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Superpowers Learning Workflow

Community
SYZ-Coder
superpowers-learning-workflow

Use when the user explicitly wants to capture lessons from completed work, persist durable project knowledge, or turn repeated patterns into reusable learning notes for future sessions.

Overview

PublisherSYZ-Coder
Repositorysuperpowers-openspec-team-skills
Skill namesuperpowers-learning-workflow
Stars
200
Forks
54
Bundled files
3
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 3 bundled files

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

  • Open source

    Published by SYZ-Coder on GitHub. Read the source before you install it.

Installation

Install the Superpowers Learning Workflow 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/SYZ-Coder/superpowers-openspec-team-skills/tree/main/team-skills/superpowers-learning-workflow
  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/SYZ-Coder/superpowers-openspec-team-skills.git /tmp/superpowers-openspec-team-skills
mkdir -p .claude/skills
cp -r /tmp/superpowers-openspec-team-skills/team-skills/superpowers-learning-workflow .claude/skills/superpowers-learning-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Superpowers Learning Workflow 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 Superpowers Learning Workflow 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 Superpowers Learning Workflow 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.

Superpowers Learning Workflow

Overview

Use this workflow after meaningful work to capture what should survive the current session. It is a lightweight, repo-owned learning loop inspired by reflective agent systems, but scoped for safe use inside normal project workflows.

This is an explicit opt-in workflow. Do not use it by default. Only use it when the user explicitly asks for this workflow, names this skill, or a repository policy explicitly requires it.

Workflow

  1. Review the recent work, decisions, and verification evidence.
  2. Classify what was learned into four buckets:
    • durable project facts
    • current working state
    • session outcome
    • reusable method or repeated pitfall
  3. Add required metadata for durable entries:
    • id
    • status
    • confidence
    • source
    • last_updated
    • review_after Do not mark an entry as verified if source is empty.
  4. If .superpowers-memory/ exists, update:
    • PROJECT_CONTEXT.md for durable facts
    • CURRENT_STATE.md for active state
    • DECISIONS.md for lasting decisions
    • KNOWN_FAILURES.md for repeated failure patterns
    • VERIFICATION_BASELINE.md for trusted verification rules
    • TEAM_PREFERENCES.md for durable team agreements
    • USER_PROFILE.md for durable user preferences that are not project facts
    • AGENT_NOTES.md for durable execution reminders that are not project facts
    • session-journal/ for the session summary
    • LEARNING_BACKLOG.md for reusable patterns that may deserve future workflows or skills
  5. If .superpowers-memory/ does not exist, tell the user to install the memory scaffold or keep the learning summary in a normal project doc.
  6. Check whether any backlog item is strong enough to recommend promotion into a checklist, project rule, workflow step, script, or skill draft.
  7. Review .superpowers-memory/SESSION_CLOSE_CHECKLIST.md before finishing the learning capture.
  8. Use scripts/suggest-superpowers-memory-updates.ps1 if it is unclear which memory surfaces should be updated from the current session signals.
  9. Prefer scripts/run-superpowers-memory-closeout.ps1 as the standard closeout helper when you want one command to review the checklist, get update suggestions, and optionally run validation.
  10. When memory files were updated, run scripts/validate-superpowers-memory.ps1 and include the result in the summary.
  11. Use scripts/search-superpowers-memory.ps1 when you need to confirm whether a pattern already exists in durable memory or recent journals.
  12. Summarize what was learned and what, if anything, should become a future rule, checklist, script, or skill.

When to Use

  • The user explicitly asks to capture lessons from the current session
  • The user explicitly names $superpowers-learning-workflow
  • The user wants to persist durable knowledge for future sessions
  • The user wants to turn repeated patterns into reusable learning notes
  • A repository policy explicitly requires reflective capture after meaningful work

Outputs

  • Updated .superpowers-memory/PROJECT_CONTEXT.md when durable facts changed
  • Updated .superpowers-memory/CURRENT_STATE.md
  • Updated .superpowers-memory/DECISIONS.md when durable decisions changed
  • Updated .superpowers-memory/KNOWN_FAILURES.md when repeated failure patterns were identified
  • Updated .superpowers-memory/VERIFICATION_BASELINE.md when trusted verification rules changed
  • Updated .superpowers-memory/TEAM_PREFERENCES.md when durable team agreements changed
  • Updated .superpowers-memory/USER_PROFILE.md when durable user preferences changed
  • Updated .superpowers-memory/AGENT_NOTES.md when durable execution reminders changed
  • New or updated session journal entry
  • Updated .superpowers-memory/LEARNING_BACKLOG.md for reusable lessons
  • Updated .superpowers-memory/SESSION_CLOSE_CHECKLIST.md only as a reference checklist, not as a session log
  • Memory validation evidence when memory was updated
  • Optional memory update suggestion evidence when the suggestion script was used
  • Optional closeout helper output when the closeout script was used
  • A short summary of what should be remembered next time

Guardrails

  • Do not write temporary TODO noise into PROJECT_CONTEXT.md
  • Do not turn a one-off fix into a reusable rule without a clear repeated pattern
  • Do not auto-edit the skill library itself unless the user explicitly asks for that separate step
  • Keep learning notes concise and actionable
  • Do not promote a backlog item without enough repeated evidence or cross-session value
  • Treat ready_for_promotion as a higher bar: expect repeated evidence, linked sources, and a reviewable promotion rationale

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 Superpowers Learning Workflow AI skill do?

Use when the user explicitly wants to capture lessons from completed work, persist durable project knowledge, or turn repeated patterns into reusable learning notes for future sessions.

Why use Superpowers Learning Workflow on TypingMind?

Because you install it once and use it with any model. Superpowers Learning Workflow 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 Superpowers Learning Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SYZ-Coder/superpowers-openspec-team-skills/tree/main/team-skills/superpowers-learning-workflow. 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 Superpowers Learning Workflow?

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 Superpowers Learning Workflow?

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

Is the Superpowers Learning Workflow AI skill free?

Yes. It is published on GitHub by SYZ-Coder 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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