show-me-your-work

Maintains an append-only TSV decision log recording what, why, evidence, and result for reviewable work.

1|Updated Aug 26, 2026
One-click install
npx skills add https://github.com/edivad1999/stuc-stack --skill show-me-your-work-edivad1999
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: show-me-your-work
Source: https://github.com/edivad1999/stuc-stack/tree/main/skills/show-me-your-work
Command: npx skills add https://github.com/edivad1999/stuc-stack --skill show-me-your-work-edivad1999

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Long-running or unattended agent work leaves reviewers unable to reconstruct what was decided, why, and on what evidence without rereading the entire transcript. This Skill keeps a single canonical decision trail so a human can audit the run after the fact. ## Core Features & Use Cases - Structured TSV decision log: One row per decision with timestamp, phase, decision, why, evidence, and result columns, rendered as a sortable table on GitHub or in a terminal. - Safe logging helper: The scripts/log.sh script stamps timestamps, writes the header on first use, strips stray tabs and newlines, and escapes spreadsheet formula characters in generated cells. - End-of-run audit and cross-model review: The log is checked against the run transcript for truthfulness, and a subagent on a different model family flags weak evidence, skipped verification, and risky choices in an Attention section. - Use Case: During a multi-week migration, log each fork chosen, unit completed, and revert triggered to decisions.tsv, then commit the log so the PR reviewer can follow the evidence pointers row by row. ## Quick Start Ask the agent to start a show-me-your-work decision log for this task and record each major decision with its evidence as the run progresses.

Frequently Asked Questions about show-me-your-work

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I keep an audit trail of decisions made by an AI agent?▼

Keep a single TSV decision log with one row per decision recording timestamp, phase, decision, reason, evidence pointer, and result. Append rows at decision points and checkpoints using the log.sh helper, then audit the log against the run transcript before handing back.

What format works best for a reviewable decision log?▼

A single TSV file works best because GitHub renders it as a sortable table, spreadsheets and column -t read it, and rows append with one command. Cells stay single-line and evidence is a pointer like a commit SHA or file:line, never prose.

Should the decision log be committed to git?▼

By default the log stays local as a working artifact at decisions.tsv or under .audit/ and is left out of git. Commit it only when the work is ambitious enough that a reviewer needs the trail to trust the result, such as a large port or multi-week migration.

How does the log prevent spreadsheet formula injection?▼

The log.sh helper strips tabs, newlines, and carriage returns from every cell and prefixes any cell starting with =, +, -, or @ with a single quote. This prevents attacker-controlled text like PR titles from executing as formulas when a reviewer opens the log in a spreadsheet.

Why is a cross-model review required before handing back?▼

Self-review cannot provide fresh eyes, so a subagent on a different model family reads the trail and transcript to flag weak evidence, skipped verification, and risky choices. Every reply for a run with a trail ends with an Attention section listing the reviewer model and its flags.