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Suggest Power Tools

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Matt-Hulme
suggest-power-tools

Proactively spot moments in ordinary work where one of Claude Code's force-multipliers should be proposed - a multi-agent Workflow (fan-out audits, repo-wide sweeps, judge panels, adversarial verification), an agent team (several separable long-lived tracks that must coordinate an evolving contract and that you want to steer in parallel - e.g. a cross-layer feature spanning frontend, backend, and tests), a /goal (a mission lacking verifiable success criteria), a /loop or /schedule (babysitting, polling, recurring or deferred obligations), capturing a just-invented repeatable technique as a skill, a settings hook for "I keep manually doing X after Y" patterns, recording a durable design principle in a decision log, or recalibrating effort (cheap-vs-heavy models, quick-pass-vs-adversarial verification, /fast, token budgets) when a task's gravity does not match how it is being approached. Use during ANY substantive task when its shape matches one of these - the user will not ask for them by name; noticing is the job. Also use when the user asks whether a workflow/goal/loop could help, how to approach something "systematically", "thoroughly", or "quickly", or how to "weed out" issues "across the board".

Overview

PublisherMatt-Hulme
Repositoryclaude-power-tools
Skill namesuggest-power-tools
Stars
70
Forks
6
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

    Published by Matt-Hulme on GitHub. Read the source before you install it.

Installation

Install the Suggest Power Tools 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/Matt-Hulme/claude-power-tools/tree/main/suggest-power-tools
  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/Matt-Hulme/claude-power-tools.git /tmp/claude-power-tools
mkdir -p .claude/skills
cp -r /tmp/claude-power-tools/suggest-power-tools .claude/skills/suggest-power-tools
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Suggest Power Tools 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 Suggest Power Tools 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 Suggest Power Tools 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.

Opportunity Scout

Claude Code has force-multipliers the user cannot be expected to reach for, because they think in terms of their problem ("are all these endpoints consistent?", "ugh, waiting on CI again", "we keep making this mistake") - not in terms of orchestration, goals, loops, hooks, or skills. The scout's job: notice when the current task's shape matches one of these tools, and surface it at the right moment with an honest pitch. The user decides.

Shared etiquette (applies to every scout)

  • One proposal per opportunity per task. If declined or ignored, drop it without ceremony and do the work inline well. A scout that nags gets ignored, then disabled.
  • Never stack proposals. If two scouts fire on the same moment, pick the strongest and stay quiet about the rest.
  • Propose at a seam, not mid-execution - when the shape becomes clear enough to sketch concretely, typically just before the heavy part would begin.
  • Be honest about cost. Token spend, setup time, maintenance burden - the user is deciding whether the multiplier pays for itself. Don't lowball to get a yes.
  • Enthusiasm is not approval. "Great idea" about the design is not "go". Each reference defines its action policy - most are propose-only; only act where the policy explicitly says so.
  • The proposal is itself a thinking step. Articulating the pipeline/condition/trigger forces clarity, and the user can correct the shape before anything runs. Even a declined proposal usually paid for itself.

Recognition routing

When the current work matches a row, read that reference for the per-type recognition signals, action policy, and proposal template:

Shape you're seeingScoutReference
Same operation/question across many units; audit or consistency passes; findings that need adversarial verification; whole-repo sweeps; "systematically", "across the board", "weed out"Workflowreferences/workflow.md
Several separable, long-lived tracks that must negotiate an evolving shared contract; cross-layer feature builds where each track is owned and steered in parallel; wanting to answer several agents at once instead of one thread at a timeAgent teamsreferences/agent-teams.md
A multi-turn mission with mushy success criteria; "keep going until it works"; work that will drift without a fixed finish lineGoalreferences/goal.md
Babysitting: waiting on CI/deploys/external processes; "check back later"; "remind me"; recurring choresLoop / Schedulereferences/loop-schedule.md
We just did something the hard way that will recur: invented a pipeline, corrected the same mistake twice, built a reusable recipeSkill capturereferences/skill-creation.md
"From now on, when X..."; the user (or you) keeps manually repeating a step after a trigger eventAutomation hookreferences/automation-hook.md
A durable design principle just crystallized in conversation; a previously locked decision got revisedDecision logreferences/decision-log.md
The task's gravity doesn't match the effort being applied - heavy models grinding rote work, or a subtle/critical change getting a quick skim; the user says "thoroughly", "quick pass", "don't overthink"Effort calibrationreferences/effort.md

The strongest opportunities are often compositional (e.g. a workflow whose completion condition becomes a /goal). Name the composition if you see it - those are the proposals users remember.

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 Suggest Power Tools AI skill do?

Proactively spot moments in ordinary work where one of Claude Code's force-multipliers should be proposed - a multi-agent Workflow (fan-out audits, repo-wide sweeps, judge panels, adversarial verification), an agent team (several separable long-lived tracks that must coordinate an evolving contract and that you want to steer in parallel - e.g. a cross-layer feature spanning frontend, backend, and tests), a /goal (a mission lacking verifiable success criteria), a /loop or /schedule (babysitting, polling, recurring or deferred obligations), capturing a just-invented repeatable technique as a...

Why use Suggest Power Tools on TypingMind?

Because you install it once and use it with any model. Suggest Power Tools 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 Suggest Power Tools in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Matt-Hulme/claude-power-tools/tree/main/suggest-power-tools. 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 Suggest Power Tools?

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 Suggest Power Tools?

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

Is the Suggest Power Tools AI skill free?

Yes. It is published on GitHub by Matt-Hulme 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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