Paper Source Trace logo

Paper Source Trace

Organization
AMinerOrg
paper-source-trace

Use when a user provides an academic paper PDF, extracted paper text, citation contexts, or references and asks for paper source tracing, claim-centered source tracing, citation intent extraction, entity/relation extraction, contribution mapping, citation graph JSON, SVG/HTML citation maps, or optional AMiner metadata/citation enrichment. Do NOT use this skill for simple paper lookup by title, scholar search, or lightweight academic queries — use aminer-free-academic or aminer-academic-search for those instead.

Overview

PublisherAMinerOrg
Repositoryaminer-open-skill
Skill namepaper-source-trace
Stars
66
Forks
19
Bundled files
11
LicenseMIT
Links
  • 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.

  • 11 bundled files

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

  • Open source

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

Installation

Install the Paper Source Trace 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/AMinerOrg/aminer-open-skill/tree/main/skills/paper-source-trace
  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/AMinerOrg/aminer-open-skill.git /tmp/aminer-open-skill
mkdir -p .claude/skills
cp -r /tmp/aminer-open-skill/skills/paper-source-trace .claude/skills/paper-source-trace
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Source Trace 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 Paper Source Trace 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 Paper Source Trace 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.

Paper Source Trace

Paper Source Trace turns one target paper into grounded, claim-centered source-tracing artifacts. It keeps one canonical skill name, paper-source-trace.

Invoke it through natural language or /paper-source-trace.

Language Routing

  • Use SKILL.zh.md when the user mainly writes in Chinese or explicitly requests Chinese output.
  • Use this SKILL.md for all other requests.
  • Keep json/graph/citation_graph.json keys, intent labels, relation types, source roles, and parameter names in English in both workflows.

English Workflow

Standard Artifacts

Produce these artifacts when evidence and filesystem access allow:

  • analysis.md: human-readable report.
  • json/graph/citation_graph.json: canonical machine-readable graph following references/schema.md.
  • citation_map.svg: static citation map when SVG generation is possible.
  • citation_map.html: single-file interactive graph when graph data is available.
  • citation_map_chain.svg: chain-style source trace SVG only when svg: both.
  • citation_map_spec.md: only when SVG generation has caveats or fails.

Default output directory:

text
outputs/paper-source-trace/<safe-paper-stem>/

Preferred layout:

text
analysis.md
citation_map.svg
citation_map.html
citation_map_chain.svg          # only when svg is both
citation_map_spec.md            # only when SVG generation has caveats or fails
json/graph/citation_graph.json
json/aminer/*.json              # only when AMiner raw results are saved
json/extraction/*.json          # only when structured intermediates are saved

Legacy compatibility: if the user writes mode: current, treat it as svg: radial; mode: example as svg: chain; and mode: all as svg: both. If svg and mode are both supplied and conflict, ask the user to confirm the intended SVG output. hybrid, interactive graph, or expandable knowledge graph means the user wants the standard citation_map.html; it is not an SVG mode and does not change the SVG choice.

Startup Confirmation

Before reading the paper, extracting citations, checking AMINER_API_KEY, calling AMiner, or generating SVG, ask the user to confirm:

  • SVG output: radial, chain, or both.
  • AMiner enrichment: on or off.

If the user already supplied one or both values, restate them as provisional and still ask for final confirmation. Stop until the user answers.

If interactive confirmation is genuinely impossible, use the recommended defaults: svg: both and aminer: on. Still follow the high-cost confirmation rule before any AMiner call estimated at ¥5 or more; if AMINER_API_KEY is missing, skip AMiner enrichment and continue local analysis.

Core Rules

  1. Use only supplied paper text, citation contexts, reference entries, user notes, or explicitly requested AMiner results as evidence.
  2. Do not infer citation intent from domain memory alone.
  3. AMiner enrichment is explicit opt-in only. Do not check AMINER_API_KEY unless requested.
  4. AMiner may enrich IDs, URLs, candidate reference matches, and external citation relationships, but it cannot replace local citation contexts or prove intent/source traces by itself.
  5. Preserve uncertainty; lower confidence when citation context, reference matching, or source role evidence is incomplete.
  6. Keep citation_sentence and context in the source language of the target paper. They are original evidence anchors, not localized explanations.
  7. Use the user's output language for evidence, summary, notes, Markdown report text, and visible SVG/HTML UI.
  8. If output files can be written, do not stop at a chat-only summary.

AMiner Enrichment

When confirmation is possible, treat AMiner as off unless the user confirms aminer: on or explicitly writes enhance with AMiner, use AMiner metadata, complete paper_id, or trace AMiner citation relationships. When confirmation is genuinely impossible, the recommended fallback is aminer: on.

When enabled:

  1. Check only whether AMINER_API_KEY exists; never print the token.
  2. If missing, continue local analysis and record that enrichment was skipped.
  3. Use the shortest viable chain: paper_search or paper_search_pro, paper_detail, paper_relation, and paper_info.
  4. Output a cost summary for all planned or completed AMiner calls.
  5. If estimated cost is ¥5 or more, ask for explicit confirmation before paid calls.
  6. Record enrichment metadata under metadata.aminer_enrichment in json/graph/citation_graph.json.

Intent Labels

Use only these labels unless the user explicitly extends the taxonomy:

background, problem, core-method, supporting-method, dataset, metric, baseline, tool-resource, theory, result-evidence, limitation, future-work.

Execution Steps

  1. Ask the startup confirmation question and wait for the answer.
  2. Resolve input evidence and output directory.
  3. Extract reliable paper text, reference entries, and citation contexts.
  4. Read references/evidence_protocol.md before important classification or source tracing.
  5. Classify citations with the allowed intent labels.
  6. Extract key target-paper claims or contributions when supported by text.
  7. Build source_traces[] by linking claims to local citation contexts, cited-source roles, and evidence steps.
  8. Extract entities and relations that explain the target paper.
  9. If AMiner is enabled, enrich metadata without replacing local evidence.
  10. Write json/graph/citation_graph.json before visual artifacts.
  11. Write analysis.md; use references/analysis_template.md only for explicit template or fixed-format requests.
  12. Generate citation_map.html and the confirmed SVG output with one renderer command: python3 scripts/render_html.py --graph <output>/json/graph/citation_graph.json --output <output>/citation_map.html --svg <radial|chain|both> --language auto.
  13. Do not hand-write ad hoc SVG or HTML; both static SVG modes and the HTML radial / chain views must share the renderer layout, colors, language pack, text wrapping, node priority, and edge rules.
  14. Validate artifacts before the final reply.

Reference Files

  • references/schema.md: canonical schema and JSON example.
  • references/evidence_protocol.md: evidence and uncertainty rules.
  • references/prompts.md: English and Chinese extraction/review prompts.
  • references/visual.md: SVG and HTML graph rules.
  • references/analysis_template.md: fixed report templates for explicit template mode.
  • scripts/render_html.py: standard graph renderer. Use it for citation_map.html, citation_map.svg, and citation_map_chain.svg; do not hand-write ad hoc SVG or HTML.

Quality Checks

  • Every citation has intent, evidence, confidence, and either reference_id or unmatched_reference: true.
  • Every intent label is in the allowed list.
  • Key citations trace back to a citation sentence or local context.
  • citation_sentence and context preserve the target paper's original language; only explanation fields are localized.
  • Every entity is supported by at least one citation.
  • Every source trace is grounded in at least one local citation context; AMiner metadata cannot be the sole support.
  • Generate citation_map.html and SVG maps with scripts/render_html.py after json/graph/citation_graph.json is written. HTML and SVG must use one visible language, the same canonical group labels, the same node ranking, and the same reduced-edge layout strategy.
  • Static SVG should use only useful main edges, summarize dense groups with +N, and avoid citation-to-citation cross-links or AMiner-only relation lines.
  • json/graph/citation_graph.json remains complete even if Markdown, SVG, or HTML has caveats.

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 Paper Source Trace AI skill do?

Use when a user provides an academic paper PDF, extracted paper text, citation contexts, or references and asks for paper source tracing, claim-centered source tracing, citation intent extraction, entity/relation extraction, contribution mapping, citation graph JSON, SVG/HTML citation maps, or optional AMiner metadata/citation enrichment. Do NOT use this skill for simple paper lookup by title, scholar search, or lightweight academic queries — use aminer-free-academic or aminer-academic-search for those instead.

Why use Paper Source Trace on TypingMind?

Because you install it once and use it with any model. Paper Source Trace 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 Paper Source Trace in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AMinerOrg/aminer-open-skill/tree/main/skills/paper-source-trace. 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 Paper Source Trace?

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 Paper Source Trace?

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

Is the Paper Source Trace AI skill free?

Yes. It is published on GitHub by AMinerOrg under the MIT license. 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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