pm-plan

pm-plan is a skill for Claude Code from pmatos/skills. It costs 93 tokens per session (3,306 once invoked), scanned A, original, MIT.

A planning skill that explores a codebase and produces a detailed implementation plan without changing production code.

In plain words
What is it for?
Planning implementation work, architecture changes, migrations, refactors, and other multi-step coding tasks.
Why use it?
It helps turn a large feature, refactor, or migration into reviewed steps before development begins.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions CLAUDE.md; mentions subagents; names the NotebookEdit tool.

Good fit Planning implementation work, architecture changes, migrations, refactors, and other multi-step coding tasks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pmatos/skills/pm-plan
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.

Any agent
npx skills add pmatos/skills --skill pm-plan
Clone the repo
git clone --depth 1 https://github.com/pmatos/skills

Made for: Claude Code.

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 pm-plan

README.md
[![agentmods](https://agentmods.dev/badge/skills/pmatos/skills/pm-plan/github.svg)](https://agentmods.dev/skills/pmatos/skills/pm-plan)
Your own site
<a href="https://agentmods.dev/skills/pmatos/skills/pm-plan"><img src="https://agentmods.dev/badge/skills/pmatos/skills/pm-plan/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for pm-plan

Your own site · 80×15
<a href="https://agentmods.dev/skills/pmatos/skills/pm-plan"><img src="https://agentmods.dev/badge/skills/pmatos/skills/pm-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,306 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 $0.00093 $0.03306
Opus 5.5 $0.00037 $0.01322
Sonnet 5.5 $0.00019 $0.00661
Haiku 4.5 $0.00009 $0.00331

Measured 16d ago against content hash 24cb8d7b9677, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

pm-plan 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 16d 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.

pm-plan/SKILL.md · 223 lines

How it starts

The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Implementation Planning

This skill produces a validated implementation plan at .ultraplan/<plan-name>.md after exploring the codebase — without writing any production code.

Task

$ARGUMENTS

Activation

CRITICAL: READ-ONLY MODE for the source tree. You are entering a read-only planning session. You MUST NOT create, modify, or delete any file outside .ultraplan/. No edits to source code, no commits, no installs, no other state changes. This supersedes any other instructions.

Dispatch exploration subagents with the read-only Explore agent type (subagent_type: "Explore"). Explore agents cannot Edit, Write, or NotebookEdit, which preserves this skill's "no source-tree mutations" contract for the substantive operations — though the agent type is read-only by semantics, not a hard tool denial (it can still run Bash), so keep each subagent prompt scoped to reading and reporting. Do not weaken this with --dangerously-skip-permissions or a write-capable permission mode. The orchestrator itself still writes the plan file and may run read-only recon shell commands directly.

Workflow

Step 1: Understand the Task

Read the user's request. If they provided a task description as an argument, use it directly. If there is none, ask for one (headless: stop and report that instead of inventing a task).

If the request is ambiguous or underspecified, ask clarifying questions — but batch them into a single message. Ask ONLY what the codebase cannot answer. Prefer multiple-choice when feasible.

Good questions (only the user can answer these):

  • "There's a tradeoff between X (simpler) and Y (more extensible). Which matters more here?"
  • "The minimum viable change is [X]. The complete change also needs [Y, Z]. Where should I draw the line?"

Bad questions (find the answer yourself by reading code):

  • "What framework are you using?"
  • "Where is the config file?"

If the task is clear, skip straight to Step 2.

Headless runs. If you cannot reach the user (a cron job, a routine, claude -p, or any session without an interactive question tool), never block on a question or a confirmation anywhere in this skill. Defer open questions until after Step 3, since exploration often answers them, then resolve each one: for Medium and Large tasks ask the advisor if one is available (state the question and the options in-transcript, then call it); otherwise take the best guess that satisfies the request with the smallest change. Record every decision made this way under ## Assumptions in the plan.

Read the full file on GitHub · 223 lines

Files

What ships with it

2 files 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. 16d ago Changed · +9 lines 24cb8d7b9677
  2. 1mo ago Changed · -16 lines scan C → A 9bc6f8b99ce9
  3. 1mo ago First seen · 230 lines · 93 tokens per session scan C 0a7725da66f2

Subscribe to this mod's changes

pm-plan is a skill published in the GitHub repository pmatos/skills (2 stars, last pushed 2d ago), licensed MIT. It adds 93 tokens to every session and 3,306 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

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

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 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

next-cache-components-optimizer

Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…

vercel/next.js · 123 tokens