plan-review

plan-review is a skill for Claude Code, Codex from fmind/dot. It costs 43 tokens per session (1,027 once invoked), scanned A, original, MIT.

A critical review of a product, software architecture, migration, or launch plan before work begins. It examines assumptions, dependencies, risks, failure cases, and how success will be proven.

In plain words
What is it for?
Use it for pre-mortems, scope checks, architecture reviews, migration planning, delivery planning, security analysis, and launch risk assessment.
Why use it?
It helps find gaps and unsafe assumptions while changes to the plan are still relatively easy.

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

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-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/fmind/dot/plan-review.svg)](https://agentmods.dev/skills/fmind/dot/plan-review)
Your own site
<a href="https://agentmods.dev/skills/fmind/dot/plan-review"><img src="https://agentmods.dev/badge/skills/fmind/dot/plan-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,027 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 $0.00043 $0.01027
Opus 5 $0.00022 $0.00513
Sonnet 5 $0.00009 $0.00205
Haiku 4.5 $0.00004 $0.00103

Measured yesterday against content hash 3f7623d66db5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

plan-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 yesterday.

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-review/SKILL.md · 46 lines

How it starts

The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Review

Attack a plan's load-bearing assumptions while course correction is still cheap; implementation-plan writes plans and diff-review reviews code.

Workflow

  1. Reconstruct intent: Read the full plan, its source requirements, and repository reality, never a summary; state the desired outcome, non-goals, constraints, evidence, and proof required for completion.
  2. Steelman first: State the strongest case for the intended outcome before criticizing the approach.
  3. Inspect current reality: Verify the source paths, interfaces, dependencies, runtime assumptions, and existing mechanisms the plan replaces or duplicates.
  4. Map claims: Extract the decisions and assumptions the plan depends on; flag any requirement without a task, task without a requirement, or success claim without proof.
  5. Choose lenses: Name which lenses were applied.
    • Engineering: architecture, data flow, interfaces, invariants, failure handling, security, privacy, performance, compatibility, and maintainability.
    • Delivery: dependency order, vertical slices, test seams, migrations, observability, rollout, rollback, operational ownership, and authority boundaries.
    • Founder and product: use the discovery and specification lenses of product-loop.
  6. Challenge the premise: Ask whether the problem is real, the scope is the smallest useful wedge, and a no-build or manual alternative could learn more cheaply.
  7. Attack failure modes: Imagine the plan failed through missing value, integration breakage, data loss, abuse, operational burden, migration, adoption, or rollback; trace concrete chains, not categories.
  8. Test intended versus planned: Compare documented permissions, journeys, data rules, and operational promises with the actual tasks and verification steps.
  9. Rank findings: Score each issue by impact, likelihood, confidence, and cheapness to test; promote only issues that could change the decision or execution order, and prefer five decision-changing findings over a long generic list.
  10. Offer remedies: Give the smallest corrective change, cheapest decisive test, and kill or rollback criterion; present numbered alternatives when a real trade-off remains.
  11. Issue the verdict: Return GO, GO WITH CHANGES, or NO-GO with the minimum conditions for the next state, then report:
    • Load-bearing assumptions, and findings ranked P0P3 (scale in diff-review) with evidence and impact.
    • Missing proof, the cheapest tests and kill criteria, and recommended scope changes.
    • Residual risks and owner decisions, plus a revised critical path only when revisions were requested.

Read the full file on GitHub · 46 lines

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. yesterday First seen · 46 lines · 43 tokens per session scan A 3f7623d66db5

Subscribe to this mod's changes

plan-review is a skill published in the GitHub repository fmind/dot (4 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,027 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 2026-09-03.