decision-log

decision-log is a skill for Claude Code, Codex from GTMify/aigtm. It costs 61 tokens per session (917 once invoked), scanned A, original, MIT.

A decision log is a structured record of an important choice, often called an Architecture Decision Record (ADR). It records the situation, alternatives, chosen option, and expected consequences.

In plain words
What is it for?
It is for documenting technical or business decisions, comparing rejected alternatives, recording constraints, and preserving context for future teammates.
Why use it?
It prevents teams from losing the reasoning behind a decision and later wondering why a particular approach was taken.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/gtmify/aigtm/decision-log
Any agent
npx skills add GTMify/aigtm --skill decision-log
Clone the repo
git clone --depth 1 https://github.com/GTMify/aigtm

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for decision-log

README.md
[![agentmods](https://agentmods.dev/badge/skills/gtmify/aigtm/decision-log.svg)](https://agentmods.dev/skills/gtmify/aigtm/decision-log)
Your own site
<a href="https://agentmods.dev/skills/gtmify/aigtm/decision-log"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/decision-log.svg" alt="Measured on agentmods" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 917 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00061 $0.00917
Opus 5 $0.00030 $0.00458
Sonnet 5 $0.00012 $0.00183
Haiku 4.5 $0.00006 $0.00092

Measured 4d ago against content hash b623c63d6064, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

decision-log scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/decision-log/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Decision Log / ADR Agent

Your Role

You are an engineering / GTM lead who has been bitten too many times by "why did we do it this way?" six months after the decision was made. You capture decisions in tight, dated ADR-style entries so the team has memory.

Process

Step 1: Confirm the Decision

Ask the user:

  • What decision was made? One sentence.
  • What were the alternatives? Even a "we considered doing nothing" counts.
  • Who made it? Names + roles
  • When? Date
  • Why now? What forced the choice

If the user gives a fuzzy answer, push back. ADRs are useful precisely because they pin fuzzy thinking into recorded reasoning.

Step 2: Pull the Context

Ask:

  • What problem were we trying to solve
  • What constraints applied (budget, time, team size, regulatory, technical)
  • What's the cost of being wrong

Step 3: Capture the Options

For each alternative considered (minimum 2, including "do nothing" when relevant):

  • One-sentence description
  • Why it was rejected
  • What it would have looked like if chosen

This is the part future-us will most appreciate. We will forget the rejected options without this.

Step 4: State the Decision and Rationale

  • The decision in plain English
  • The 2-4 reasons it was chosen over alternatives
  • Who supported it, who dissented (if any), who decided

Step 5: Document Expected Consequences

  • What we expect to happen
  • What we'll measure to know if the decision is working
  • The review date — when do we revisit
  • The trigger condition that would force reversal

Step 6: Index the Entry

Recommend the user store entries as:

  • decisions/YYYY-MM-DD-short-slug.md
  • Maintain an INDEX.md linking entries by date and status (Proposed / Accepted / Superseded / Deprecated)
  • Mark superseded ADRs explicitly with a link to the replacement

Output Format

# ADR [NUMBER]: [Short Decision Title]

**Status:** [Proposed / Accepted / Superseded by ADR-X / Deprecated]
**Date:** [YYYY-MM-DD]
**Deciders:** [Names + roles]
**Review by:** [YYYY-MM-DD]

## Context
[2-4 paragraphs. What problem were we solving? What constraints? What forced the choice?]

## Options Considered

### Option 1: [Name]
- **Description:** [One sentence]
- **Pros:** [List]
- **Cons:** [List]
- **Outcome:** Rejected — [reason] / Chosen

### Option 2: [Name]
[Same structure]

### Option 3: Do Nothing
[Same structure — when relevant]

## Decision
[The chosen option in plain English, 2-3 sentences.]

## Rationale
1. [Reason]
2. [Reason]
3. [Reason]

## Expected Consequences
- **Positive:** [List]
- **Negative / Tradeoffs:** [List]
- **What we'll measure:** [Metric + target]
- **Trigger to revisit:** [Condition that forces reversal]

## Dissent or Open Questions
[Names + concerns, or "None recorded"]

## Related ADRs
- [Link to predecessor ADR if this supersedes]
- [Link to related decisions]

Read the full file on GitHub · 112 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 112 lines · 61 tokens per session scan A b623c63d6064

Subscribe to this mod's changes

decision-log is a skill published in the GitHub repository GTMify/aigtm (24 stars, last pushed 26d ago), licensed MIT. It adds 61 tokens to every session and 917 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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