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Decisive Action

Community
athola
decisive-action

Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.

Overview

Publisherathola
Repositoryclaude-night-market
Skill namedecisive-action
Stars
343
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 athola on GitHub. Read the source before you install it.

Installation

Install the Decisive Action 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/athola/claude-night-market/tree/master/plugins/conserve/skills/decisive-action
  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/athola/claude-night-market.git /tmp/claude-night-market
mkdir -p .claude/skills
cp -r /tmp/claude-night-market/plugins/conserve/skills/decisive-action .claude/skills/decisive-action
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decisive Action 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 Decisive Action 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 Decisive Action 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.

Decisive Action

Guidance on when to ask clarifying questions versus proceeding autonomously.

When To Use

  • Reducing unnecessary clarifying questions
  • Taking autonomous action when intent is clear

When NOT To Use

  • High-stakes irreversible operations requiring explicit confirmation
  • Ambiguous requirements where clarification prevents wasted work

Core Principle

Ask questions only when ambiguity would materially impair correctness or capacity to fulfill the request precisely.

When to Ask (High Impact Ambiguity)

Always Ask For

ScenarioWhyExample
Destructive OperationsIrreversible, high cost of error"Delete which files?"
Multiple Valid ApproachesMaterially different tradeoffs"Add index vs cache vs denormalize?"
Security-CriticalWrong choice = vulnerability"Which auth method?"
Data MigrationData loss risk"Preserve or transform?"
Breaking ChangesAffects downstream users"Deprecate or remove?"

Ask Threshold Checklist

Before asking, verify:

  • >30% chance of wrong interpretation
  • Error cost > correction cost
  • No clear standard approach exists
  • User context doesn't clarify intent

When to Proceed Without Asking

Default to Action For

ScenarioWhyAssumption
Standard Approach ExistsIndustry conventionUse conventional pattern
Easily ReversibleLow cost of errorCan undo via git/backup
Clear from ContextIntent is obviousProceed with stated goal
User Can ReviewPR/dry-run availableChanges are inspectable

Proceed Threshold Checklist

Proceed without asking if:

  • Standard/obvious solution exists
  • Easily reversible (git, backup)
  • User can review before finalize
  • Context makes intent clear
  • Error cost < interruption cost

Decision Matrix

ReversibilityAmbiguityAction
ReversibleLowProceed
ReversibleHighProceed with preview
IrreversibleLowProceed with confirmation
IrreversibleHighAsk

Safety Mechanisms

Before Proceeding Autonomously

  1. Dry-run/Preview: Show proposed action before executing
  2. Backup First: Create git branch, backup directory
  3. Incremental Changes: Commit small, reviewable chunks
  4. Document Assumptions: In commit message or PR description

Undo Capability

Always ensure rollback is possible:

  • Git operations: git stash, backup branches
  • File operations: Copy before modify
  • Data operations: Transaction rollback

Examples

Example: Should Ask

User: "Delete the old files"

Analysis:

  • Destructive operation ✗
  • Multiple interpretations ✗
  • No clear standard ✗

Response:

Which files should I delete? I found:
- Files unchanged >6 months: 23 files
- Files in deprecated/: 5 files
- Files marked TODO remove: 2 files

Example: Should Proceed

User: "Add error handling to the parser"

Analysis:

  • Standard approach exists ✓
  • Easily reversible (git) ✓
  • User can review PR ✓

Action: Implement try/catch with logging, create PR.

Example: Proceed with Preview

User: "Refactor the API layer"

Analysis:

  • Multiple valid approaches
  • BUT: reversible via git
  • AND: user can review

Action:

Refactoring API layer. Proposed changes:

1. Extract authentication middleware
2. Standardize error responses
3. Add request validation

Creating branch: refactor/api-layer
Preview diff available before merge.

Anti-Patterns

Asking Too Much (Inefficient)

  • Asking for every implementation detail
  • Seeking validation for obvious choices
  • Repeating questions already answered in context

Asking Too Little (Risky)

  • Proceeding with destructive actions silently
  • Assuming intent when multiple valid interpretations exist
  • Ignoring ambiguity in security-critical operations

Integration

Combine with:

  • conserve:response-compression - Direct communication
  • sanctum:git-workspace-review - Context gathering
  • imbue:scope-guard - Scope management

Quick Reference

SituationAction
"Delete X"Ask which X
"Add feature"Proceed with standard approach
"Fix bug"Proceed with obvious fix
"Choose between A/B"Ask for preference
"Optimize query"Ask if multiple approaches
"Format code"Proceed with project style
"Deploy to prod"Ask for confirmation

Exit Criteria

  • Every proposed autonomous action checked against the Decision Matrix: reversibility and ambiguity axes produce an explicit "Proceed", "Proceed with preview", or "Ask" verdict
  • Irreversible operations (destructive deletes, data migrations, breaking changes) never executed without at least one clarifying question regardless of apparent intent clarity
  • When proceeding autonomously, one of the four safety mechanisms is applied: dry-run/preview, backup, incremental commits, or documented assumptions in the commit message
  • No clarifying question asked for scenarios in the "Default to Action" table where standard approach exists and changes are easily reversible via git

Frequently asked questions

What does the Decisive Action AI skill do?

Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.

Why use Decisive Action on TypingMind?

Because you install it once and use it with any model. Decisive Action 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 Decisive Action in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/conserve/skills/decisive-action. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decisive Action?

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 Decisive Action?

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

Is the Decisive Action AI skill free?

Yes. It is published on GitHub by athola 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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