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Paper Glossary

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917Dhj
paper-glossary

Use when building reusable Obsidian glossary notes from an existing paper source manifest, optionally with a raw-sections override, especially when a reader needs a reviewed shortlist before glossary notes and article links are changed.

Overview

Publisher917Dhj
RepositoryDeepPaperNote
Skill namepaper-glossary
Stars
1.2K
Forks
80
Bundled files
22
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.

  • 22 bundled files

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

  • Open source

    Published by 917Dhj on GitHub. Read the source before you install it.

Installation

Install the Paper Glossary 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/917Dhj/DeepPaperNote/tree/main/skills/paper-glossary
  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/917Dhj/DeepPaperNote.git /tmp/DeepPaperNote
mkdir -p .claude/skills
cp -r /tmp/DeepPaperNote/skills/paper-glossary .claude/skills/paper-glossary
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Glossary 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 Glossary 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 Glossary 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 Glossary

Build shared glossary notes from *_source_manifest.json. --source-manifest is always required; --raw-sections only overrides its raw_sections_path with an explicit *_raw_sections.jsonl. This skill never runs or modifies a paper-reading workflow; the manifest and raw-sections file are its only paper-content boundary. See references/file-contract.md for JSON and CLI contracts.

Preview And Wait (Default)

  1. Show saved configuration on first use per person/device. If absent, ask for a term directory inside an Obsidian vault and configure it; reuse valid configuration later. Setup may create ~/.paper-glossary/config.json.
  2. Require an explicit article Markdown path when article links are requested. Validate it and the configured term directory are in the same Obsidian vault; never infer an article path.
  3. Run deterministic proposal from the effective body.
  4. Perform exactly one grounded host semantic review, bounded by shortlist_limit. It may only drop or reorder candidates and must preserve each retained proposal candidate's exact term string.
  5. Record the reviewed shortlist with plan_glossary.py --review-proposal PROPOSAL --reviewed-terms NAMES; this validates the saved proposal against the current paper source and preserves each full candidate, ordered surface_forms, and provenance. If review produces an empty reviewed shortlist, report no_candidates and stop without presenting a selector.
  6. Present every retained term from that reviewed artifact as a numbered Markdown list. This is the terminal, Codex, and Claude Code interaction. Show the resolved term directory, the article Markdown path (or that none was requested), and that selection authorizes glossary writes/enrichment plus first-safe-occurrence article links when an article was supplied.
  7. State: No glossary notes or article Markdown have been written before selection. End the response and wait. Do not triage, inventory, generate, link, or lint during preview.

A broad request, manifest, raw sections path, or article path is not selection approval. A host-native selector is allowed only when it displays the complete same list in one interaction.

After Selection

Accept only numbers, exact term names, or 全部写入 from the immediately preceding numbered list. Resolve them to the exact displayed term strings before invoking a script; 全部写入 applies only to that list. Invalid selections receive the valid range and another wait. Triage requires both --reviewed-shortlist REVIEW and the resolved exact names in --terms; never use --terms alone or add alias syntax.

Pass the current --source-manifest, optional --raw-sections, and saved --reviewed-shortlist to inventory, writer, and linker. Inventory consumes the saved --triage artifact as its selected-term input; writer and linker must also receive that same saved --triage. They require exact ordered term and surface_forms equality with this independent authorization before any glossary or article write.

  1. Triage the selection; its selection identity binds each exact selected name and ordered paper-grounded forms.
  2. Run library inventory from that triage artifact; it recomputes the complete proposal/review/selection provenance chain and fails closed on mismatched paper, source, shortlist, or forms.
  3. Then generate one action-aware batch.
  4. Run one writer invocation against the configured glossary directory; it revalidates the same provenance chain and requires its ordered inventory results to match the authorized triage exactly, then performs whole-batch preflight followed by the create/enrich/reuse commit. The writer resolves device-local configuration (or an explicit --config-path for that device) and does not accept a standalone write destination. With --article, its backlink is derived from the resolved article Markdown stem. Without an article, the glossary-only backlink comes from the validated manifest paper_id. The successful artifact preserves triage_sha256, provenance, article context, ordered mappings, and their deterministic mappings_sha256; the mapping digest binds the triage identity.
  5. Link each successful writer result at its first safe occurrence only if an article Markdown was supplied/requested. The linker authenticates the writer provenance, context, digest, note paths, stems, and forms against the same current source/review, and rejects an artifact with no bound article path. For a glossary-only request, skip link_glossary_terms.py.
  6. Lint writer-returned changed glossary note files whose action is created, enriched, or updated in one lint_glossary.py invocation with repeated --input PATH arguments. Do not pass article Markdown to lint_glossary.py.
  7. Report observable wall-clock timing and statuses. Time host-only phases separately. Each CLI emits top-level elapsed_ms for its own complete invocation. The single writer invocation includes whole-batch preflight and commit. Do not report separate preflight and commit timings.

new entries are created; existing_thin entries receive only missing structured fields and a missing occurrence; existing_complete entries may receive only a missing occurrence. Do not overwrite existing note content. See references/file-contract.md for existing_thin, entry operations, writer forms, and link statuses.

Grounding

  • anchor_only: concise paper use plus a thin general explanation.
  • needs_explanation: labeled background explanation with confidence and paper occurrence.
  • Exclude reference-only occurrences as evidence. Keep paper facts in occurrence and outside knowledge labeled.

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 Glossary AI skill do?

Use when building reusable Obsidian glossary notes from an existing paper source manifest, optionally with a raw-sections override, especially when a reader needs a reviewed shortlist before glossary notes and article links are changed.

Why use Paper Glossary on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/917Dhj/DeepPaperNote/tree/main/skills/paper-glossary. 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 Glossary?

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 Glossary?

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

Is the Paper Glossary AI skill free?

Yes. It is published on GitHub by 917Dhj 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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