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Consolidate Task

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mcouthon
consolidate-task

Use when you need to consolidate a completed task into an architectural decision record and a proposed instruction delta for future agents, reducing clutter in .tasks/. Triggers on: 'use consolidate-task mode', 'consolidate-task', 'consolidate task', 'summarize task', 'retrospective', 'what did we learn', 'compound learnings'.

Overview

Publishermcouthon
Repositoryagents
Skill nameconsolidate-task
Stars
79
Forks
11
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 mcouthon on GitHub. Read the source before you install it.

Installation

Install the Consolidate Task 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.
  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/mcouthon/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/generated/claude/skills/consolidate-task .claude/skills/consolidate-task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Consolidate Task 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 Consolidate Task 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 Consolidate Task 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.

Consolidate a Completed Task

One pass over .tasks/{task-folder}/ produces up to two outputs:

OutputAudienceQuestionDestination
Architectural recordfuture developersWhat did the code decide?docs/architecture/ADR-NNN-*.md
Instruction deltafuture agentsWhat should agents do differently?routed by scope — see Process Learnings

Each is independently skippable. Most tasks produce one; some produce neither. Read .tasks/{task-folder}/task.md for the architectural record, and the phase plans for the instruction delta.

When to Create an ADR

Not every completed task needs an ADR. ADRs document architectural decisions, not implementation work.

Skip ADR entirely when:

  • Task adds a minor feature within existing patterns
  • Task fixes bugs or refines implementation details
  • Task is routine maintenance or cleanup
  • Changes follow conventions already documented elsewhere

Update existing ADR when:

  • Task extends or modifies patterns documented in a prior ADR
  • Task adds significant new component to an existing architectural area
  • Changes affect how future developers should approach that area

Create new ADR when:

  • Task introduces new architectural patterns
  • Task reverses or significantly modifies a prior decision
  • Task establishes new conventions for the codebase

When updating, add an entry to the "Updates" table and integrate changes into relevant sections.

File Naming

Required format: ADR-NNN-{decision-name}.md

  1. Scan docs/architecture/ for existing ADR-* files
  2. Check if any existing ADR covers the same architectural area (update if so)
  3. For new ADRs: find the highest number (e.g., ADR-003-* → next is 004)
  4. Start at 001 if no ADR files exist
  5. Save to: docs/architecture/ADR-NNN-{decision-name}.md

Example: ADR-004-unified-query-execution.md

Output Format

markdown
# {Decision Title}

**Source:** Task {NNN} ({Month Year})

## Decision

One sentence describing the high-level architectural choice.

## Why

- Bullet points explaining the motivation
- Focus on problems solved, not implementation details

## Problem Statement (if applicable)

What issue prompted this change? What was broken or suboptimal?

## Solution

Brief description with before/after comparison:

### Before

{Old approach - can include diagrams or code}

### After

{New approach - can include diagrams or code}

## Implementation Phases

| Phase     | What Changed       |
| --------- | ------------------ |
| 1. {Name} | {One-line summary} |
| ...       | ...                |

## Key Architectural Patterns

Include 2-3 code snippets showing the most important patterns established.
Only patterns that future developers need to understand and follow.

## Current Structure

```
relevant/directory/
├── file1.py # Brief purpose
├── file2.py # Brief purpose
```

## Deleted

- List of deleted files/code (shows what was replaced)

## Updates (for existing ADRs only)

| Date         | Task  | Summary                              |
| ------------ | ----- | ------------------------------------ |
| {Month Year} | {NNN} | One-line description of what changed |

Process Learnings (Instruction Delta)

The ADR tells future developers what the code decided. This tells future agents what to do differently. Same pass, second audience.

Input — read, don't recall

  1. .tasks/{task-folder}/plan/phase-*.md → every ## Execution Notes section. Primary and preferred evidence — Builder writes these while the friction happens.
  2. .tasks/{task-folder}/task.md → the phase table's Notes column and any deviation notes.
  3. Phase plans whose ## Verification section proved insufficient — a manual-testing finding that no planned check would have caught is itself a learning about how plans get written.

If there are no ## Execution Notes anywhere, output "no process learnings" and stop. Do not reconstruct a retrospective from the diff or from memory. That is fabrication.

Filter — what qualifies

A learning must be generalizable and actionable: one sentence must be able to complete "next time, the instruction should say ___."

QualifiesDoes not
An instruction was ambiguous and cost a retryA one-off bug in this task's code
A convention exists that no instruction statesA preference with no evidence behind it
A verification step is routinely missing from plansAnything already covered by an existing instruction
A recurring shape of mistake across phasesA restatement of the ADR

At most 3 proposals per task. More than three means the filter isn't being applied.

Routing — by scope of the learning, not by a fixed path

Scope of the learningDestinationNotes
A convention or non-obvious dependency of this repositoryAGENTS.md## Learned Patterns tableSame sink Explorer's Repository Patterns step uses. Create the section if absent.
Domain terminology / namingCONTEXT.md at workspace rootRead by Explorer and Builder
A gap in how an agent or skill behavesThe instruction file that owns that behaviorIn a framework repo that is the template source of truth (never edit generated output; run its build+install after). In a consuming repo it is the user's own agent/skill files.
A gap in the framework's posture, not one agent'sNote it for docs/research/BACKLOG.mdOut of write scope — propose only

Output — propose, never apply silently

📝 Process learnings from task {NNN}

1. [What happened] → [proposed instruction change]
   Evidence: .tasks/{slug}/plan/phase-N-*.md ## Execution Notes
   Destination: [file] → [section]

Apply these? [All] [Select] [None]
  • Every proposal cites the ## Execution Notes line it came from. No citation, no proposal.
  • Nothing is written without confirmation.
  • On confirmation, if a destination is a template source of truth, run the build and install yourself (in this framework repo: make && ./install.sh) before reporting completion — do not merely state that it is required.
  • "No process learnings this task" is a normal, expected, good outcome. Do not pad to look useful.
  • Fast Path Mode: when the invoking prompt says the task runs under Fast Path Mode, do not block for confirmation here. Return the proposal list unapplied and apply nothing; Conductor folds it into the final Delivery Report, where approval happens once.

Guidelines

  1. High-level only — Skip implementation details that don't affect architecture
  2. Focus on patterns — What should future code follow?
  3. Include deletions — Shows what was replaced, not just what was added
  4. Code examples — Only for patterns that repeat across the codebase
  5. Keep it scannable — Tables and bullets over paragraphs

After Saving

  1. Update docs/architecture/README.md (create if missing with a decisions table)
  2. Delete or archive the original task folder
  3. If updating an existing ADR: add entry to the Updates table at the bottom
  4. Report the instruction delta outcome: which instruction files were changed, or "no process learnings". When a template source of truth was changed on confirmation, confirm the build+install (make && ./install.sh here) was run, quoting its output — not that it is required. Under Fast Path Mode nothing was applied: report the pending proposal count instead, and note that build+install runs only if a template-targeted proposal is approved at the final Delivery Report.

Frequently asked questions

What does the Consolidate Task AI skill do?

Use when you need to consolidate a completed task into an architectural decision record and a proposed instruction delta for future agents, reducing clutter in .tasks/. Triggers on: 'use consolidate-task mode', 'consolidate-task', 'consolidate task', 'summarize task', 'retrospective', 'what did we learn', 'compound learnings'.

Why use Consolidate Task on TypingMind?

Because you install it once and use it with any model. Consolidate Task 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 Consolidate Task in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mcouthon/agents/tree/main/generated/claude/skills/consolidate-task. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Consolidate Task?

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 Consolidate Task?

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

Is the Consolidate Task AI skill free?

Yes. It is published on GitHub by mcouthon 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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