# Coordinator:eval Output

> Scores a research output against the 5-criteria rubric via Sonnet.

- Skill: `dbc-oduffy/coordinator-eval-output` (Agent Skill)
- Install (CLI): `npx skillmds@latest add dbc-oduffy/coordinator-eval-output`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dbc-oduffy/coordinator-eval-output/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: dbc-oduffy (https://skillmd.com/u/dbc-oduffy)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dbc-oduffy/coordinator-eval-output

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# Evaluate Research Output

## Usage
/coordinator:eval-output <path-to-research-output>

## Process
1. Read the eval rubric at `${CLAUDE_PLUGIN_ROOT}/pipelines/deep-research/eval-rubric.md`
2. Read the research output at the provided path
3. Dispatch a Sonnet agent (model: sonnet, tools: Read, WebFetch, Write) with:
   - The rubric
   - The research output
   - Instruction to: read the output, sample 3-5 cited URLs via WebFetch to verify citation accuracy, score each of the 5 criteria with a 0.0-1.0 score and 2-3 sentence justification, provide overall pass/marginal/fail grade
4. Present the scores to the PM

## Notes
- This is a post-hoc quality check, not a gate. Use it to calibrate prompt improvements.
- Start by running it on recent pipeline outputs to establish a baseline.
- Anthropic found a single LLM call with a single prompt was most consistent.