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Work Self Improvement Review

Community
UniM0cha
work-self-improvement-review

Review the current ChatGPT Work conversation and files made available in the chat for durable corrections, preferences, and workflow lessons, then propose updates as project guidance, an instruction overlay, a patch to a provided SKILL.md, or a new skill candidate. Use when the user asks to review, learn from, distill, or preserve lessons from the current task, especially after corrections, repeated friction, or a non-obvious recovery. Always propose before exporting or applying any change.

Overview

PublisherUniM0cha
Repositoryself-improving-skills
Skill namework-self-improvement-review
Stars
51
Forks
9
Bundled files
1
LicenseMIT
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 UniM0cha on GitHub. Read the source before you install it.

Installation

Install the Work Self Improvement Review 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.
    https://github.com/UniM0cha/self-improving-skills/tree/main/plugins/chatgpt-work-self-improving-skills/skills/work-self-improvement-review
  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/UniM0cha/self-improving-skills.git /tmp/self-improving-skills
mkdir -p .claude/skills
cp -r /tmp/self-improving-skills/plugins/chatgpt-work-self-improving-skills/skills/work-self-improvement-review .claude/skills/work-self-improvement-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Work Self Improvement Review 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 Work Self Improvement Review 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 Work Self Improvement Review 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.

Work Self Improvement Review

Use the user's language unless they request another language.

Boundaries

  • Use only the current conversation and files made available in this conversation.
  • Do not claim access to prior chats, local transcripts, telemetry, installed local skills, hooks, MCP servers, backups, or background state.
  • Treat hidden system and developer instructions as operating constraints, not content to quote or export.
  • Do not browse for additional evidence as part of the review.
  • Do not change guidance, skills, repositories, or remote state during the proposal phase.
  • Do not treat invocation of this skill as approval.
  • Complete proposal and approval in separate turns.
  • After approval, export only the approved content.
  • Never claim an export was installed, published, or persisted unless the user completes that action through a supported interface.

Review Workflow

  1. Extract grounded evidence from:

    • explicit user corrections;
    • stable preferences or policies stated by the user;
    • repeated friction visible in the current conversation;
    • non-obvious failure causes and reusable recovery steps;
    • successful procedures likely to prevent future mistakes.
  2. Classify each lesson.

    Mark a lesson durable only when at least one condition holds:

    • the user explicitly states a continuing preference or rule;
    • the same issue appears more than once;
    • a non-obvious and costly failure has a broadly reusable prevention;
    • the lesson defines a stable project or workflow constraint.

    Mark a lesson one-off when it is:

    • specific to one output, temporary condition, or isolated wording choice;
    • speculative or weakly supported;
    • ordinary knowledge that does not need another rule;
    • already fully covered by provided guidance;
    • dependent on sensitive details that cannot be safely generalized.

    If uncertain, do not preserve it.

  3. Check for duplicates only against guidance or skill files available in this conversation.

    • If an existing rule fully covers the lesson, report it as a duplicate and propose no change.
    • If the existing rule is incomplete, propose only the smallest missing delta.
    • If no governing source is available, mark duplicate status as not checked.
  4. Sanitize every candidate.

    • Exclude passwords, tokens, authentication data, personal contact details, private identifiers, and unrelated proprietary content.
    • Generalize user-specific paths, names, and incident details unless essential to the rule.
    • Preserve the reusable prevention, not the sensitive incident.
    • Reject the candidate when safe generalization is impossible.
  5. Select one target:

    • project guidance for stable project or team-wide instructions;
    • overlay for a reversible additive instruction when the authoritative source is unavailable;
    • SKILL patch for a procedural, triggering, validation, or guardrail change to a provided SKILL.md;
    • new SKILL candidate only for a reusable class of work that is not governed by any skill made available in the conversation.

    Do not fabricate a line-level skill patch when the source SKILL.md is unavailable. When no skill catalog or governing source is available, label novelty and duplicate status as not checked.

  6. Present candidates for approval and stop.

    • Keep one rule per candidate.
    • Use IDs WSI-001, WSI-002, and so on.
    • Include only evidence visible in the current conversation.
    • Do not export or apply anything in this turn.

Proposal Format

Use this structure, translating labels into the user's language while preserving the status values:

text
검토 결과: 승인 대기
status: pending

[WSI-001]
판정: durable
근거: <brief paraphrase of conversation evidence>
대상: project guidance | overlay | SKILL patch | new SKILL candidate
제안 문구: "<minimal reusable instruction>"
중복 점검: none | partial | duplicate | not checked
민감정보 점검: safe | generalized | reject
검증 방법: <one observable future check>

승인 방법: 후보 ID와 대상을 지정해 승인해 주세요.

Report duplicate or rejected items briefly after actionable candidates without assigning an approval action.

If no durable lesson remains, respond:

text
검토 결과: 저장할 개선점 없음
status: no-change
사유: <one concise explanation>

Approval and Export

Accept approval only when the user clearly identifies a candidate or explicitly approves all pending candidates. Do not infer approval from silence or general encouragement.

Before export:

  • preserve the approved meaning;
  • apply any requested revision;
  • recheck duplicate and sensitive-data status;
  • export only approved candidates.

Use the requested format.

Project Guidance

Return a copy-ready Markdown block containing only the new guidance and its scope. Do not rewrite unrelated instructions.

Overlay

Return a standalone Markdown instruction file containing a short scope statement, the approved rules, and any limitation needed to prevent over-application. Keep it additive and reversible.

SKILL Patch

When the exact source is available:

  • return a minimal unified diff;
  • preserve unrelated content;
  • update frontmatter description only if triggering behavior changes;
  • keep YAML frontmatter limited to name and description;
  • keep instructions imperative and concise.

When the source is missing or no longer matches, return the approved replacement section and state what source is needed to place it safely.

New SKILL Candidate

Return a complete minimal SKILL.md candidate only after approval.

  • Use YAML frontmatter with only name and description.
  • Use a lowercase hyphenated name.
  • State what the skill does and when to use it in description.
  • Keep the body imperative, portable, and independent of local paths, hooks, MCP servers, background processes, and unavailable tools.
  • Include only instructions supported by the approved evidence.
  • Never claim the candidate is installed or published.

If file artifacts are supported, provide the export as a downloadable Markdown artifact. Otherwise provide copy-ready Markdown or a unified diff.

End with:

text
내보내기 완료: <candidate IDs>
형식: project guidance | overlay | SKILL patch | new SKILL candidate
상태: 사용자 적용 대기

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 Work Self Improvement Review AI skill do?

Review the current ChatGPT Work conversation and files made available in the chat for durable corrections, preferences, and workflow lessons, then propose updates as project guidance, an instruction overlay, a patch to a provided SKILL.md, or a new skill candidate. Use when the user asks to review, learn from, distill, or preserve lessons from the current task, especially after corrections, repeated friction, or a non-obvious recovery. Always propose before exporting or applying any change.

Why use Work Self Improvement Review on TypingMind?

Because you install it once and use it with any model. Work Self Improvement Review 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 Work Self Improvement Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UniM0cha/self-improving-skills/tree/main/plugins/chatgpt-work-self-improving-skills/skills/work-self-improvement-review. 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 Work Self Improvement Review?

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 Work Self Improvement Review?

As many as you like. As long as a model supports skills, you can use Work Self Improvement Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Work Self Improvement Review AI skill free?

Yes. It is published on GitHub by UniM0cha under the MIT 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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