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Gdd Apply Reflections

Community
hegemonart
gdd-apply-reflections

Review and selectively apply proposals from .design/reflections/<cycle-slug>.md. Diffs each proposal, prompts user to accept/skip/edit, then writes changes.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namegdd-apply-reflections
Stars
3
Forks
1
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 hegemonart on GitHub. Read the source before you install it.

Installation

Install the Gdd Apply Reflections 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/hegemonart/get-design-done/tree/main/scripts/skill-templates/apply-reflections
  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/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/apply-reflections .claude/skills/gdd-apply-reflections
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Apply Reflections 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 Gdd Apply Reflections 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 Gdd Apply Reflections 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.

{{command_prefix}}apply-reflections

Interactive proposal review loop. Reads .design/reflections/<cycle-slug>.md, walks each numbered proposal, and applies accepted ones to the appropriate target file. Nothing is applied without explicit user confirmation.

Steps

1. Resolve reflections file

  • If --cycle <slug> given: load .design/reflections/<slug>.md
  • Else: glob .design/reflections/*.md, sort by modified time descending, load the most recent
  • If no file found: error "No reflections found. Run {{command_prefix}}reflect first."
  • Print: "Reviewing reflections: "

2. Parse proposals

Scan file for lines matching ### Proposal N — [TYPE] .... Extract each proposal block (Why / Change / Risk).

If --filter <TYPE> given: skip proposals whose type tag doesn't match.

Print: "Found N proposals (N after filter)."

3. Review loop

For each proposal (in order):

Print the full proposal block:

─────────────────────────────────────────
Proposal N/TOTAL — [TYPE] Title
Risk: low|medium

Why: ...
Change: ...
─────────────────────────────────────────
(a) apply   (s) skip   (e) edit   (q) quit

If --dry-run: print [dry-run — would prompt here] and continue to next proposal without prompting.

Based on user choice:

  • a - apply (see Apply Logic below)
  • s - mark proposal as **Reviewed: skipped** in the reflections file; continue
  • e - show the Change text, ask user to provide edited version, then apply the edited version
  • q - stop processing; print "Stopped at proposal N. Resume with {{command_prefix}}apply-reflections --cycle <slug>."

4. Apply Logic by Proposal Type

After the user chooses a (apply) or e (edit-then-apply), branch on the proposal's bracketed type tag and follow the per-type apply procedure in ./apply-reflections-procedure.md - one numbered procedure each for [FRONTMATTER], [REFERENCE], [BUDGET], [QUESTION], [GLOBAL-SKILL]. All branches end with **Applied**: <date> appended to the proposal block in the reflections file.

5. Summary

After all proposals processed (or q):

─────────────────────────────────────────
Apply-reflections complete
  Applied:  N
  Skipped:  N
  Remaining: N (run again to continue)
─────────────────────────────────────────

[INCUBATOR]

Incubator drafts authored by scripts/lib/incubator-author.cjs (Phase 29-04) appear as a distinct proposal class. For each draft under .design/reflections/incubator/<slug>/, use scripts/lib/apply-reflections/incubator-proposals.cjs:

  1. discoverIncubatorDrafts() → list pending drafts.
  2. renderProposal(draft) → show full body + diff + origin signals.
  3. User chooses accept | reject | defer | edit.
  4. accept - scope-guard runs FIRST (validateScope from scripts/validate-incubator-scope.cjs); applyAccept then promotes draft → agents/<slug>.md or skills/<slug>/SKILL.md and appends a registry entry. Single-step per D-04.
  5. reject - applyReject removes the incubator subdir.
  6. defer - no-op; draft re-surfaces next run.
  7. edit - applyEdit opens $EDITOR; re-prompt user on close.

Stage-1 gate. At session start, call checkStage1Gate(). If thresholdMet && !optInRecorded, display the opt-in prompt once. NEVER auto-flip per D-01 - recording opt-in requires explicit user confirmation via recordOptIn(). Full procedure: ./apply-reflections-procedure.md §[INCUBATOR].

[KFM-CANDIDATE]

KFM-catalogue proposals authored by scripts/lib/reflector-kfm-proposer.cjs (Phase 30.5-03 D-05) appear as a 6th proposal class. Drafts at .design/reflections/incubator/kfm-<slug>/CATALOGUE-ENTRY.md; pre-filled 11-field schema with TODO: placeholders for pattern + fix. Two upstream signals share the surface (D-06): capability_gap clusters (≥3, no existing match) + kfm-candidate events (whitelist-matched articles, 1-shot). User chooses accept | reject | defer | edit. applyAccept appends to reference/known-failure-modes.md + reference/registry.json (origin: incubator-kfm); applyReject removes the incubator subdir; applyDefer stamps deferred_until; applyEdit returns the draft path for $EDITOR. Full procedure: ./apply-reflections-procedure.md §[KFM-CANDIDATE].

[INSTINCT]

Atomic instinct units emitted by design-reflector (and surfaced from {{command_prefix}}extract-learnings) appear as a distinct proposal class, alongside [INCUBATOR] and [KFM-CANDIDATE]. Each unit is a fenced yaml block under the reflector's ## Atomic instincts section, shaped per reference/instinct-format.md (id, trigger, confidence, domain, scope, project_id, source, cycles_seen, first_seen, last_seen, plus a short body). A unit is a proposal, never a stored fact - nothing lands until the user accepts it.

Mirror the [INCUBATOR] flow:

  1. Discover the units: parse every yaml block under ## Atomic instincts in the reflections file. Skip malformed blocks (warn on stderr, keep going).
  2. For each unit: show the parsed trigger, domain, confidence, and body so the user sees what would be stored.
  3. Prompt: (a) accept (r) reject (d) defer (e) edit (q) quit.

Per-action behavior:

  1. accept - call scripts/lib/instinct-store.cjs add(unit, { scope, baseDir }) with the unit at its emitted confidence. The store owns de-duplication and cycles_seen bookkeeping. On success print Stored instinct <id> (<domain>, confidence <n>). and append **Applied**: <date> to the proposal block.
  2. reject - do not store the unit. Append **Reviewed: rejected** to the reflections file.
  3. defer - no-op; the unit re-surfaces next run. Append **Reviewed: deferred**.
  4. edit - let the user adjust trigger, confidence, or domain, then accept the edited unit through the same add(...) call. Default scope: 'project' (write global only when the unit's frontmatter says so); never edit .design/instincts/instincts.json directly, and promote to global via the separate gated {{command_prefix}}instinct promote.

Do Not

  • Do not apply any proposal without the user explicitly choosing a or e.
  • Do not modify source code files (.ts, .tsx, .css, .js) - only agent files, reference files, budget.json, discussant questions, global skills, and incubator drafts.
  • Do not re-run the reflector - this skill only applies existing proposals.
  • Do not bypass the scope guard or auto-flip Stage-1 - both are non-negotiable per D-05 / D-01.

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 Gdd Apply Reflections AI skill do?

Review and selectively apply proposals from .design/reflections/<cycle-slug>.md. Diffs each proposal, prompts user to accept/skip/edit, then writes changes.

Why use Gdd Apply Reflections on TypingMind?

Because you install it once and use it with any model. Gdd Apply Reflections 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 Gdd Apply Reflections in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/apply-reflections. 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 Gdd Apply Reflections?

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 Gdd Apply Reflections?

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

Is the Gdd Apply Reflections AI skill free?

Yes. It is published on GitHub by hegemonart 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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