Agent skill · Code Review & Quality

critic

Review a finished Data2Story blog against the 5 quality rubric dimensions (visual_design, narrative_pacing, data_method_transparency, claim_data_alignment, insight_value), score each 1-7 with on-page evidence, and emit critic.json with pass/fail + targeted, surgical send-back instructions. Verifies every load-bearing claim/asset against its traceability chain before scoring; applies the caveat-survival, honest-accuracy, and third-party-attribution caps. Does NOT rewrite content — scores and sends back. Use at Stage 6.5 after the Auditor and after verify.py has produced verifier.json; re-runs e

QinghongLingithub.com/QinghongLinGitHub ↗
claude-codecan modify filesMIT
Install
npx skills add QinghongLin/data2story-skill --skill critic --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 18 KB
Bundled scripts: none
Allowed tools: Bash(*)ReadWriteGrep
Path: skills/data2story-pro/critic/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 149
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

Scores a finished Data2Story article against five rubric dimensions (visual_design, narrative_pacing, data_method_transparency, claim_data_alignment, insight_value), using on-page evidence and traceability from verifier.json, and emits critic.json containing pass/fail plus targeted send-back instructions. It verifies each load-bearing claim/asset against its traceability chain and applies caveat-survival, honest-accuracy, and third-party-attribution caps. It does NOT rewrite content and runs at Stage 6.5 after Auditor and verify.py have produced verifier.json; re-runs each revision round. Triggered when a built index.html plus verifier.json exist, or when you determine whether the article is actually good.

How it works

  • Read index.html (the finished article prose) and verifier.json, analyst.json, editor.json, designer.json, detective.json. Rubric is from references/rubric.json.
  • For each dimension, read how the article handles that aspect and verify load-bearing claims by tracing data lineage in verifier.json to code lines, data_tables, or source URLs. If evidence is not resolvable, score cannot exceed 3 for transparency and claim-data alignment.
  • Check for the Runnable verifier enhancement: ensure in-page run capability and reproducible notebook exist to support data_method_transparency.
  • Apply caveat_survival: detect material caveats from analyst/detective/editor inputs and ensure they survive into visible prose, otherwise cap the relevant dimensions at 3.
  • Score each dimension on a 1-7 scale anchored at 3, citing concrete evidence.
  • Determine overall pass/fail: every dimension must be >=4, and at least one dimension >=5 (signature move). If all dims are >=4 but none reach 5, prepare a flagship-lift send-back targeting a single dimension and a concrete honest-axis move. For each failing dimension, provide a surgical send-back with role, exact section/asset to change, minimal fix, and why.
  • Write critic.json with the required shape including overall and five dimensions, ensuring the 'pass' logic and tier rules are followed.

When to use it

  • Triggered when a built index.html and verifier.json exist, or when judging whether the article is actually good.

What it can touch

  • Uses the allowed tools Bash, Read, Write, Grep to inspect index.html and verifier.json and craft critic.json. It references the rubrics and chart references as evidence anchors but does not modify source assets.

Caveats

  • It does not rewrite content and relies on stage-6.5 gating; it requires verifier.json produced previously by verify.py. It enforces the five-dimension evaluation with explicit evidence and surgical fixes, and returns critic.json with the round information and a single round assessment per cycle.
From the SKILL.md

# Critic Your job is **quality review**, like a demanding editor-in-chief. You read the finished article, score it against five fixed rubric dimensions, and hand back **specific, minimal fixes** for whatever falls short. You do **not** rewrite content yourself — you score and you send back. This role exists because the pipeline otherwise has no quality gate: the Auditor only fixes layout, the Inspector only checks traceability. You are the only step that judges whether the article is actually *good*. ## Setup - `PROJECT_DIR` = first argument. - Read **`index.html`** (the finished article — read the prose, not just tags), plus `verifier.json`, `analyst.json`, `editor.json`, `designer.json`, `detective.json`. (`verifier.json` is produced by `verify.py` at **Stage 6.4**, before the Critic, so the traceability index is available when you score.) - The rubric is **[`references/rubric.json`](references/rubric.json)** — the 5 dimensions, the 1-7 scale anchored at 3, the score-gates, the global anti-leniency rules R1-R7, the per-dimension `send_back_to` role, and the ethos. Read it fully before scoring. ## Step 1: Score each dimension (evidence-checked) For each of the five dimensions (`vi

What's inside
Steps it walks through
  1. Setup
  2. Step 1: Score each dimension (evidence-checked)
  3. Step 2: Decide pass/fail + write targeted send-backs
  4. Step 3: Write critic.json
  5. Rules (the ethos — read rubric.json ethos in full)
  6. Output
Ships with 1 file
  • references/rubric.json
More from data2story-skill
All skills →
About this skill
What does the critic skill do?

Review a finished Data2Story blog against the 5 quality rubric dimensions (visual_design, narrative_pacing, data_method_transparency, claim_data_alignment, insight_value), score each 1-7 with on-page evidence, and emit critic.json with pass/fail + targeted, surgical send-back instructions. Verifies every load-bearing claim/asset against its traceability chain before scoring; applies the caveat-survival, honest-accuracy, and third-party-attribution caps. Does NOT rewrite content — scores and sends back. Use at Stage 6.5 after the Auditor and after verify.py has produced verifier.json; re-runs e

How do I install it?

Run `npx skills add QinghongLin/data2story-skill --skill critic --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From QinghongLin/data2story-skill, a repository with 149 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going