A three-stage reviewer for AI-generated work: it defines suitable checks, evaluates the result, and creates a visual report. It can review visualizations, code, documents, conversations, or skill output.

Use it to score an AI-generated artifact, record evidence, list fixes, and track before-and-after quality.

It gives a structured way to find quality problems and separate broad issues from individual mistakes.

evaluate — Skill by careerhackeralex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/careerhackeralex/visualize/eval

Any agent

npx skills add careerhackeralex/visualize --skill eval

Clone the repo

git clone --depth 1 https://github.com/careerhackeralex/visualize

Made for: Claude Code, Codex.

Per session 113 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,356 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00113	$0.02356
Opus 5	$0.00056	$0.01178
Sonnet 5	$0.00023	$0.00471
Haiku 4.5	$0.00011	$0.00236

Measured 2d ago against content hash cc64ca3fd802, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

evaluate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

eval/SKILL.md · 229 lines

How it starts

The opening of the file, as written. The whole thing — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Evaluate

Comprehensive quality evaluation for any AI-generated artifact. Produces its report as a visualization.

How It Works

┌──────────────────────────────────────────────┐
│                                              │
│  Phase 1: SPEC GENERATION                    │
│  Analyze the artifact type                   │
│  Generate tailored evaluation criteria       │
│  Define scoring dimensions + weights         │
│  Set quality gates                           │
│           │                                  │
│           ▼                                  │
│  Phase 2: EVALUATION                         │
│  Run automated checks (when possible)        │
│  Visual/manual inspection                    │
│  Score each dimension with evidence          │
│  Identify systemic vs local issues           │
│           │                                  │
│           ▼                                  │
│  Phase 3: REPORT (via /visualize)            │
│  Generate a beautiful HTML eval report       │
│  Scores, charts, screenshots, fix list       │
│  Radar chart of dimensions                   │
│  Before/after tracking                       │
│                                              │
└──────────────────────────────────────────────┘

Phase 1: Spec Generation

For any artifact, generate evaluation specs by analyzing:

1. Identify Artifact Type

HTML Visualization → visual design, interactivity, technical, content, shareability
Code/Project → correctness, readability, architecture, test coverage, performance
Document/Report → clarity, structure, accuracy, completeness, tone
Conversation/Agent → helpfulness, accuracy, tone, efficiency, safety
Slide Deck → all visualization dims + narrative flow, persuasion, pacing
Dashboard → data accuracy, information density, scannability, actionability
Custom → derive dimensions from the skill's SKILL.md and stated goals

2. Generate Dimensions

For each artifact type, produce 6-10 evaluation dimensions. Each dimension needs:

Name — short, clear label
Description — what this dimension measures
Weight — percentage (all weights sum to 100%)
Scoring anchors — what does a 10, 8, 6, 4 look like?
Automated checks — any programmatic tests (if applicable)
Deductions — specific issues and their point costs

Read the full file on GitHub · 229 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 229 lines · 113 tokens per session scan A cc64ca3fd802

Subscribe to this mod's changes

evaluate is a skill published in the GitHub repository careerhackeralex/visualize (218 stars, last pushed 5mo ago), licensed MIT. It adds 113 tokens to every session and 2,356 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Analytics and metrics Code review ai claude-code dashboards html infographics open-source

pr-to-video

Turn a GitHub pull request (a PR URL, owner/repo#N, or 'this PR' in a checked-out repo) into a code-change explainer video — changelog, feature reveal, fix, or refactor walkthrough built from the diff, commits, and files: the input is a code change, not a website. Not a product promo (/product-launch-video) or a no-PR…

heygen-com/hyperframes · 104 tokens

general-video

Author or edit a custom HyperFrames composition when no specialized workflow fits, or when BRIEF.md sets flow: companion. Use for longer or multi-scene pieces, brand and sizzle reels, montages, static loops, static title cards, footage remixes, and freeform builds. Use motion-graphics instead for a short unnarrated…

heygen-com/hyperframes · 92 tokens

motion-graphics

A short, design-led motion graphic where motion is the message — kinetic typography, stat count-up, chart/data-viz hit, logo sting / brand lockup, lower-third / callout / social overlay, animated map (highlight regions, connect places, zoom to a location), animated tweet / news-article / headline, webpage / UI…

heygen-com/hyperframes · 139 tokens

metrics-instrumentation

Specification for instrumenting an opik-backend workflow with operational OpenTelemetry metrics — per-stage throughput/latency/error counters and native histograms, dimensioned per-customer (workspace). Use when a pipeline (scoring, ingestion, experiments, jobs) needs per-stage visibility. Covers metric emission only…

comet-ml/opik · 93 tokens

captions-overlay

Overlay doctrine for the embedded-captions workflow — the caption MODEL (drop / rail / embed) and the rule that captions are an OVERLAY composited on top of the film, never a reserved bottom band you shift content up to avoid. Load when adding captions/subtitles to a talking-head or launch video, when deciding whether…

heygen-com/hyperframes · 136 tokens

review-agents-md

Audit Dograh AGENTS.md files for drift against the live repo and for bad scope boundaries between parent and child docs. Use when the user asks to review existing AGENTS files, identify stale guidance, decide whether a subtree needs its own AGENTS.md, or update the AGENTS.md hierarchy under the repo root, api/, or ui/.

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