Skill Debrief logo

Skill Debrief

Organization
computerlovetech
skill-debrief

Debrief an AI agent skill (SKILL.md) after using it — capture session feedback or a retrospective and fold it back into the skill. Use whenever the user says: "debrief the X skill", "let's debrief X", "retrospective on X", "feedback on X skill", "improve the X skill", "update the X skill", "let's revise X based on what we just did", "the X skill should also handle Y", "X didn't trigger when it should have", or otherwise wants to capture lessons from a session back into the skill that drove it. Handles in-repo skills (under skills/) by editing the source, committing, and re-installing via `agr upgrade`. Handles remote/upstream skills by offering to fork them in-repo or to file a GitHub issue via `gh`. Do NOT use for greenfield skill authoring (a separate concern — see `anthropics/skills/skill-creator`) or for installing / syncing / removing skills (use the `agr` CLI directly).

Overview

Publishercomputerlovetech
Repositoryagr
Skill nameskill-debrief
Stars
453
Forks
37
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Skill Debrief 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/computerlovetech/agr/tree/main/skills/skill-debrief
  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/computerlovetech/agr.git /tmp/agr
mkdir -p .claude/skills
cp -r /tmp/agr/skills/skill-debrief .claude/skills/skill-debrief
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Debrief 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 Skill Debrief 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 Skill Debrief 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.

Skill Debrief

Capture lessons from a session into the skill that drove it. The default shape is listen → propose → align → apply → re-install.

When to use

Trigger when the user wants to debrief an existing SKILL.md based on what happened in the session. Examples:

  • "debrief the X skill" / "let's debrief X"
  • "retrospective on X" / "feedback on X"
  • "improve the X skill" / "let's update X based on what we learned"
  • "X skill should also handle …"
  • "X didn't trigger when it should have"

Do NOT use this skill for:

  • Greenfield skill authoring. Use agr init to scaffold a SKILL.md and defer the body content to the user — or to a dedicated authoring skill such as anthropics/skills/skill-creator (agr add anthropics/skills/skill-creator).
  • Installing / syncing / removing skills. That's plain agr CLI work (agr add, agr sync, agr upgrade, agr remove).

Step 1: Identify the skill

Ask which skill is being improved if it isn't obvious from context. Then locate the source:

bash
agr list                # see installed deps and short names
ls skills/              # in-repo source if present
cat agr.toml            # see whether the dep is local-path or remote

Two cases — they have different update paths:

CaseSource locationUpdate path
In-repo ({path = "./skills/<name>", type = "skill"} in agr.toml)skills/<name>/Edit source → commit → agr upgrade <name>
Remote ({handle = "user/repo/<name>", …})Upstream GitHub repoCannot edit directly — see Step 5

If the skill isn't installed at all but the user wants to improve it, ask whether to add it first (and which case applies).

Step 2: Receive feedback

Listen. The user invoked this skill because they have something to say — let them say it. Do not interrogate. Do not run a checklist of questions at them. Take in whatever they offer, in whatever shape they offer it.

Only ask a clarifying question if you genuinely cannot proceed without one (e.g. the user named a skill that doesn't exist, or two skills share the name and you need to disambiguate). Even then, ask the minimum.

Be dynamic. The user may surface things in any shape — a single sentence ("the description should also fire on X"), a structured list, or a ramble that you need to distill. They may also surface things outside the standard buckets below (rename a section, restructure references/, change output format, drop a deprecated workflow, fix a typo). Apply whatever the user actually says.

The buckets below are a mental map for you when distilling what you heard, not a checklist to recite at the user:

  • description / triggers — under-fired or over-fired
  • Gotchas / boundaries — a foot-gun the skill didn't warn about
  • Workflow steps — missing, wrong, or out of order
  • References — a topic kept needing more depth → new references/<topic>.md
  • Examples / output format — vague where it should be concrete
  • Pruning — outdated content that misled

Step 3: Propose changes

Summarize what you heard, then propose specific edits. Format:

Proposed changes to skills/<name>/SKILL.md (and any references):

  1. Description — add trigger phrase "…" (because: …)
  2. Boundaries — add: never X (because: discovered this in session)
  3. New section "Y" — describes the workflow that was missing

Want me to apply these, revise, or add more?

For small edits, show the exact diff inline. For larger changes, summarize first and apply section by section.

Wait for explicit user approval before editing. "yes" / "go ahead" / similar. If the user revises, loop back to Step 2 or 3.

Step 4: Apply (in-repo case)

Edit the source file(s) under skills/<name>/. Then:

bash
git status                        # confirm only the intended files changed
git add skills/<name>/
git commit -m "skill(<name>): <one-line summary>"
agr upgrade <name>                # re-installs into all configured tools, refreshes agr.lock
git add agr.lock
git commit --amend --no-edit      # or commit separately; match the repo's style

Do not push. Stop after the commit and let the user push when ready.

Commit message style

Use a conventional-commits-style scope:

skill(<name>): <imperative summary>

Examples:

skill(agr-cli): clarify upgrade vs sync for local paths
skill(agr-cli): add gotcha for same-repo siblings
skill(code-review): drop outdated linter pre-check

If the repo's commit style differs (check git log --oneline -20), match it.

Why agr upgrade and not agr sync?

agr sync only installs missing deps — it does NOT re-copy a local-path skill that's already installed. agr upgrade <name> re-copies it and refreshes agr.lock. Use agr upgrade.

(Equivalent: agr add ./skills/<name> --overwrite. Pick upgrade for consistency — it's the same verb used to refresh remote skills.)

Step 5: Apply (remote case)

If the skill is a remote dep, the change cannot be applied directly. Ask the user which path they want:

Option A — File an issue upstream via gh

Best when the change benefits everyone (a real bug or universal improvement in someone else's published skill).

Resolve the upstream repo from the handle:

  • anthropics/skills/pdf → --repo anthropics/skills
  • user/myrepo/skill → --repo user/myrepo

Confirm the title and body with the user, then:

bash
gh issue create \
  --repo <owner>/<repo> \
  --title "[<skill-name>] <short summary>" \
  --body "$(cat <<'EOF'
## What I observed

<concrete scenario from the session>

## Suggested change

<proposed wording or workflow>

## Why

<reasoning>
EOF
)"

gh issue create posts publicly — treat it the same as any other shared- state action. Always confirm before running.

If the user has push access and a local clone, also offer to open a PR instead of (or alongside) the issue.

Option B — Fork to in-repo

Best when the change is project-specific or unlikely to be accepted upstream. Copies the skill into skills/<name>/ so future retros work the in-repo way.

Suggested flow:

bash
# 1. Find the upstream commit (agr.lock has it)
agr list

# 2. Sparse-checkout or full clone, then copy the folder:
mkdir -p skills
cp -r /tmp/upstream-clone/<skill-folder> skills/<name>

# 3. Swap the dep
agr remove anthropics/skills/<name>
agr add ./skills/<name>

Tell the user this forks the skill — they're now responsible for keeping it current with upstream. Then continue from Step 3 with the in-repo flow.

Both A and B

Offer Option A first when the change is generally useful. Offer Option B when upstream is unlikely to accept, or when the user wants the change now without waiting on upstream.

Step 6: Verify

After re-installing (in-repo case):

bash
agr list                                          # status should be `installed`
diff skills/<name>/SKILL.md .claude/skills/<name>/SKILL.md   # should be empty

For remote case (issue filed): confirm the issue URL with the user.

Tell the user what's done and what's pending (commit done, push pending; or issue filed, awaiting response).

Boundaries

  • Don't edit a skill without explicit user approval of the proposed changes. Skills are user-owned content — never silently revise.
  • Don't push. Commit only; the user pushes when ready.
  • Don't open issues or PRs without confirming the title and body with the user first. gh issue create is publicly visible — treat it as shared-state.
  • Don't edit agr.lock by hand — agr upgrade regenerates it.
  • Don't broaden the scope of the edit beyond what was discussed. If the user asked to fix one gotcha, don't also restructure the file.
  • Don't write a skill from scratch — that's a separate workflow.

See also

  • anthropics/skills/skill-creator — canonical greenfield skill authoring (install with agr add anthropics/skills/skill-creator if needed)

Frequently asked questions

What does the Skill Debrief AI skill do?

Debrief an AI agent skill (SKILL.md) after using it — capture session feedback or a retrospective and fold it back into the skill. Use whenever the user says: "debrief the X skill", "let's debrief X", "retrospective on X", "feedback on X skill", "improve the X skill", "update the X skill", "let's revise X based on what we just did", "the X skill should also handle Y", "X didn't trigger when it should have", or otherwise wants to capture lessons from a session back into the skill that drove it. Handles in-repo skills (under skills/) by editing the source, committing, and re-installing via `a...

Why use Skill Debrief on TypingMind?

Because you install it once and use it with any model. Skill Debrief 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 Skill Debrief in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/computerlovetech/agr/tree/main/skills/skill-debrief. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Skill Debrief?

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 Skill Debrief?

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

Is the Skill Debrief AI skill free?

Yes. It is published on GitHub by computerlovetech 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇