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Literature Engineer

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WILLOSCAR
literature-engineer

Multi-route literature expansion + metadata normalization for evidence-first surveys.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameliterature-engineer
Stars
513
Forks
39
Bundled files
4
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.

  • 4 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 Literature Engineer 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/literature-engineer
  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/literature-engineer .claude/skills/literature-engineer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Literature Engineer 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 Literature Engineer 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 Literature Engineer 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.

Literature Engineer (evidence collector)

Triggers & routing

  • Trigger: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance.
  • Use when: Workflow 需要按锁定的 retrieval policy 扩充候选文献并补齐可追溯 metadata。

Goal: build a large, verifiable candidate pool for downstream dedupe/rank, mapping, notes, citations, and drafting.

This skill is intentionally evidence-first: if you can't reach the target size with verifiable IDs/provenance, the correct behavior is to block and ask for more exports / enable network, not to fabricate.

Load Order

Always read:

  • references/domain_pack_overview.md — how domain packs drive topic-specific behavior

Domain packs (loaded by topic match):

  • assets/domain_packs/llm_agents.json — pinned classic/survey arXiv IDs for LLM agent topics

Script Boundary

Use scripts/run.py only for:

  • multi-route offline import, normalization, and provenance tagging
  • online arXiv/Semantic Scholar API retrieval
  • snowball expansion and deduplication
  • retrieval report generation

Do not treat run.py as the place for:

  • hardcoded pinned arXiv ID lists (use domain packs)
  • hardcoded topic detection logic (use domain packs)

Inputs

  • queries.md
    • keywords, exclude, max_results, time window
  • Optional offline sources (any combination; all are merged):
    • papers/import.(csv|json|jsonl|bib)
    • papers/arxiv_export.(csv|json|jsonl|bib)
    • papers/imports/*.(csv|json|jsonl|bib)
  • Optional snowball exports (offline):
    • papers/snowball/*.(csv|json|jsonl|bib)

Outputs

  • papers/papers_raw.jsonl
    • 1 record per line; minimum fields:
      • title (str), authors (list[str]), year (int|""), url (str)
      • stable identifier(s): arxiv_id and/or doi
      • abstract (str; may be empty in offline mode)
      • source (str) + provenance (list[dict])
  • papers/papers_raw.csv (human scan)
  • papers/retrieval_report.md (route counts, missing-meta stats, next actions)

Workflow (multi-route)

  1. Offline-first merge: ingest all available offline exports (and label provenance per file).
  2. Online retrieval (optional): if enabled, run arXiv API retrieval for each keyword query.
  3. Snowballing (optional): expand from seed papers via references/cited-by (online), or merge offline snowball exports.
  4. Normalize + dedupe: canonicalize IDs/URLs, merge duplicates while unioning provenance.
  5. Report: write a concise retrieval report with coverage buckets and missing-meta counts.

Quality checklist

  • Candidate pool meets the active Workflow's declared retrieval floor without fabrication. For Workflows with retrieval_policy.minimum_records, use that value; survey profiles may instead derive a stricter pool target from core_size.
  • Each record has a stable identifier (arxiv_id or doi, plus url).
  • Each record has provenance: which route/file/API produced it.

Script

Quick Start

  • uv run python .codex/skills/literature-engineer/scripts/run.py --help

All Options

  • See uv run python .codex/skills/literature-engineer/scripts/run.py --help.
  • Reads retrieval config from queries.md.
  • Offline inputs (merged if present): papers/import.(csv|json|jsonl|bib), papers/arxiv_export.(csv|json|jsonl|bib), papers/imports/*.(csv|json|jsonl|bib).
  • Optional offline snowball inputs: papers/snowball/*.(csv|json|jsonl|bib).
  • Online expansion requires network: use --online and/or --snowball.
  • Online retrieval is best-effort: arXiv API can be flaky in some environments; the script will also attempt a Semantic Scholar route when needed.
  • For LLM-agent topics, the script also performs a best-effort pinned arXiv id_list fetch (canonical classics like ReAct/Toolformer/Reflexion/Voyager/Tree-of-Thoughts + a small prior-survey seed set) so ref.bib can include must-cite anchors even when keyword search misses them.
  • If HTTPS/TLS to external domains is unstable, the Semantic Scholar route is fetched via the r.jina.ai proxy so the pipeline can still self-boot without manual exports.
  • When an online run returns 0 records due to transient network errors, a simple rerun is often sufficient (the pipeline should not fabricate).

Examples

  • Offline imports only:

    • Put exports under papers/imports/ then run:
      • uv run python .codex/skills/literature-engineer/scripts/run.py --workspace <workspace>

  • Explicit offline inputs (multi-route):

    • uv run python .codex/skills/literature-engineer/scripts/run.py --workspace <workspace> --input path/to/a.bib --input path/to/b.jsonl

  • Online arXiv retrieval (needs network):

    • uv run python .codex/skills/literature-engineer/scripts/run.py --workspace <workspace> --online

  • Snowballing (needs network unless you provide offline snowball exports):

    • uv run python .codex/skills/literature-engineer/scripts/run.py --workspace <workspace> --snowball

Troubleshooting

Issue: cannot reach the active Workflow's candidate-pool target

Symptom:

  • papers/papers_raw.jsonl is below the explicit or profile-derived minimum declared by the locked Workflow.

Causes:

  • Only a small offline export was provided.
  • Network is blocked so online retrieval/snowballing can't run.

Solutions:

  • Provide additional exports under papers/imports/ (multiple routes/queries).
  • Provide snowball exports under papers/snowball/.
  • Enable network and rerun with --online --snowball.

Issue: many records missing stable IDs

Symptom:

  • Report shows many entries with empty arxiv_id and doi.

Solutions:

  • Prefer arXiv/OpenReview/ACL exports that include stable IDs.
  • If you have network, rerun with --online to backfill arXiv IDs.
  • Filter out ID-less entries before downstream citation generation.

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 Literature Engineer AI skill do?

Multi-route literature expansion + metadata normalization for evidence-first surveys.

Why use Literature Engineer on TypingMind?

Because you install it once and use it with any model. Literature Engineer 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 Literature Engineer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/literature-engineer. 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 Literature Engineer?

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 Literature Engineer?

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

Is the Literature Engineer 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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