show-me-your-work

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

Updated Jul 29, 2026
One-click install
npx skills add https://github.com/mmdmcy/fluttAIrbar --skill show-me-your-work-mmdmcy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: show-me-your-work
Source: https://github.com/mmdmcy/fluttAIrbar/tree/main/plugins/pstack/skills/show-me-your-work
Command: npx skills add https://github.com/mmdmcy/fluttAIrbar --skill show-me-your-work-mmdmcy

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 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 prefixes spreadsheet-formula characters to prevent formula injection from generated text. - End-of-run auditing: Walks the log against the run transcript to remove invented rows, verify evidence resolves, and add missing pivots, then requires a cross-model subagent review that flags risks in an "Attention" section. - 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 confirm the work. ## Quick Start Use the show-me-your-work skill to keep a TSV decision log for this multi-phase refactoring run and audit it against the transcript before handing back.

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 for long-running AI agent work?▼

Keep a single TSV decision log with one row per decision recording timestamp, phase, decision, rationale, evidence pointer, and result. Append rows at forks, completed units, reverts, and blockers rather than logging every trivial action.

What format works best for a decision log reviewers can read?▼

TSV works well because GitHub renders it as a sortable table, spreadsheets open it directly, and `column -s$'\t' -t` displays it in a terminal. Each row appends with one command and cells stay single-line.

Should the decision log be committed to git?▼

By default keep it as a local working artifact at decisions.tsv or under .audit/ and leave it 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 migration.

How does the log script prevent spreadsheet formula injection?▼

The log.sh helper 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 opened in a spreadsheet.

Why does the skill require a cross-model review before handoff?▼

Self-review cannot provide fresh eyes on the work, so a subagent on a different model family scans the trail and transcript for weak evidence, skipped verification, and risky choices. Its flags appear in an Attention section ending every reply for the run.