plan-review

A review process for updating a product requirements document after research. A product requirements document is a plan describing the user stories and work needed to build a product.

In plain words
What is it for?
Use it after research to decide whether to modify, split, combine, or reorder stories and to record the reasons for those plan changes.
Why use it?
It checks whether research uncovered missing steps, better approaches, dependency changes, or important edge cases. It prevents interesting findings from becoming unnecessary scope changes.

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/youglin-dev/aha-loop/plan-review
Any agent
npx skills add YougLin-dev/Aha-Loop --skill plan-review
Clone the repo
git clone --depth 1 https://github.com/YougLin-dev/Aha-Loop

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,798 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 $0.00040 $0.01798
Opus 5 $0.00020 $0.00899
Sonnet 5 $0.00008 $0.00360
Haiku 4.5 $0.00004 $0.00180

Measured 2d ago against content hash ba332c33449e, 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 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.

.agents/skills/plan-review/SKILL.md · 299 lines

How it starts

The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Review Skill

Evaluate research findings and decide whether to adjust upcoming stories in the PRD.


The Job

  1. Read the research report for the current story
  2. Evaluate if findings impact the current or future stories
  3. Decide on plan modifications (if any)
  4. Update prd.json with changes
  5. Document all changes in changeLog
  6. Ensure plan remains coherent and achievable

When to Modify the Plan

MODIFY stories when research reveals:

  • A better technical approach than originally planned
  • Missing prerequisite steps
  • Stories that should be split (too large for one context)
  • Stories that can be combined (too small, tightly coupled)
  • Changed dependencies requiring reordering
  • New edge cases requiring additional acceptance criteria

DO NOT modify when:

  • The finding is interesting but doesn't affect implementation
  • Changes would invalidate already-completed stories
  • The modification is scope creep (outside original PRD goals)

Types of Plan Modifications

1. Modify Existing Story

Update acceptance criteria, description, or research topics.

{
  "timestamp": "2026-01-29T12:00:00Z",
  "storyId": "US-002",
  "action": "modified",
  "reason": "Research found that existing badge component supports priority colors, simplifying implementation",
  "changes": {
    "acceptanceCriteria": {
      "removed": ["Create new PriorityBadge component"],
      "added": ["Reuse Badge component with priority color variant"]
    }
  }
}

2. Add New Story

Insert a prerequisite or follow-up story.

{
  "timestamp": "2026-01-29T12:00:00Z",
  "storyId": "US-001.5",
  "action": "added",
  "reason": "Research revealed need for database index on priority column for filter performance",
  "insertAfter": "US-001"
}

3. Split Story

Break a story into smaller pieces.

{
  "timestamp": "2026-01-29T12:00:00Z",
  "storyId": "US-003",
  "action": "split",
  "reason": "Story too large - separating dropdown component from save logic",
  "splitInto": ["US-003a", "US-003b"]
}

Read the full file on GitHub · 299 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. 2d ago First seen · 299 lines · 40 tokens per session scan A ba332c33449e

Subscribe to this mod's changes

plan-review is a skill published in the GitHub repository YougLin-dev/Aha-Loop (181 stars, last pushed 7mo ago), licensed MIT. It adds 40 tokens to every session and 1,798 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 2026-08-30.

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