Decision Logger logo

Decision Logger

CommunityPopular
alirezarezvani
decision-logger

Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked automatically by the board-meeting skill after Phase 5 founder approval.

Overview

Publisheralirezarezvani
Repositoryclaude-skills
Skill namedecision-logger
Stars
26.1K
Forks
3.7K
Bundled files
2
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Decision Logger 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/alirezarezvani/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/c-level-advisor/skills/decision-logger .claude/skills/decision-logger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decision Logger 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 Decision Logger 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 Decision Logger 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.

Decision Logger

Two-layer memory system. Layer 1 stores everything. Layer 2 stores only what the founder approved. Future meetings read Layer 2 only — this prevents hallucinated consensus from past debates bleeding into new deliberations.

Keywords

decision log, memory, approved decisions, action items, board minutes, /cs:decisions, /cs:review, conflict detection, DO_NOT_RESURFACE

Quick Start

bash
python scripts/decision_tracker.py --demo             # See sample output
python scripts/decision_tracker.py --summary          # Overview + overdue
python scripts/decision_tracker.py --overdue          # Past-deadline actions
python scripts/decision_tracker.py --conflicts        # Contradiction detection
python scripts/decision_tracker.py --owner "CTO"      # Filter by owner
python scripts/decision_tracker.py --search "pricing" # Search decisions

Commands

CommandEffect
/cs:decisionsLast 10 approved decisions
/cs:decisions --allFull history
/cs:decisions --owner CMOFilter by owner
/cs:decisions --topic pricingSearch by keyword
/cs:reviewAction items due within 7 days
/cs:review --overdueItems past deadline

Two-Layer Architecture

Storage follows the canonical two-layer decision memory (see ../agent-protocol/SKILL.md → "Decision Memory (Canonical Layout)") — the same layout /cs:decide writes.

Layer 1 — Raw Transcripts

Location: ~/.claude/decisions/raw/YYYY-MM-DD-<slug>.md

  • Full Phase 2 agent contributions, Phase 3 critique, Phase 4 synthesis
  • All debates, including rejected arguments
  • NEVER auto-loaded. Only on explicit founder request.
  • Archive after 90 days → ~/.claude/decisions/raw/archive/YYYY/

Layer 2 — Approved Decisions

Location: ~/.claude/decisions/approved/ — one record per decision (YYYY-MM-DD-<slug>.md) plus the append-only index decisions.md

  • ONLY founder-approved decisions, action items, user corrections
  • Loaded automatically in Phase 1 of every board meeting
  • Append-only. Decisions are never deleted — only superseded.
  • Managed by Chief of Staff after Phase 5. Never written by agents directly.

Migration: a legacy memory/board-meetings/ folder may exist from earlier versions; read it for history but write all new entries to ~/.claude/decisions/.

Decision Entry Format

markdown
## [YYYY-MM-DD] — [AGENDA ITEM TITLE]

**Decision:** [One clear statement of what was decided.]
**Owner:** [One person or role — accountable for execution.]
**Deadline:** [YYYY-MM-DD]
**Review:** [YYYY-MM-DD]
**Rationale:** [Why this over alternatives. 1-2 sentences.]

**User Override:** [If founder changed agent recommendation — what and why. Blank if not applicable.]

**Rejected:**
- [Proposal] — [reason] [DO_NOT_RESURFACE]

**Action Items:**
- [ ] [Action] — Owner: [name] — Due: [YYYY-MM-DD] — Review: [YYYY-MM-DD]

**Supersedes:** [DATE of previous decision on same topic, if any]
**Superseded by:** [Filled in retroactively if overridden later]
**Raw transcript:** ~/.claude/decisions/raw/[DATE]-<slug>.md

Conflict Detection

Before logging, Chief of Staff checks for:

  1. DO_NOT_RESURFACE violations — new decision matches a rejected proposal
  2. Topic contradictions — two active decisions on same topic with different conclusions
  3. Owner conflicts — same action assigned to different people in different decisions

When a conflict is found:

⚠️ DECISION CONFLICT
New: [text]
Conflicts with: [DATE] — [existing text]

Options: (1) Supersede old  (2) Merge  (3) Defer to founder

DO_NOT_RESURFACE enforcement:

🚫 BLOCKED: "[Proposal]" was rejected on [DATE]. Reason: [reason].
To reopen: founder must explicitly say "reopen [topic] from [DATE]".

Logging Workflow (Post Phase 5)

  1. Founder approves synthesis
  2. Write Layer 1 raw transcript → ~/.claude/decisions/raw/YYYY-MM-DD-<slug>.md
  3. Check conflicts against ~/.claude/decisions/approved/decisions.md
  4. Surface conflicts → wait for founder resolution
  5. Write the approved record to ~/.claude/decisions/approved/YYYY-MM-DD-<slug>.md and append to the index decisions.md
  6. Confirm: decisions logged, actions tracked, DO_NOT_RESURFACE flags added

Marking Actions Complete

markdown
- [x] [Action] — Owner: [name] — Completed: [DATE] — Result: [one sentence]

Never delete completed items. The history is the record.

File Structure

~/.claude/decisions/
├── raw/YYYY-MM-DD-<slug>.md        # Layer 1: full transcript per meeting
├── raw/archive/YYYY/               # Raw files after 90 days
├── approved/YYYY-MM-DD-<slug>.md   # Layer 2: one record per approved decision
└── approved/decisions.md           # Layer 2 index: append-only, founder-approved

References

  • templates/decision-entry.md — single entry template with field rules
  • scripts/decision_tracker.py — CLI parser, overdue tracker, conflict detector

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 Decision Logger AI skill do?

Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked automatically by the board-meeting skill after Phase 5 founder approval.

Why use Decision Logger on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alirezarezvani/claude-skills/tree/main/c-level-advisor/skills/decision-logger. 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 Decision Logger?

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 Decision Logger?

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

Is the Decision Logger 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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