research-visuals
Research visual references and build annotated moodboards for visual creative work across media.
Skill by JoseArron
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/josearron-research-visuals
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About
Use cases: research
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/research-visuals
What's inside
- Skill name
research-visuals- Description
- Research visual references and build annotated moodboards for visual creative work across media. Use when choosing an aesthetic, comparing art territories, or investigating visual identity references.
- License
- MIT
- Instructions
- 34 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- openai.yaml · agents/
SKILL.md (first lines)
# Research visuals
Find references that help the user make a visual decision. An annotated board explains what to borrow from each reference and what to leave behind.
## Start with the decision
Read the project's selected art contract, identity notes, owner feedback, and relevant experiments first. Separate selected direction, candidate direction, and rejected work. A convenient preview or recent file does not supersede an explicit decision.
Identify the visual decision: form, imagery, material, typography, composition, setting, or the whole identity. Use the audience, intended feeling, output medium, and production constraints already supplied. Ask only for a missing choice that changes the research.
## Collect usable evidence
- Choose reference territories from the brief's subject, audience, medium, and desired traits. Prefer creators' portfolios, official work or product pages, press kits, studio breakdowns, museum collections, and original publications. Trace reposted images to their creator when practical.
- Inspect the actual visual before describing it. If you can access only text, label that limit; do not invent a visual analysis.
- Record creator, work, source URL or local path, and the specific image, screen, or timestamp examined. For live references, verify the link.
- Describe visible traits: silhouette, proportion, contour, value grouping, palette relationships, material, lighting, type hierarchy, framing, and density. Separate observation from your proposed interpretation.
- Separate subject matter from visual treatment. Carry only the traits relevant to the request; a reference's setting, objects, or culture need not become the output's subject.
- For cultural references, identify the place, community, object, or practice represented. Verify language and heritage claims rather than treating a national label as a visual style.
## Build a useful comparison
Group references into distinct directions when the user is exploring. Change the underlying visual grammar, not just the palette. Each direction needs a rationale, useful traits, traits to exclude, and production implications.
When a board helps, use an editable HTML page or the requested format with labeled reference images and source links. Keep originals distinguishable from your generated studies. Use permitted embeds, supplied local images, or links; do not present a generated imitation as source evidence. If the request is research only, a cited comparison can be enough.
For each reference, answer: **What does this help us decide?** Omit attractive images with no role in that decision. Note when references contradict one another instead of implying that all their traits fit together.
## Deliver
Give a supported recommendation and annotated reference set. Mark unresolved choices as candidates. Save durable research in the project's established location, preferably `{agentRoot}/research/`, when useful. Follow the configured agent root; a one-off board needs no setup ceremony.
When the user selects a direction, use `define-art-direction` to turn it into production rules. Load its entrypoint through the harness's skill mechanism, or read `SKILL.md` directly when no mechanism is available. Research does not itself approve a direction.From JoseArron/skills, MIT.
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choose-skill
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 11, 2026 (22 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/JoseArron/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from JoseArron
- 0.603score
build-asset-library
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Produce and organize a consistent reusable visual asset set with editable sources, exports, a manifest, and a preview gallery.
SkillAFreshness: A★ 2Claude Code +9 - 0.598score
design-motion
JoseArron
Design or implement motion for creative work across media, including animation, graphic sequences, interfaces, and interactive pieces.
SkillAFreshness: A★ 2Claude Code +9 - 0.598score
concrete-naming
JoseArron
Naming and comment conventions that keep code readable without a change log baked into it — subject-first names for data (itemsCharged not chargedItems)…
SkillAFreshness: A★ 2Claude Code +9 - 0.598score
create-procedural-art
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Create or refine visuals through code, including SVG, Canvas, shaders, procedural 2D or 3D geometry, and generative graphics.
SkillAFreshness: A★ 2Claude Code +9 - 0.598score
define-art-direction
JoseArron
Define or revise art direction for visual creative work across media.
SkillAFreshness: A★ 2Claude Code +9 - 0.598score
design-sound
JoseArron
Design sound effects, soundscapes, audio cues, sonic identity, and audio-generation briefs across media.
SkillAFreshness: A★ 2Claude Code +9
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Cite this page
research-visuals skill. Agent Mart. https://agentmart.dev/skills/josearron-research-visuals (last updated 2026-10-24)