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Survey Visuals

Community
WILLOSCAR
survey-visuals

Draft non-prose visuals artifacts (timeline, figure specs) for a survey, grounded in evidence and using citation keys from `citations/ref.bib`.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namesurvey-visuals
Stars
513
Forks
39
Bundled files
5
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Survey Visuals AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/survey-visuals
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/survey-visuals .claude/skills/survey-visuals
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Survey Visuals in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Survey Visuals on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Survey Visuals is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Survey Visuals (timeline + figure specs; NO PROSE)

Triggers & routing

  • Trigger: survey visuals, timeline, figures, visuals, 图表, 时间线, figure spec.
  • Use when: survey 的 C4（NO PROSE），已有 outline + claim/evidence + citations，需要先把时间线/图规格落盘。

This skill creates non-prose artifacts that make the writing stage less template-y:

  • timeline / evolution bullets
  • figure specs (what to draw, why it matters, what papers support it)

Load Order

Always read:

  • references/overview.md
  • references/figure_archetypes.md

Read by task:

  • references/timeline_patterns.md when building timeline milestones

Machine-readable assets:

  • assets/figure_templates.yaml — figure archetype specifications (extensible without code changes)

Script Boundary

Use scripts/run.py only for:

  • deterministic assembly of timeline bullets from paper_notes + bibkeys
  • table generation from outline + mapping
  • figure spec skeleton generation using assets/figure_templates.yaml

Do not treat run.py as the place for:

  • hardcoded figure descriptions or narratives
  • milestone selection heuristics that should be inspectable from references

Tables are handled by dedicated table skills:

  • table-schema -> outline/table_schema.md
  • table-filler -> outline/tables_index.md (internal index)
  • appendix-table-writer -> outline/tables_appendix.md (reader-facing Appendix tables)

Inputs

  • outline/outline.yml
  • outline/claim_evidence_matrix.md
  • papers/paper_notes.jsonl
  • citations/ref.bib

Outputs

  • outline/timeline.md
  • outline/figures.md

Workflow inputs (explicit)

  • Use outline/outline.yml + outline/claim_evidence_matrix.md to decide what to visualize.
  • Use papers/paper_notes.jsonl for year/milestone candidates.
  • Use only citation keys from citations/ref.bib.

Workflow (heuristic)

  1. Read the outline + claim-evidence matrix and pick recurring comparison axes.
  2. Timeline (outline/timeline.md):
    • Write year -> key milestone bullets (aim for breadth and citations).
  3. Figures (outline/figures.md):
    • Write 2-4 figure specs that a human could draw:
      • purpose (what insight this figure communicates)
      • required elements (boxes/arrows/axes)
      • what papers support each element (cite keys)
  4. Use only citation keys present in citations/ref.bib.

Quality checklist

  • No TODO and no <!-- SCAFFOLD ... --> markers remain in the outputs.
  • outline/timeline.md contains >=8 year bullets and each bullet has >=1 citation marker [@...].
  • outline/figures.md contains >=2 figure specs and each mentions at least one supporting citation.

Helper script (optional)

Quick Start

  • uv run python .codex/skills/survey-visuals/scripts/run.py --help
  • uv run python .codex/skills/survey-visuals/scripts/run.py --workspace <workspace>

All Options

  • --workspace <workspace> (required)
  • --unit-id <id> (optional; used only for runner bookkeeping)
  • --inputs <a;b;c> (optional; defaults to the four Inputs listed above)
  • --outputs <timeline_rel;figures_rel> (optional; defaults to outline/timeline.md;outline/figures.md)
  • --checkpoint <C#> (optional; ignored by the helper)

Examples

  • Generate timeline + figures with defaults:

    uv run python .codex/skills/survey-visuals/scripts/run.py --workspace <workspace>

  • Generate to custom output paths:

    uv run python .codex/skills/survey-visuals/scripts/run.py --workspace <workspace> --outputs outline/timeline.md;outline/figures.md

Notes

  • The helper is intentionally minimal and never overwrites non-placeholder artifacts.
  • In strict mode it blocks only if placeholder markers remain (and if minimum timeline/figure requirements are not met).

Troubleshooting

Issue: timeline is thin or citation-free

Fix:

  • Prefer fewer, higher-signal milestones, but ensure each bullet has >=1 [@...].
  • Route upstream if notes are thin: strengthen paper-notes / evidence-draft rather than padding.

Issue: figure specs read like prose

Fix:

  • Keep specs as draw-instructions: purpose + elements + what each element is supported by (cite keys).
  • Move narrative explanation into the main text; this file should stay non-prose.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Survey Visuals AI skill do?

Draft non-prose visuals artifacts (timeline, figure specs) for a survey, grounded in evidence and using citation keys from `citations/ref.bib`.

Why use Survey Visuals on TypingMind?

Because you install it once and use it with any model. Survey Visuals is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Survey Visuals in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/survey-visuals. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Survey Visuals?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Survey Visuals?

As many as you like. As long as a model supports skills, you can use Survey Visuals with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Survey Visuals AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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