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Codex Session Product Reviewer Maintainer

Community
Undertone0809
codex-session-product-reviewer-maintainer

Use when reviewing a local Codex session, task, thread, or commit as a product manager or first-principles reviewer for product correctness, scope, behavior, validation, and whether the task solved the right user problem.

Overview

PublisherUndertone0809
Repositoryrudder
Skill namecodex-session-product-reviewer-maintainer
Stars
290
Forks
31
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Undertone0809 on GitHub. Read the source before you install it.

Installation

Install the Codex Session Product Reviewer Maintainer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Undertone0809/rudder.git /tmp/rudder
mkdir -p .claude/skills
cp -r /tmp/rudder/agent-skills-bak/maintainer/codex-session-product-reviewer-maintainer .claude/skills/codex-session-product-reviewer-maintainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codex Session Product Reviewer Maintainer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codex Session Product Reviewer Maintainer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codex Session Product Reviewer Maintainer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codex Session Product Reviewer Maintainer

This skill reviews a completed or in-progress Codex task from a product manager's point of view. It is a reviewer workflow, not an implementation workflow.

The core question is:

Did this session solve the right product problem, with the right scope, behavior, and validation?

Use it to turn a vague request like "review this Codex session as a PM" into a grounded product review based on the session transcript, actual code changes, tests, plan docs, and repository product standards.

Use When

Use this skill when the user asks to:

  • review a Codex session id, task, thread, or run as a product manager
  • evaluate whether another agent's implementation was product-correct
  • apply first-principles thinking to a shipped task
  • judge whether a proposal or implementation solved the right user problem
  • produce a reviewer-style accept / conditional accept / reject
  • translate fuzzy dissatisfaction into explicit product critique

Common trigger phrases:

  • "review 一下 codex session id ..."
  • "as a 专业的产品经理 review"
  • "第一性原理思考一下"
  • "作为 reviewer"
  • "PM review this task"
  • "这个 session 做得怎么样"
  • "这个实现是不是产品上对"

Do Not Use When

Do not use this skill for:

  • generic code review where the user mainly wants bugs and line comments
  • debugging a failed Rudder agent run transcript
  • creating a new product idea or brainstorming from scratch
  • implementing the fixes found during review, unless the user explicitly asks
  • summarizing a session without judgment
  • judging only from the final assistant message when local evidence is available

If the user asks for code correctness review, use a code-review workflow. If the user asks why a Rudder agent run failed, use the run transcript debugging workflow first.

Inputs

Required:

  • A Codex session id, commit hash, PR, branch, or clearly identified task to review.

Optional:

  • The review lens, such as PM, first principles, design, workflow, release, or founder mode.
  • A desired output style, such as short verdict, detailed memo, or findings only.

Evidence Collection

Never start with opinion. Build the evidence packet first.

  1. Locate the Codex session.

    • Search ~/.codex/session_index.jsonl, ~/.codex/sessions/**/*.jsonl, and ~/.codex/archived_sessions/*.jsonl for the session id or prefix.
    • Confirm the session title, timestamp, cwd, branch, and final assistant message.
    • If the session cannot be found, say that and review only the artifacts the user provided.
  2. Extract the real user request and corrections.

    • Ignore injected AGENTS.md, environment context, skill bodies, and system text.
    • Capture each meaningful user prompt, especially mid-task corrections such as scope changes, product constraints, or "also consider..." feedback.
  3. Extract what the agent actually did.

    • Summarize proposals, plans, implementation steps, changed files, commands, validation results, commits, and pushes.
    • Use git show, git diff, plan docs, tests, and relevant source files when a commit or file path is mentioned.
    • Distinguish committed changes from unrelated dirty worktree changes.
    • Compare the changed-file set with the user's actual request. Flag unrelated files or commits mixed into the task, especially skill renames, dependency/version churn, generated artifacts, release files, or broad runtime changes inside a narrow product/UI fix.
  4. Read the relevant product context.

    • Start with doc/product/GOAL.md, doc/product/PRODUCT.md, doc/product/README.md plus relevant doc/product/domains/**, and doc/engineering/DESIGN.md when the task affects product behavior or UI.
    • Read the task's plan doc if one exists.
    • For release, desktop, plugin, database, or CLI work, follow the relevant AGENTS.md doc route.
  5. Check validation evidence.

    • Record which focused tests, full checks, E2E tests, builds, screenshots, or browser checks actually passed.
    • Treat "attempted but timed out" as unverified, not as passed.
    • For visible UI changes, look for real visual verification of the non-empty and edge-case states, not only empty states.

First-Principles Review Frame

Use this frame before writing the verdict:

  1. User job

    • What real user or operator problem was this task supposed to solve?
    • Was the original request a symptom of a deeper workflow or information architecture issue?
  2. Object model

    • What is the product object really? View, navigation shortcut, workflow state, preference, project artifact, run, issue, or setting?
    • Did the implementation model it as the correct kind of object?
  3. Core loop impact

    • Does the change help Rudder complete real agent-work loops end to end?
    • Does it reduce operator friction, improve control, or clarify agent work?
  4. Scope discipline

    • Did the agent delete or avoid unnecessary surface area?
    • Did it create a new concept when an existing one should have been reused?
    • Did it handle the user's explicit edge cases?
  5. Behavioral completeness

    • Are the important entry points covered?
    • Does the behavior remain correct for empty, normal, long, cross-org, mobile, direct-link, and legacy-link states when relevant?
  6. Trust and validation

    • Did the agent prove the change works in the states users actually care about?
    • Are failed or skipped checks clearly separated from passing checks?

Review Method

Follow this sequence.

  1. State the evidence basis.

    • Mention the session id, title, date, commit hash if present, and files or plan docs inspected.
  2. Give the verdict early.

    • Use one of: accept, conditional accept, reject, or needs more evidence.
    • Add a numeric score only if useful.
  3. Explain the product essence.

    • In one paragraph, say what the task was really about.
    • Translate UI wording into product language. For example, "recent views in main content feels wrong" becomes "navigation history was mis-modeled as a workspace view."
  4. Name what was right.

    • Focus on product decisions, not implementation busywork.
    • Explain why the chosen model fits or does not fit Rudder's operating layer.
  5. Name the gaps.

    • Prioritize product correctness, behavioral gaps, missed edge cases, validation gaps, and scope mistakes.
    • Be concrete: cite files, lines, tests, or session events when possible.
    • Separate "not done" from "not proven."
  6. Recommend the next move.

    • Give one pragmatic follow-up, not a wishlist.
    • Say whether to keep direction, patch behavior, rewrite spec, gather evidence, or reject and reframe.

Output Shape

Default to Chinese when the user asks in Chinese.

Keep the final review compact:

markdown
结论:...

我会给这次任务:7/10,conditional accept。

做得好的地方:
- ...

关键缺口:
1. ...
2. ...

下一步建议:...

For higher-stakes reviews, add:

  • Evidence inspected
  • First-principles diagnosis
  • Product acceptance criteria
  • Residual risks

Do not bury the verdict after a long transcript summary.

Judgment Rules

  • A session can be directionally correct and still not product-done.
  • Passing typecheck/build does not prove product behavior.
  • A failed E2E launch is not a product failure by itself, but it leaves the affected user flow unverified.
  • A visible UI task is not fully reviewed unless at least the relevant rendered states were inspected or explicitly marked unverified.
  • "Too many items" should be reviewed as an experience question, not only as a rendering limit.
  • Compatibility paths should not preserve the old wrong mental model.
  • The best review says which part to keep, which part to patch, and why.

Common Findings To Look For

  • Mis-modeled product object: history treated as a view, settings treated as content, workflow state treated as global preference.
  • Incomplete entry points: only list clicks record behavior, but direct detail, board cards, sidebar links, deep links, or mobile paths do not.
  • Validation mismatch: tests cover the new API but not the actual operator path.
  • Commit hygiene mismatch: the final commit claims to fix one product problem but includes unrelated skill, dependency, generated, or release changes.
  • Edge-case theater: code handles a number limit but not whether that limit is the right product experience.
  • Over-scoped fix: the implementation adds management UI when a small recovery affordance was enough.
  • Under-scoped fix: the implementation moves UI but leaves the underlying behavioral contract inconsistent.

Boundaries

Do not edit code during this review unless the user explicitly asks to fix the findings. If asked to fix, switch from reviewer mode to normal implementation mode and follow repository validation and commit rules.

Do not claim certainty about user satisfaction from logs alone. Phrase such claims as evidence-based judgment, not truth.

Do not quote long chunks of the transcript. Summarize the evidence and cite the local file paths or commit hashes that matter.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Codex Session Product Reviewer Maintainer AI skill do?

Use when reviewing a local Codex session, task, thread, or commit as a product manager or first-principles reviewer for product correctness, scope, behavior, validation, and whether the task solved the right user problem.

Why use Codex Session Product Reviewer Maintainer on TypingMind?

Because you install it once and use it with any model. Codex Session Product Reviewer Maintainer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codex Session Product Reviewer Maintainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Undertone0809/rudder/tree/main/agent-skills-bak/maintainer/codex-session-product-reviewer-maintainer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Codex Session Product Reviewer Maintainer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codex Session Product Reviewer Maintainer?

As many as you like. As long as a model supports skills, you can use Codex Session Product Reviewer Maintainer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codex Session Product Reviewer Maintainer AI skill free?

Yes. It is published on GitHub by Undertone0809 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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