plan-ceo-review

A structured review of a software plan that challenges its assumptions and decides whether to expand, keep, or reduce its scope. It offers four review modes for different levels of ambition and strictness.

In plain words
What is it for?
Use it to review product ideas, feature plans, and technical proposals before implementation. It helps compare a large ideal version with a focused, essential version.
Why use it?
It helps expose weak premises, missed risks, and worthwhile improvements before development begins. It also makes scope decisions explicit instead of letting them happen accidentally.

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/mrmps/chomsky-stack/plan-ceo-review
Any agent
npx skills add mrmps/chomsky-stack --skill plan-ceo-review
Clone the repo
git clone --depth 1 https://github.com/mrmps/chomsky-stack

Made for: Claude Code, Codex.

Per session 165 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,576 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 $0.00165 $0.06576
Opus 5 $0.00082 $0.03288
Sonnet 5 $0.00033 $0.01315
Haiku 4.5 $0.00016 $0.00658

Measured 2d ago against content hash b9585052faa2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

plan-ceo-review 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 2d 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.

skills/plan-ceo-review/SKILL.md · 416 lines

How it starts

The opening of the file, as written. The whole thing — 416 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mega Plan Review Mode

Philosophy

You are not here to rubber-stamp this plan. You are here to make it extraordinary, catch every landmine before it explodes, and ensure that when this ships, it ships at the highest possible standard.

But your posture depends on what the user needs:

  • SCOPE EXPANSION: You are building a cathedral. Envision the platonic ideal. Push scope UP. Ask "what would make this 10x better for 2x the effort?" You have permission to dream — and to recommend enthusiastically. But every expansion is the user's decision. Present each scope-expanding idea as an AskUserQuestion. The user opts in or out.
  • SELECTIVE EXPANSION: You are a rigorous reviewer who also has taste. Hold the current scope as your baseline — make it bulletproof. But separately, surface every expansion opportunity you see and present each one individually as an AskUserQuestion so the user can cherry-pick. Neutral recommendation posture — present the opportunity, state effort and risk, let the user decide. Accepted expansions become part of the plan's scope for the remaining sections. Rejected ones go to "NOT in scope."
  • HOLD SCOPE: You are a rigorous reviewer. The plan's scope is accepted. Your job is to make it bulletproof — catch every failure mode, test every edge case, ensure observability, map every error path. Do not silently reduce OR expand.
  • SCOPE REDUCTION: You are a surgeon. Find the minimum viable version that achieves the core outcome. Cut everything else. Be ruthless.
  • COMPLETENESS IS CHEAP: AI coding compresses implementation time 10-100x. When evaluating "approach A (full, ~150 LOC) vs approach B (90%, ~80 LOC)" — prefer A. The 70-line delta costs seconds. "Ship the shortcut" is legacy thinking from when human engineering time was the bottleneck.

Critical rule: In ALL modes, the user is 100% in control. Every scope change is an explicit opt-in via AskUserQuestion — never silently add or remove scope. Once the user selects a mode, COMMIT to it. Do not silently drift toward a different mode. If EXPANSION is selected, do not argue for less work during later sections. If SELECTIVE EXPANSION is selected, surface expansions as individual decisions — do not silently include or exclude them. If REDUCTION is selected, do not sneak scope back in. Raise concerns once in Step 0 — after that, execute the chosen mode faithfully.

Read the full file on GitHub · 416 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. 2d ago First seen · 416 lines · 165 tokens per session scan A b9585052faa2

Subscribe to this mod's changes

plan-ceo-review is a skill published in the GitHub repository mrmps/chomsky-stack (7 stars, last pushed 4d ago), licensed MIT. It adds 165 tokens to every session and 6,576 once invoked, about $0.0008 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.

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