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Decide

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alirezarezvani
decide

/cs:decide <memo> — Log a decision to two-layer memory via decision-logger. Approved memo becomes durable; raw transcripts kept for reference. Use when the founder has approved a boardroom memo and the decision must become durable company memory — e.g. right after /cs:boardroom concludes.

Overview

Publisheralirezarezvani
Repositoryclaude-skills
Skill namedecide
Stars
28K
Forks
3.9K
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 alirezarezvani on GitHub. Read the source before you install it.

Installation

Install the Decide 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/alirezarezvani/claude-skills/tree/main/c-level-agents/skills/decide
  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/alirezarezvani/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/c-level-agents/skills/decide .claude/skills/decide
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decide 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 Decide 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 Decide 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.

/cs:decide — Log the Decision

Command: /cs:decide <memo-path>

Logs the founder's decision via the decision-logger skill. This is the gate where in-session deliberation becomes durable company memory.

Pipeline Position

/cs:office-hours  →  /cs:brief  →  /cs:boardroom  →  /cs:decide  →  /cs:execute  →  /cs:post-mortem
                                                       ↑ you are here

Two-Layer Memory Model

The decision-logger skill maintains two layers:

  1. Raw transcripts — every boardroom session, every advisor's Phase 2 position, every dissent. Stored under ~/.claude/decisions/raw/. Reference only, never feeds back automatically.
  2. Approved decisions — only the founder-signed memos. Stored under ~/.claude/decisions/approved/. Feeds into future /cs:office-hours and /cs:founder-mode calls.

This split prevents the system from "remembering" unresolved debates as if they were decisions.

Input

A board memo file (output of /cs:boardroom).

Workflow

  1. Read the memo path
  2. Verify it has founder approval (status: APPROVED)
  3. Extract structured decision record:
    • Decision title
    • Date decided
    • Option chosen
    • Success + kill criteria
    • Dissent (preserved)
    • Review checkpoint date
  4. Append to ~/.claude/decisions/approved/<YYYY-MM-DD>-<slug>.md
  5. Update the raw transcript pointer
  6. If llm-wiki bridge configured, write to vault (~/company-vault/10-decisions/)
  7. Schedule auto-revisit (90 days)

Output Record Format

markdown
# Decision: <title>
**Decided:** YYYY-MM-DD
**By:** <founder name>
**Memo:** <link to boardroom memo>
**Brief:** <link to original brief>
**Review checkpoint:** YYYY-MM-DD (90d default)

## Decision
**Chose:** <option>
**Rejected:** <other options + one-line why>

## Success Criteria (binding)
- <metric, threshold, timeframe>

## Kill Criteria (binding)
- <metric, threshold, action>

## Preserved Dissent
- **<dissenter>:** <unresolved concern>
- (preserved verbatim; dissent never erased)

## Next Action
- `/cs:execute` → 90-day plan due <date>

## Status History
- YYYY-MM-DD: APPROVED

Why Preserved Dissent

The biggest risk in approved decisions is forgetting why someone disagreed. When the kill criteria trigger, the dissent often turns out to have been correct. Preserving it verbatim — not summarized — keeps the company honest at post-mortem time.

Routing

  • /cs:execute <decision> — build the 90-day plan
  • /cs:freeze <decision> <days> — lock if irreversible
  • (Auto-scheduled) /cs:post-mortem <decision> — at 90-day checkpoint

Stale-Decision Audit

cs-chief-of-staff runs a weekly stale audit:

  • Decisions > 90 days without revisit → flag for /cs:post-mortem
  • Decisions with kill criteria triggered → flag immediately
  • Decisions whose company-context.md basis has changed → flag for re-examination

Related


Version: 1.0.0

Frequently asked questions

What does the Decide AI skill do?

/cs:decide <memo> — Log a decision to two-layer memory via decision-logger. Approved memo becomes durable; raw transcripts kept for reference. Use when the founder has approved a boardroom memo and the decision must become durable company memory — e.g. right after /cs:boardroom concludes.

Why use Decide on TypingMind?

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

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

Which AI models can use Decide?

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 Decide?

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

Is the Decide AI skill free?

Yes. It is published on GitHub by alirezarezvani 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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