show-me-your-work

Maintains an append-only TSV decision log recording choices, evidence, and results for reviewable long-running work.

Updated Aug 22, 2026
One-click install
npx skills add https://github.com/squidllee/skills --skill show-me-your-work-squidllee
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: show-me-your-work
Source: https://github.com/squidllee/skills/tree/main/show-me-your-work
Command: npx skills add https://github.com/squidllee/skills --skill show-me-your-work-squidllee

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve? Long-running or unattended work leaves reviewers unable to reconstruct what was decided, why, and on what evidence without rereading entire transcripts. 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, rationale, evidence pointer, and result, rendered as a sortable table on GitHub or in a terminal. - Safe logging helper: The scripts/log.sh script appends well-formed rows, strips stray tabs and newlines, and neutralizes spreadsheet formula injection from untrusted cell content. - End-of-run audit and cross-model review: Verifies the log against the actual transcript and spawns a subagent on a different model family to flag weak evidence, skipped verification, and risky choices. - Use Case: During a multi-week migration, an agent logs each fork, revert, and verification result to decisions.tsv; a reviewer later opens the committed file in the PR and follows each evidence link to trust the outcome. ## Quick Start Ask the agent to keep a show-me-your-work decision log for this task and append a row for each major decision with its evidence and result.

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, rationale, evidence, 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 human-reviewable decision log?▼

TSV works well because GitHub renders it as a sortable table, spreadsheets open it directly, and terminal tools like column display it cleanly. Each row stays on one line with evidence stored as a pointer such as a commit SHA or file path, not prose.

Should the decision log be committed to git?▼

By default the log stays local as a working artifact 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 cross-language port or multi-week migration.

How does the logging script prevent spreadsheet formula injection?▼

The log.sh script strips tabs, newlines, and carriage returns from every cell, then 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 cross-model review required for the decision trail?▼

Self-review cannot provide fresh eyes on the work, so the skill requires spawning a subagent on a different model family to scan the trail. It flags decisions with weak evidence, skipped verification, and risky choices, and every run ends with an Attention section listing those flags.