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Autodecision

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harshilmathur
autodecision

Auto-Decision Engine: iterative decision simulation using autoresearch principles and a persona council. Decomposes decisions, generates competing hypotheses, simulates first/second-order effects with probabilities, critiques via anonymized peer review, and iterates until insights stabilize mechanically. The output is a possibility map — hypotheses, effects, council disagreements, adversarial scenarios, assumptions — with a recommendation synthesized at the end, not as the product.

Overview

Publisherharshilmathur
Repositoryautodecision
Skill nameautodecision
Stars
104
Forks
9
Bundled files
33
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.

  • 33 bundled files

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

  • Open source

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

Installation

Install the Autodecision 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/harshilmathur/autodecision/tree/main/.claude/skills/autodecision
  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/harshilmathur/autodecision.git /tmp/autodecision
mkdir -p .claude/skills
cp -r /tmp/autodecision/.claude/skills/autodecision .claude/skills/autodecision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autodecision 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 Autodecision 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 Autodecision 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.

Auto-Decision Engine

Iterative decision simulation. Spend compute to think better. Five persona analysts as independent subagents, anonymized peer review, mechanical convergence — until the answer is robust.

The output is a possibility map: every hypothesis, every first/second-order effect, every council disagreement, every adversarial scenario, every assumption that must hold. The recommendation is one synthesis of that map, written at the end of the brief — never the product, never compressed into the lead.

How this skill is organized

SKILL.md (this file) is the entry point and contract — the rules below are non-negotiable. The full loop protocol lives in references/engine-protocol.md; per-phase protocols live in references/phases/*.md; canonical structure for the brief lives in references/brief-schema.json. The references table at the bottom is the single source of truth for where to read each thing.

Command Routing

triggers: (frontmatter above) is the canonical list. Each routes to a per-command protocol file:

  • /autodecision <decision> (default 2 iterations) → execute references/engine-protocol.md end-to-end
  • /autodecision --iterations N <decision> → 1 = medium (council, no convergence), 2 = full default, 3-5 = deep
  • /autodecision --context file1 [file2 ...] <decision> → attach context documents (Claude Code only). Files are extracted, tagged [D#], and threaded through the full pipeline. See references/phases/scope.md "Context File Extraction".
  • /autodecision:quick <decision> → references/engine-protocol.md "Quick Mode Protocol" section
  • /autodecision:compare "A" vs "B" → quick mode on both, then side-by-side comparison
  • /autodecision:revise {slug} "{change}" → references/phases/revise.md
  • /autodecision:challenge "{action}" → references/phases/challenge.md (adversary-only, ~5 min)
  • /autodecision:summarize {slug} → compress an existing brief to one page
  • /autodecision:publish {slug} [--summary] → references/phases/publish.md
  • /autodecision:plan → Phase 0 (SCOPE) interactive only
  • /autodecision:review → read past runs, compare predictions vs outcomes
  • /autodecision:export → bundle journal + assumptions + briefs into portable archive

Non-Negotiable Rules

  1. NEVER simulate in a vacuum. Phase 1 (GROUND) is mandatory. If WebSearch yields nothing, mark the run UNGROUNDED in the brief header — do not proceed silently.

  2. Phase 1.5 (ELICIT) runs after GROUND, before the loop, unless --skip-elicit. The single biggest quality lever — never default-off.

  3. Each persona runs as a SEPARATE Agent subagent. Genuine context-window independence. Sequential authoring in one context destroys diversity. Non-negotiable.

  4. The main conversation IS the orchestrator. Walk the phases yourself. Spawn agents for parallelizable tasks (5 personas, critique + adversary). NEVER spawn one agent to "run the loop" — that agent can't spawn grandchildren and the council collapses. See engine-protocol.md "Orchestration Model."

  5. The 5 canonical personas are the default. Optimist, Pessimist, Competitor, Regulator, Customer. ELICIT may modify (rename, specify a competitor), add (e.g., "Investor" for fundraising), or remove (e.g., Regulator when irrelevant) personas. Custom personas follow the same structure: optimization objective, blind spot, contrarian question, no-hedging rule. Names defined in references/persona-council.md "Canonical Persona Names."

  6. Every effect carries a stable effect_id, a probability, a probability_range, and explicit assumption keys. The Judge compares by ID across iterations — descriptions drift, IDs don't. No implicit assumptions. Assumption keys are as stable as effect_ids: iteration 2+ personas receive the full all_assumptions map from iter-1's effects-chains.json and MUST reuse keys verbatim for conceptually-identical assumptions. Renaming market_has_demand to market_demand_exists between iterations fakes instability and breaks the Judge's assumption_stability metric. See phases/simulate.md "Assumption Key Stability."

  7. Persona disagreement IS the uncertainty signal. The probability range is the data — never average it away.

  8. Generate 2nd-order effects for ALL 1st-order effects. No probability gate. Tail risks matter most.

  9. Anonymize during peer review. Personas review "Analysis A", "Analysis B" — never by name. Mapping randomized per iteration.

  10. The Decision Brief is for humans. Never emit snake_case, never backtick raw effect_ids in prose. Use the description field. See references/phases/decide.md.

  11. Phase 8.5 (VALIDATE-BRIEF) is mandatory for full/medium/revise/quick. It means literally invoking scripts/validate-brief.py against the schema. Writing a custom inline Python validation script (checking for your own invented section headers, declaring "13/13 passed" against a list you authored) IS NOT Phase 8.5. It is self-certification. Self-certification against invented headers is a HARD protocol violation — it silently lets a structurally broken brief ship. If the named script cannot run (e.g., python3 missing), fall back to the Step 5.5 self-check in phases/decide.md and emit the structural-self-check footer. Do NOT roll your own validator. On HARD_FAIL, re-prompt DECIDE once; if still failing, prepend VALIDATION_FAILED and continue. See references/phases/validate-brief.md.

    Writer must not invent section headers. The brief's H2 structure is defined by references/brief-schema.json and is MANDATORY. Inventing headers like ## Context, ## Decision tilt, ## The possibility map, ## Methodology, ## Analysis Approach (any header not in the schema) is a HARD_FAIL, even if it reads well. Improving readability is the schema's job, not the writer's. Do Step 4a's pre-write checklist before composing a single line.

  12. Stop when the Judge says so. Max iterations configurable (default 2, up to 5). The iteration folders ARE the memory — read previous iteration's convergence-summary.md (≤500 tokens) before starting the next, never the full JSON.

  13. Subagent nesting. If /autodecision runs inside another agent, the Agent tool may be unavailable. STOP and ask the user (full protocol in engine-protocol.md). Never silently degrade.

  14. Read references/engine-protocol.md BEFORE starting any phase. This is not "on demand" — it is the first file you read after SKILL.md. If you have not read engine-protocol.md in this conversation, stop and read it now. The protocol defines the loop structure, file writes, and orchestration model. Without it, you will write a one-shot memo instead of running the actual loop.

  15. Intermediate files are mandatory — no brief without a loop. Before Phase 8 (DECIDE) can start, the run directory MUST contain: config.json (Phase 0), ground-data.md (Phase 1), at least one iteration-{N}/effects-chains.json (Phase 3), and convergence-log.json (Phase 7). If these files do not exist, the loop did not run. Do NOT write a brief from memory or from the context file alone. Go back and run the missing phases. A Decision Brief without upstream data files is fabricated, not analyzed.

  16. The Decision Brief has exactly 16 H2 sections in full mode. They are, in order: ## Executive Summary, ## Data Foundation, ## Hypotheses Explored, ## Effects Map, ## Council Dynamics, ## Minority-View Winners (optional), ## Stable Insights, ## Fragile Insights, ## Adversarial Scenarios, ## Key Assumptions, ## Convergence Log, ## Recommendation, ## Appendix A: Decision Timeline, ## Appendix B: Complete Effects Map, ## Appendix C: Quick Mode vs Full Loop Comparison (optional), ## Sources. Writing a brief with different section names — "Evidence Summary", "Options Considered", "Adversary Findings", "Sensitivity Analysis", "Persona Council Results", "Critique Findings" — is a HARD_FAIL. These are NOT schema headers. Use the exact headers above.

References

Read on-demand as each phase begins. Each phase file opens with a self-describing metadata block (phase number, when it runs, what it reads, what it writes, what gates it has) — always read that block first.

ReferenceFile
Full loop protocolreferences/engine-protocol.md
Progress tracker templates (per mode)references/progress-templates.md
Persona definitions + canonical names + subagent protocolreferences/persona-council.md
Shared persona prompt preamblereferences/persona-preamble.md
Effects chain JSON specreferences/effects-chain-spec.md
Phase 0: Scopereferences/phases/scope.md
Phase 1: Groundreferences/phases/ground.md
Phase 1.5: Elicitreferences/phases/elicit.md
Phase 2: Hypothesizereferences/phases/hypothesize.md
Phase 2.5: Clarifyreferences/phases/clarify.md (iter-1 only; skippable with --skip-clarify)
Phase 3: Simulatereferences/phases/simulate.md
Phase 4: Critiquereferences/phases/critique.md
Phase 5: Adversaryreferences/phases/adversary.md
Phase 6: Sensitivityreferences/phases/sensitivity.md
Phase 7: Convergereferences/phases/converge.md
Phase 8: Decidereferences/phases/decide.md
Phase 8.5: Validate Briefreferences/phases/validate-brief.md
Revise protocolreferences/phases/revise.md
Challenge protocolreferences/phases/challenge.md
Publish protocolreferences/phases/publish.md
Output validation rules (canonical)references/validation.md
Decision Brief template (human view)references/output-format.md
Decision Brief schema (canonical structure)references/brief-schema.json
Decision journal specreferences/journal-spec.md
Assumption library specreferences/assumption-library-spec.md
Templatesreferences/templates/{pricing,expansion,build-vs-buy,hiring}.md

If anything in this file conflicts with references/engine-protocol.md, the protocol file wins — it is canonical for loop mechanics. This file is the entry-point contract; the protocol file is the manual.

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 Autodecision AI skill do?

Auto-Decision Engine: iterative decision simulation using autoresearch principles and a persona council. Decomposes decisions, generates competing hypotheses, simulates first/second-order effects with probabilities, critiques via anonymized peer review, and iterates until insights stabilize mechanically. The output is a possibility map — hypotheses, effects, council disagreements, adversarial scenarios, assumptions — with a recommendation synthesized at the end, not as the product.

Why use Autodecision on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/harshilmathur/autodecision/tree/main/.claude/skills/autodecision. 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 Autodecision?

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 Autodecision?

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

Is the Autodecision AI skill free?

Yes. It is published on GitHub by harshilmathur 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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