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retro

Review a coding session and identify improvements to the agent’s working environment. Examine file navigation, automated checks, instructions, tool use, and information access, then present proposed changes in order of severity.

OriginalShow the original description

Conduct a retrospective on a coding session.

AI rating90/ 100See how to install

Good for

  • Reviewing a coding session that took too long
  • Finding automated checks for recurring mistakes
  • Simplifying agent instructions
  • Reviewing costly tool calls

Overview

What it does

Review coding session records and identify changes to the agent’s working environment for future sessions. It examines file navigation, automated checks, coding standards, instruction length, tool use, and access to information. Improvement candidates are presented in order of severity. Existing checks that are not working are also treated as candidates for improvement.

When it helps

Use it when an agent spent too long finding the right files or repeated the same kind of mistake. It also helps when AGENTS.md has grown long and its instructions need organizing. The approach distinguishes violations that a machine can detect from standards that require human judgment, so these can be considered separately.

Example requests

  • “Review this coding session and rank possible improvements to the working environment by severity.”
  • “Check whether existing automated checks could have prevented the mistakes in this session.”

Notes

The review requires primary sources, such as logs from the specified session. If no session is specified, it uses the current one. The procedure refers to writing-for-agents. The skill is configured for user invocation, and the documented workflow ends with presenting improvement candidates.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt explicitly defines a coding retrospective focused on environment improvements and lists the areas to inspect.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt specifies reading primary sources, inspecting existing checks, reviewing defined categories, and presenting candidates by severity.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodThe documented steps read logs and configuration and present proposals; they do not include sending data or deleting files.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingIt requires writing-for-agents and session sources, but does not explain how to install the referenced skill.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

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Files included (2)

  • SKILL.md4.3 KB
  • agents/openai.yaml146 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

The user has asked for a retrospective. You are suggesting improvements to the coding agent's environment to improve future runs.

Steps

  1. Call the Skill tool with writing-for-agents for the writing style guide.

  2. Read the primary sources for the session the user specifies. This may mean searching through session logs on this machine. If the user doesn't specify a session, default to the current one.

  3. Look for candidates for improvement in these categories.

  • Navigation: how easy was it for the agent to find the right files? Are there hidden dependencies between files? Would a navigation pointer make it easier? Use when the session took a long time to find a piece of information.
  • Automated checks: are there automated checks that could catch errors the agent made? Linting, typing, tests, filesystem linters? Read the repo's own check command first (its package.json/build-tool lint/check scripts, its CI workflow), so a check that already exists but sits unwired or silently broken is the finding, not a reinvention. A repo with no guardrail (no pre-commit hook and no CI job running its lint/typecheck/test command) is itself a finding: an un-linted repo is a standing missed opportunity, not a neutral default. Use when the agent made a mistake an automated check could have caught, or the repo has no guardrail at all.
  • Coding standards: should the reviewer agent be given a new rule to enforce? Should an existing rule be removed or clarified? Classify the violation first: a mechanical one (a fixed syntactic pattern, a banned API, an import shape, a file-location rule) gets a deterministic check, full stop: a custom rule in the repo's own linter, a new pre-commit hook, or a new CI job, whichever the repo's language and existing guardrail make cheapest. Default to building the check over writing the rule. Reserve CODING_STANDARDS.md for genuine judgement calls (cross-file consistency, "matches the surrounding style," anything no guardrail could ever substitute for). Use when the reviewer agent failed to catch a mistake.
  • Global AGENTS.md: are there any steering instructions that should be moved to coding standards (or automated checks) instead? Use when the AGENTS.md file is particularly large - in the repo OR the user's global scope.
  • Tool economy: did the agent make expensive tool calls that could be streamlined? Is there any custom tooling (CLI's, MCP's) that is particularly token-inefficient? Use when the agent made an expensive tool call.
  • No-ops: look for instructions in steering files that don't modify the agent's behavior. Use when the steering files are large and unwieldy.
  • Information access: look for opportunities to increase the agent's access to information. Teeing dev server logs, readonly access to third-party services. Use when a crucial piece of information was not available to the agent.
  1. Present these candidates to the user, in order of severity.

Reference

Implementation vs Review

Remember that all work goes through two stages: implementation and review. The implementation agent has the most context pressure. They are responsible for exploration, writing code, and debugging failures.

The review agent has the least context pressure - it receives a diff, so no exploration needed. It often does not need to write code or debug.

This means that the review agent should be responsible for imposing coding standards, not the implementation agent.

Files

You have access to several files in the repo:

  • CLAUDE.md/AGENTS.md: these files are pushed to the context window of any agent working in this repo. They should be used incredibly sparingly, usually only for navigation pointers to other files.
  • CODING_STANDARDS.md: this file is read during review, not implementation. Add navigation pointers to docs folders if the standards file gets more than 1,000 lines long.
  • Docs: use docs as references files, pointed to by other files. Look for existing docs before writing new ones.
  • Skills: use skills for docs (since their description goes into the agent's context window), or for user-invoked commands. Follow the advice in the writing-for-agents skill.

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