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.
npx agentmods add skills/gtmify/aigtm/decision-lognpx skills add GTMify/aigtm --skill decision-loggit clone --depth 1 https://github.com/GTMify/aigtmWrote 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.
[](https://agentmods.dev/skills/gtmify/aigtm/decision-log)<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>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.
| Model | Per session | Once 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 |
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.
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.mdlinking 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]
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.
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.
- 4d ago First seen · 112 lines · 61 tokens per session scan A b623c63d6064
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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