plan-generation

plan-generation is a skill for Claude Code, Codex from abuango/pos-ai. It costs 41 tokens per session (1,084 once invoked), scanned A, original, MIT.

A planning workflow for turning a project request into an implementation plan with technical decisions, ordered tasks, dependencies, and acceptance criteria. It is designed for projects that require approval before implementation begins.

In plain words
What is it for?
Use it to inspect project context and the codebase, design the technical approach, divide the work into assignable tasks, and document the plan for approval.
Why use it?
It makes the work explicit before coding starts, so the team can review the approach, responsibilities, and expected results.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/abuango/pos-ai/plan-generation
Any agent
npx skills add abuango/pos-ai --skill plan-generation
Clone the repo
git clone --depth 1 https://github.com/abuango/pos-ai

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for plan-generation

README.md
[![agentmods](https://agentmods.dev/badge/skills/abuango/pos-ai/plan-generation.svg)](https://agentmods.dev/skills/abuango/pos-ai/plan-generation)
Your own site
<a href="https://agentmods.dev/skills/abuango/pos-ai/plan-generation"><img src="https://agentmods.dev/badge/skills/abuango/pos-ai/plan-generation.svg" alt="Measured on agentmods" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,084 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00041 $0.01084
Opus 5 $0.00020 $0.00542
Sonnet 5 $0.00008 $0.00217
Haiku 4.5 $0.00004 $0.00108

Measured 4d ago against content hash d49e53f7460e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

plan-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/plan-generation/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Generation Skill

You are creating an implementation plan for a POS project. This is a critical document that must be approved before any work begins.

Setup

Before starting: check .handoff/sessions/ for active sessions, read context status.yaml, run git status. Follow .rules/universal.md (Plan -> Approve -> Execute).

Process

  1. Load Project Context

    • Read the project YAML from {teams_dir}/{team}/projects/{project-name}.yaml
    • Understand requirements, constraints, and acceptance criteria
    • Identify the assigned team and available engineers
  2. Analyze the Workspace

    • If workspace path is specified, explore the codebase
    • Understand existing architecture and patterns
    • Identify files that will need modification
  3. Design Technical Approach

    • Determine architecture decisions
    • Choose appropriate technologies/patterns
    • Document key decisions with rationale
  4. Break Down into Tasks

    • Create phases for logical groupings
    • Each task should be:
      • Assignable to a single engineer specialty
      • Completable in a focused work session
      • Have clear inputs and outputs
    • Define dependencies between tasks
    • Assign appropriate engineer type to each task
  5. Identify Risks

    • Technical risks
    • Dependency risks
    • Timeline risks
    • Document mitigations for each
  6. Create Plan Document

    • Save to: {teams_dir}/{team}/projects/{project}/plans/plan-v1.md
    • Update project.yaml status to planning

Available Engineer Specialties

Engineer Use For
eng-backend APIs, services, business logic, databases
eng-frontend UI components, pages, client-side logic
eng-testing Tests, coverage, quality assurance
eng-devops CI/CD, deployment, infrastructure
eng-security Security reviews, vulnerability fixes

Plan Template

# Implementation Plan: {PROJECT_NAME}

**Version:** 1.0
**Created:** {DATE}
**Author:** tpm-{team}
**Status:** PENDING_APPROVAL

---

## Overview

{Brief description of what this project accomplishes}

## Requirements

From project.yaml:
1. {Requirement 1}
2. {Requirement 2}

## Technical Approach

### Architecture
{Describe the architectural approach}

### Key Decisions
1. **{Decision 1}**: {Rationale}
2. **{Decision 2}**: {Rationale}

### Technologies/Patterns
- {Technology/pattern 1}: {Why}

## Task Breakdown

### Phase 1: {Phase Name}
- [ ] **Task 1.1**: {Description}
  - Engineer: eng-{specialty}
  - Files: {files to create/modify}
  - Dependencies: none
  - Estimated complexity: low/medium/high

- [ ] **Task 1.2**: {Description}
  - Engineer: eng-{specialty}
  - Files: {files to create/modify}
  - Dependencies: Task 1.1

### Phase 2: {Phase Name}
...

## Testing Strategy

- Unit tests: {approach}
- Integration tests: {approach}
- Manual verification: {steps}

## Risks & Mitigations

| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| {Risk 1} | Medium | High | {Mitigation} |

## Dependencies

### External
- {External dependency 1}

### Internal
- {Internal dependency}

## Definition of Done

- [ ] All tasks completed
- [ ] Tests passing
- [ ] Code reviewed by TPM
- [ ] Documentation updated
- [ ] Acceptance criteria met

---

## Approval

**Submitted for approval:** {DATE}

**CTO Review:**
- [ ] Technical approach approved
- [ ] Risk assessment adequate
- [ ] Resource allocation appropriate

**Decision:** PENDING

**Notes:**

Read the full file on GitHub · 175 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 175 lines · 41 tokens per session scan A d49e53f7460e

Subscribe to this mod's changes

plan-generation is a skill published in the GitHub repository abuango/pos-ai (2 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,084 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens