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Light Citation

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Light0305
light-citation

Verify scholarly references and claim-citation support for Light stage 10. Use when auditing a manuscript, claim map, bibliography, DOI/arXiv/PMID/ISBN/URL, BibTeX/CSL, citekeys, chimeric or fabricated citations, retraction/correction alerts, or preparing a canonical citation registry for typesetting. Builds provenance-preserving inventories, confirms metadata with independent authoritative sources, distinguishes CONFIRMED/CONFIRMED-MISSING/UNAVAILABLE/UNRESOLVED, records Crossref update direction, and emits the citation gate plus delivery artifacts.

Overview

PublisherLight0305
RepositoryLight-skills
Skill namelight-citation
Stars
641
Forks
75
Bundled files
15
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.

  • 15 bundled files

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

  • Open source

    Published by Light0305 on GitHub. Read the source before you install it.

Installation

Install the Light Citation 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/Light0305/Light-skills/tree/master/skills/light-citation
  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/Light0305/Light-skills.git /tmp/Light-skills
mkdir -p .claude/skills
cp -r /tmp/Light-skills/skills/light-citation .claude/skills/light-citation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Light Citation 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 Light Citation 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 Light Citation 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.

Citation — stage 10

Make every author-used reference traceable, real, correctly identified, and honestly connected to the claim it is supposed to support. Treat formatting, metadata existence, publication-integrity facts, and semantic support as four different questions.

Read citation-resource-map.md before a full manuscript audit. Read references/registry_contract.md before producing or consuming the canonical registry. Read references/locator_audit.md before judging claim support.

Stage contract

  • Consume paper-writing's light.paper_claims.v1, exact claim/source locators, current claim text hashes, reviewer-bound citation-support hashes, manuscript, figures/tables, supplement, and reference candidates.
  • Reuse literature-search provenance as input evidence; independently verify the references the author actually keeps.
  • Emit light.citation_registry.v1, BibTeX, CSL JSON, source-level evidence, failure records, claim-edge review, citekey audit, and light.findings.v1(producer=citation).
  • Keep stage 10's only critical gate citation_verify: confirmed missing works and chimeric citations. Metadata conflicts, unavailable sources, claim relevance, formatting, and publication-update alerts are warn/skip.
  • Fix citation failures inside stage 10. ROUTES has no key 10; never invent a back-edge.
  • Hand references.bib, citekey-audit.json, registry and delivery.json to typesetting. Typesetting compiles and checks keys; it does not re-decide authenticity. delivery.json must declare status and per-deliverable sha256: hashes so typesetting can reject stale or edited handoffs.

Non-negotiable rules

  1. Never invent a reference, DOI, author, year, venue, citekey, locator or support verdict.
  2. Never call a network failure, rate limit, 5xx, authentication failure, or index miss “this work does not exist.”
  3. Assign CONFIRMED-MISSING only when DOI Registration Agency explicitly reports non-existence and Crossref plus DataCite both return 404/410.
  4. Assign CONFIRMED only when registration metadata and at least one independent field source confirm the work. Otherwise retain UNAVAILABLE or UNRESOLVED.
  5. Preserve every source's title/authors/year/venue/identifier value. Select canonical fields from the registration agency; never use fuzzy matching to overwrite the original citation.
  6. Treat a real DOI paired with conflicting title/authors/year as a possible chimeric citation. Multi-core-field conflict is critical; one ambiguous field mismatch is a warning for manual review.
  7. Read publication updates in the correct direction: the affected original carries Crossref updated-by; the notice carries inverse update-to.
  8. Record retraction/correction/expression-of-concern facts and alerts, then hand integrity judgment and wording to research-ethics. Do not rebuild its ethics gate.
  9. Never equate metadata confirmation or “A cites B” with “B supports claim C.” SUPPORTS requires a locator-backed semantic review.
  10. Keep the core path local/free or public/no-key. OpenAlex and keyed Semantic Scholar are optional coverage enhancements; commercial managers, institutional databases, and browser plugins are never required.

Execute the author workflow

1. Inventory every reference occurrence

Use the claim map, manuscript, figures/tables and supplement together:

bash
python scripts/citation_registry.py \
  --claim-map claim_plan.json \
  --draft paper.tex \
  --figure figure_notes.md \
  --supplement supplement.md \
  --refs-spec citation_input.json \
  --out-dir citation-delivery

Preserve claim_id, exact claim, claim locator, artifact path, occurrence locator, citekey and raw identifier. Use templates/citation_input.json when the project has no reference spec.

2. Normalize without erasing versions

Normalize DOI, arXiv, PMID, ISBN and URL. Deduplicate exact works. Keep arXiv, accepted manuscript, versioned dataset and published DOI as explicit versions under one work only when the inputs establish the relationship. If not established, keep them separate and mark the relationship unresolved.

3. Verify with independent sources

bash
python scripts/verify_refs.py \
  --spec citation_input.json \
  --self-author Smith \
  --out verify.json

Use Crossref or DataCite as the registration source. Use PubMed, Semantic Scholar, or configured OpenAlex as the independent field source. Keep each endpoint, HTTP status, outcome, timestamp and returned fields in source_evidence,并保存实际参与判定的 normalized-field SHA-256。Crossref polite-pool email、OpenAlex/Semantic Scholar key 只用于请求,绝不写入 endpoint、registry 或日志。

Interpret status exactly:

StatusMeaningAction
CONFIRMEDregistration source + independent field sourceeligible for canonical BibTeX/CSL
CONFIRMED-MISSINGDOI RA explicit non-existence + Crossref/DataCite 404critical; replace or delete
UNAVAILABLEtransport/rate/auth/5xx prevents lookupretry or use another free source
UNRESOLVEDpartial evidence or second source incompleteinvestigate; do not call verified

4. Resolve field conflicts and chimeras

Compare the author's cited title, authors and year against registration metadata. Inspect field_conflicts source by source. Keep the user's raw citation unchanged until a human resolves the conflict.

Treat these as chimeric critical:

  • real identifier plus conflicts in at least two of title/authors/year;
  • title matches but the cited author list adds an author absent from the registered work.

Treat a single title translation, subtitle, year-online/year-print difference, author omission or ordering issue as metadata warning unless corroborating evidence establishes a chimera.

5. Record publication updates

Inspect publication_updates. Confirm relation direction with both the affected work and notice DOI when the alert matters. Record type, notice DOI, source and direction. Send those facts to research-ethics; do not label a correction notice itself retracted because it has update-to.

6. Audit claim↔citation support

Use templates/claim_citation_review.json and the original full text:

  • supports: the cited locator supports the claim as written;
  • partial: only a narrower statement is supported;
  • related_only: same topic, but not evidence for this proposition;
  • unsupported: the source contradicts or does not support it;
  • review_required: full text/locator/reviewer is missing.

Each review must record access, a real non-placeholder source_locator, source_evidence_sha256, reviewer, an already-occurred ISO reviewed_at, and reviewed_claim_sha256 for the exact claim text that was reviewed. The registry also emits the current claim_text_sha256; the two hashes must match, otherwise the review is stale and is downgraded to REVIEW_REQUIRED. SUPPORTS without those fields, with a future reviewed_at, with a template/absolute/escaping locator, or with a mismatched claim hash is downgraded to REVIEW_REQUIRED by the registry/gate; metadata-only access can never support a claim. If access is only ABSTRACT_ONLY, SUPPORTS also requires support_scope=ABSTRACT_EXPLICIT or abstract_claim_explicit=true; otherwise the registry/gate downgrades it to REVIEW_REQUIRED. Check direction, population, intervention, comparator, metric, time, modality, causal strength and scope. Change the claim or citation when related-only or unsupported. Do not promote an abstract-level heuristic to final support.

Normalize the four gates explicitly before delivery:

bash
python scripts/citation_four_gate.py \
  --registry citation-delivery/citation-registry.json --as-of 2026-07-05
# 或仅调试模板合约:
python scripts/citation_four_gate.py \
  --input templates/citation-four-gate.example.json --as-of 2026-07-05

The public template is intentionally UNKNOWN and must exit 1. A later gate can never repair an earlier failure: existence → identity → directed publication status → locator-backed claim support. Prefer --registry so the four-gate records are derived from the canonical registry rather than a hand copied template. ABSTRACT_ONLY may be direct only when the declared claim is explicitly present in the abstract; metadata alone never supports a claim. Retracted work may document historical record but cannot silently support a current conclusion.

7. Generate and audit delivery

Run the registry online after inputs are complete:

bash
python scripts/citation_registry.py \
  --claim-map claim_plan.json --draft paper.tex \
  --refs-spec citation_input.json --online \
  --out-dir citation-delivery

python scripts/citekey_audit.py \
  --tex paper.tex --bib citation-delivery/references.bib --json

Only confirmed, non-chimeric works enter generated BibTeX/CSL. Preserve every failure and unresolved review in separate artifacts. The generated delivery.json is DELIVERED only when citation-failures.json and claim-citation-review.json are empty and the citekey audit is OK; otherwise it exposes ERROR or REVIEW_REQUIRED. Use doi_to_any.py for one-off DOI content negotiation and formatting, not as proof of semantic support.

8. Run the gate and stage checkpoint

bash
python scripts/citation_verify_gate.py \
  --registry citation-delivery/citation-registry.json \
  --tex paper.tex --bib citation-delivery/references.bib \
  --report citation_findings.json

python ../light-orchestrator/scripts/run_checkpoint.py \
  --file .light/passport.yaml --stage 10 \
  --findings citation_findings.json --write --ts <ISO-8601>

Expect citation_verify_gate.py to return exit 1 on a critical finding. Repair the registry root cause, rebuild all derived artifacts, rerun the gate, then rerun the same stage-10 checkpoint. Deliver only after the checkpoint passes and passport records delivered.

Script map

ScriptResponsibility
citation_registry.pyinventory, normalization, version grouping, verification orchestration, claim edges, registry/BibTeX/CSL/evidence/failure/delivery
verify_refs.pyauthoritative multi-source fields, honest status machine, source evidence, conflicts, chimeras, Crossref update direction
citation_verify_gate.pystage-10 findings producer; exit 1 on confirmed missing/chimeric
citekey_audit.pymanuscript ↔ .bib key audit; reused by typesetting
doi_to_any.pyDOI content negotiation and citation-format output
verify_citation_edge.pypaper A→B open-index relation; never claim support
citation_four_gate.pyordered existence/identity/update/support contract with purpose-aware retraction handling; can derive records directly from light.citation_registry.v1

Resource boundaries

  • citation-resource-map.md: executable order and resource tiers.
  • references/registry_contract.md: machine artifact semantics.
  • references/locator_audit.md: human semantic-support protocol.
  • references.md: API/style/reference-manager details; load only the relevant section.
  • templates/*: author input and review contracts.
  • assets/citation_checklist.md: final human checklist.
  • docs/competitors/citation.md: R1 evidence and comparative claims, not an execution guide.

Completion check

  • Inventory includes claim map, manuscript, figures/tables, supplement and reference spec provenance where present.
  • DOI/arXiv/PMID/ISBN/URL are normalized; versions remain visible.
  • Every confirmed work has registration plus independent source evidence.
  • Every unavailable/unresolved source is honestly labeled.
  • No confirmed-missing or chimeric work remains.
  • updated-by direction is correct; notices are not inverted.
  • Every core claim citation has a locator-backed review; related-only is not called support; every SUPPORTS edge has source evidence SHA-256, reviewer, non-future reviewed_at, matching claim_text_sha256/reviewed_claim_sha256 and non-metadata access, with no template/absolute/escaping locator.
  • Four-gate order passes; abstract/full-text access and historical/direct purpose are explicit.
  • Four-gate input came from --registry unless there is a documented reason to use a hand-written fixture.
  • Registry, BibTeX, CSL, evidence, failures, claim review and citekey audit were generated from one canonical state.
  • delivery.json records status plus sha256: hashes for every deliverable consumed by typesetting.
  • citation gate and stage-10 checkpoint pass.
  • typesetting consumed the delivered BibTeX/citekeys.

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 Light Citation AI skill do?

Verify scholarly references and claim-citation support for Light stage 10. Use when auditing a manuscript, claim map, bibliography, DOI/arXiv/PMID/ISBN/URL, BibTeX/CSL, citekeys, chimeric or fabricated citations, retraction/correction alerts, or preparing a canonical citation registry for typesetting. Builds provenance-preserving inventories, confirms metadata with independent authoritative sources, distinguishes CONFIRMED/CONFIRMED-MISSING/UNAVAILABLE/UNRESOLVED, records Crossref update direction, and emits the citation gate plus delivery artifacts.

Why use Light Citation on TypingMind?

Because you install it once and use it with any model. Light Citation 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 Light Citation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Light0305/Light-skills/tree/master/skills/light-citation. 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 Light Citation?

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 Light Citation?

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

Is the Light Citation AI skill free?

Yes. It is published on GitHub by Light0305 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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