.com/skills/obra-writing-plans?ref=x Install: npx skills add obra/superpowers --skill writing-plans
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to obra but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/obra-writing-plans/audit)
[](https://www.openagentskill.com/skills/obra-writing-plans?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)obra
@obra
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsInstall targets
Codex install prompt
Install the "writing-plans" agent skill from https://github.com/obra/superpowers/tree/main/skills/writing-plans. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use when you have a spec or requirements for a multi-step task, before touching code After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"obra-writing-plans","task":"Install writing-plans","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add obra/superpowers --skill writing-plans
Maintenance
fresh
5d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
280K
95/100 Quality · 84/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Financial research output is not financial advice; require human review before any live investment decision.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
280K GitHub stars
Repo activity
280K stars, 25K forks
Maintenance
5d since push
License
MIT
Install
npx skills add obra/superpowers --skill writing-plans
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add obra/superpowers --skill writing-plansDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/obra-writing-plans/install
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Task: Use writing-plans in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20writing-plans%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/obra-writing-plans/install
Install command: npx skills add obra/superpowers --skill writing-plans
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
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/api/skills/obra-writing-plans/install
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/api/skills/obra-writing-plans/install?format=text
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/api/skills/search?q=writing-plans&limit=3
Agent prompt
Use writing-plans for this task. Review https://www.openagentskill.com/api/skills/obra-writing-plans/install, then install with: npx skills add obra/superpowers --skill writing-plansRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/obra-writing-plans
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/api/registry/manifest/obra-writing-plans?format=text
Install alias
/api/registry/install/obra-writing-plans
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/api/registry/recommend?task=Use%20writing-plans%20in%20an%20agent%20workflow&limit=3
Agent fit
Coding agents
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Platforms
Claude Code
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review first
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Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
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PASS280K GitHub stars
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PASS280K stars, 25K forks; issue activity unavailable in current metadata
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PASS5d since push
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PASSMIT
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High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
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Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: writing-plans description: Use when you have a spec or requirements for a multi-step task, before touching code ---
# Writing Plans
## Overview
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 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.
## 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:**
```markdown # [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.]
--- ```
## Task Structure
````markdown ### 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: 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 engineer 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 engineer 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
## 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. 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.
## Execution Handoff
After saving the plan, offer execution choice:
**"Plan complete and saved to `docs/superpowers/plans/<filename>.md`. 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:** - **REQUIRED SUB-SKILL:** Use superpowers:subagent-driven-development - Fresh subagent per task + two-stage review
**If Inline Execution chosen:** - **REQUIRED SUB-SKILL:** Use superpowers:executing-plans - Batch execution with checkpoints for review
Source provenance
Decision snapshot
280,439 GitHub stars
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No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for writing-plans, ready for a manual X post.
Before you hand an agent source-backed research, give it a repeatable starting point. writing-plans: Use when you have a spec or requirements for a multi-step task, before touching code 280.4K stars https://www.openagentskill.com/skills/obra-writing-plans?ref=x
Listing + install path for writing-plans: https://www.openagentskill.com/skills/obra-writing-plans?ref=x Install: npx skills add obra/superpowers --skill writing-plans
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Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsPermission surface
shell or command execution, filesystem or document access
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Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
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Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
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Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness