pos-ai: Skill for Claude Code

.claude/skills/plan-generation/SKILL.md

plan-generation is a skill for Claude Code 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 Code

Written for Claude Code: allowed-tools in frontmatter.

This is abuango/pos-ai's own configuration. It tells Claude Code how to work on pos-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything pos-ai configures →

Reuse

Borrowing it

Nothing to install: this file belongs to abuango/pos-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/abuango/pos-ai/main/.claude/skills/plan-generation/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/abuango/pos-ai

Made for: Claude Code.

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README.md
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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. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00041 $0.01084
Opus 5.5 $0.00016 $0.00434
Sonnet 5.5 $0.00008 $0.00217
Haiku 4.5 $0.00004 $0.00108

Measured 2mo ago against content hash d49e53f7460e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 2mo 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. 2mo 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 7mo ago), licensed MIT. It adds 41 tokens to every session and 1,084 once invoked, about $0.0002 per session on Opus 5.5. 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.

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