agentleFS
ianklassenianklassen/ai-pm-system/skills/decision-logger/SKILL.md

decision-logger

Skill1 starsChanged 7 months agoMITView on GitHub

Capture decision traces — the reasoning behind key PM decisions. Use after PRD approvals, scope changes, launch/kill calls, prioritization shifts, or any significant product decision.

5 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use andreessen, business-investment-advisor and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Hussain-ElfalahSkill

    andreessen

    Also in 4 projects that use this

  • Hussain-ElfalahSkill

    business-investment-advisor

    Also in 4 projects that use this

  • csiddhant796-blipSkill

    karpathy-coder

    Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles — surface assumptions before coding, keep it simple, make surgical changes, define verifiable goals. Triggers on "review my diff", "check complexity", "am I overcomplicating this", "karpathy check", "before I commit", or any code quality concern where the LLM might be overcoding.

    Also in 4 projects that use this

  • csiddhant796-blipSkill

    self-eval

    Honestly evaluate AI work quality using a two-axis scoring system. Use after completing a task, code review, or work session to get an unbiased assessment. Detects score inflation, forces devil's advocate reasoning, and persists scores across sessions.

    Also in 4 projects that use this

  • +8 more from Hussain-Elfalah

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 7 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    After a PRD is approved or rejected

    line 12
  2. Distinctive:

    After a scope change or pivot

    line 13
  3. Very specific:

    After a launch/kill/iterate decision on an experiment

    line 14
  4. Distinctive:

    After a prioritization shift (quarterly planning, reprioritization)

    line 15
  5. Distinctive:

    After a significant stakeholder alignment (exec review, cross-functional agreement)

    line 16
  6. Distinctive:

    When asked "log this decision", "record why we decided this", or "decision trace"

    line 17
  7. Distinctive:

    Ask if not obvious from context:

    line 23
  8. Distinctive:

    Date: When the decision was made

    line 32

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Decision Logger

Capture the reasoning behind key product decisions as structured, append-only traces. This builds the "event clock" — the record of why things became true, not just what is true now.

When to Use

  • After a PRD is approved or rejected
  • After a scope change or pivot
  • After a launch/kill/iterate decision on an experiment
  • After a prioritization shift (quarterly planning, reprioritization)
  • After a significant stakeholder alignment (exec review, cross-functional agreement)
  • When asked "log this decision", "record why we decided this", or "decision trace"

Process

1. Identify the Decision

Ask if not obvious from context:

  • What was decided? (1 sentence)
  • What triggered this decision? (signal, meeting, data, escalation)

2. Capture the Trace

Gather the following — from conversation context, user input, or by asking:

  • Decision: What was decided (1 sentence)
  • Date: When the decision was made
  • Context: What prompted this — the signal, conversation, or data point
  • Alternatives considered: What other options were on the table (at least 2)
  • Evidence: What data, insight, or customer signal drove the call
  • Stakeholders: Who was involved, who approved, who dissented
  • Expected outcome: What we expect to happen as a result
  • Risk / reversal cost: How hard is this to undo if wrong
  • Tags: strategic-pillar, metric, segment, experiment (for retrieval)

3. Write the Entry

Save to memory/decisions/YYYY-MM-DD_short-slug.md using this format:

# [Decision title]

**Date:** YYYY-MM-DD
**Type:** [PRD approval | Scope change | Launch/Kill | Prioritization | Alignment | Other]
**Tags:** [pillar, metric, segment]

## Decision
[1-2 sentences: what was decided]

## Context
[What prompted this — the trigger]

## Alternatives Considered
1. **[Option A]** — [brief description, why not chosen]
2. **[Option B]** — [brief description, why not chosen]

## Evidence
- [Data point, customer insight, or signal that drove the call]

## Stakeholders
- **Decision maker:** [who]
- **Consulted:** [who]
- **Informed:** [who]

## Expected Outcome
[What we expect to happen, and how we'll know]

## Risk / Reversal Cost
[How hard is this to undo — low/medium/high, and why]

4. Confirm with User

Show the drafted entry. Ask the user to confirm or adjust before saving.

Output

A timestamped decision trace saved to memory/decisions/. Append-only — never overwrite existing entries.

Over time, these traces enable:

  • Recall: "Why did we decide X?"
  • Pattern detection: "We've killed 3 pricing experiments in a row"
  • Drift detection: "Strategy says X but decisions are moving toward Y"
  • Counterfactual reasoning: "What if we'd chosen the alternative?"

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.