show-me-your-work

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

136|8|Updated May 9, 2026
One-click install
npx skills add https://github.com/Sma1lboy/rove --skill show-me-your-work-sma1lboy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: show-me-your-work
Source: https://github.com/Sma1lboy/rove/tree/main/.agents/skills/pstack/skills/show-me-your-work
Command: npx skills add https://github.com/Sma1lboy/rove --skill show-me-your-work-sma1lboy

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 script: scripts/log.sh appends well-formed rows, writes the header on first use, strips stray tabs and newlines, and escapes spreadsheet formula characters to prevent formula injection. - Audit and cross-model review: End-of-run self-audit against the transcript plus a mandatory subagent review from a different model family, ending with an "Attention" section of flagged risks. - 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 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 coding agent?▼

Keep a single TSV decision log with one row per decision recording timestamp, phase, decision, rationale, evidence pointer, and result. Append rows with the included log.sh helper so formatting stays consistent and the trail can be reviewed after the run.

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 and evidence cells hold pointers like commit SHAs or file paths rather than prose.

Should the decision log be committed to git?▼

By default the log stays local as a working artifact, such as decisions.tsv in the work directory. Commit it only when the work is ambitious enough that a reviewer needs the trail to trust the result, like a large migration, since a committed TSV renders as a table in the PR.

Why does the logging script prefix some cells with a single quote?▼

Cells starting with =, +, -, or @ are prefixed with a single quote so spreadsheets do not interpret them as formulas. This prevents formula execution when a reviewer opens a log containing generated or user-supplied text like PR titles or filenames.

What are the limitations of a self-audited decision log?▼

Self-review can miss weak evidence or risky choices because the same model that did the work judges it. The skill therefore requires a subagent from a different model family to review the trail and flag gaps before handoff.