Decision Log

Decision Log

Structured recording of human decisions made during AI-assisted development. Each entry captures what AI recommended, what you chose, and why — the judgment that AI cannot replicate.

Quick start

/decision-log "Chose PostgreSQL over MongoDB for the user data store"
/decision-log                    # Interactive: asks what you decided
/decision-log --review           # Review and reflect on past decisions

Why log decisions?

Three reasons, in order of importance:

1. Build judgment. Reviewing past decisions (and their outcomes) is how you

develop product intuition. Six months from now, you'll see which calls were right and which were wrong — and why.

1. Prove judgment. In interviews or reviews, "I used AI to build this" sounds

like you're a middleman. "Here are the 12 decisions I made that AI couldn't, and why" shows irreplaceable value.

1. Improve process. If most decisions are "AI said X, I went with X", your

workflow isn't challenging you enough. If most are overrides, your AI setup may need tuning.

Logging a decision

When the user describes a decision (or invokes /decision-log), structure it:

### Decision: [short title]
**Date:** [auto]
**Context:** [what was being decided]

**Option A:** [what AI/source 1 recommended]
**Option B:** [what AI/source 2 recommended, or an alternative considered]

**Chosen:** [A / B / hybrid / something else entirely]
**Reasoning:** [why — the human context, intuition, or constraint that drove this]
**What AI couldn't weigh:** [the factor that required human judgment]

**Confidence:** High / Medium / Low
**Revisit by:** [optional: date to check if this was the right call]

Append to decision-log.md in the project root. Create the file if it doesn't exist.

Entry types

Mark each entry with a type tag so they can be filtered later:

• [PRODUCT] — product direction, feature scope, user prioritization
• [TECHNICAL] — stack choice, architecture, data model
• [STRATEGY] — market positioning, pricing, go-to-market
• [PROCESS] — workflow, tool choice, team structure
• [REVIEW] — AI-assisted review finding (not a human decision — labeled separately)

The [REVIEW] type is for recording AI evaluation results (like blind review findings or user simulation outputs). These are valuable records but should be clearly distinguished from human decisions.

Interactive mode

When invoked without arguments, ask:

1. "What did you just decide?"
2. "What were the alternatives?"
3. "Why did you go this direction?"
4. "What's the one thing AI couldn't factor in?"

Format the answers into the standard template and append.

Review mode (--review)

Read the existing decision-log.md and provide:

1. Decision count by type
2. Override rate — how often did the user diverge from AI recommendations?
3. Confidence distribution — are most decisions High or Low confidence?
4. Patterns — recurring themes in "what AI couldn't weigh"
5. Revisit flags — any decisions past their revisit date

Present as a brief summary, then ask: "Any of these decisions you'd make differently now?"

Output files

| File | Purpose | |------|---------| | decision-log.md | Append-only decision record |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: wenboxia
• Source: wenboxia/decision-log
• License: MIT

Install and usage instructions live in the source repository linked above.