# Itsdevcoffee Devcoffee Agent Skills Feedback > feedback - Skill: `tomevault-io/itsdevcoffee-devcoffee-agent-skills-feedback` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/itsdevcoffee-devcoffee-agent-skills-feedback` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/itsdevcoffee-devcoffee-agent-skills-feedback/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/itsdevcoffee-devcoffee-agent-skills-feedback --- # feedback Evaluate TLDR command outputs against quality criteria and log results for improvement tracking. This is a development tool for improving the `/tldr` skill itself. ## Path Resolution All evaluation data lives inside the TLDR plugin directory. This skill is at `skills/feedback/SKILL.md` — the plugin root is two directories up (the directory containing `skills/`, `docs/`, and `.claude-plugin/`). All file paths below are relative to the plugin root. - Scoring rubrics: `skills/feedback/references/EVALUATION.md` - Evaluation log: `docs/evaluation/evaluation-log.md` - Sample files: `docs/evaluation/samples/NNN-description.md` - Notes catalog: `docs/evaluation/notes.md` ## Evaluation Workflow ### Step 1: Identify the Sample Determine the input mode: - **Interactive (default):** User pastes or references an original message and its TLDR output - **Automated (`--no-user-score`):** Called from `/tldr:note --ex` — skip user prompts, agent scores only ### Step 2: Load Scoring Rubrics Read `references/EVALUATION.md` (bundled with this skill) for the complete scoring criteria, detailed rubrics for each dimension, and the sample file format template. ### Step 3: Determine Next Sample ID Read `docs/evaluation/evaluation-log.md` and find the highest existing sample ID. The new sample is that number + 1, zero-padded to 3 digits (e.g., `004`). ### Step 4: Score the Sample Evaluate on four criteria (Completeness, Conciseness, Actionability, Accuracy) at 2.5 points each — detailed rubrics are in `references/EVALUATION.md`. For each criterion, produce: (1) items captured correctly, (2) items missed or needing improvement, (3) a numeric score with one-line justification. ### Step 5: Provide Analysis Include in the evaluation: - **What Went Well** — Specific strengths - **What Needs Work** — Specific weaknesses - **Recommendations** — Actionable improvements for future TLDR versions ### Step 6: Create Sample File Write to `docs/evaluation/samples/NNN-description.md` following the format template in `references/EVALUATION.md`: - Header with date, type, context - Original message (or URL reference if external) - TLDR output - Claude's evaluation with breakdown - User's evaluation section (populated or "Not provided") ### Step 7: Update Evaluation Log Update `docs/evaluation/evaluation-log.md`: 1. Increment "Samples Collected" count 2. Recalculate all averages (overall + per-criterion) 3. Add row to the evaluation samples table 4. Update "Samples with User Feedback" count if user scored ### Step 8: User Score Handling **Interactive mode (default):** 1. Present the agent's score and analysis 2. Ask the user for their score (0.0-10.0) and optional feedback 3. If user provides a score, update the sample file and evaluation log **Automated mode (`--no-user-score`):** - Skip user prompts entirely - Mark sample status as "Unscored" in the log - Set user evaluation to "Not provided" / "None" ## Output After logging, provide a brief summary in chat: ``` Sample 004 logged (8.5 / 10.0) - Strengths: Captured all key findings and file paths accurately - Needs work: Two bullets could be more action-oriented - Running tally: 4 samples, 9.1 avg, 1 with user feedback Saved to docs/evaluation/samples/004-auth-refactor.md ``` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/itsdevcoffee) — claim your Tome and manage your conversions.