How do I install the Citation Provenance skill?

Run npx skillmds@latest add emmanuelrtm/citation-provenance in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Citation Provenance safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Citation Provenance?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Citation Provenance free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Citation Provenance?

EmmanuelRTM (@emmanuelrtm) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Citation Provenance by emmanuelrtm — Agent Skill

Name: Citation Provenance
Author: EmmanuelRTM

File contents

Citation provenance

Prime rule: a paper you did not retrieve and read does not exist. Every citation key in this pipeline traces to a bibliography.jsonl entry with provenance and a literature/<key>.md note. No exceptions.

Retrieval ladder (degrade gracefully)

WebSearch — scholarly queries (" site:arxiv.org", venue names, author names from seeds). Provenance: websearch.
WebFetch — fetch abstract/HTML pages for candidates found in (1). Provenance: webfetch. If a fetch fails (403/timeout), keep the search snippet as read_status: abstract-only ONLY if the snippet contained the actual abstract; otherwise drop the paper.
Seeds — files in workspace/seeds/. Provenance: seed.
User-provided notes — material the user pasted or described. Provenance: user-provided.

In --offline mode, skip 1–2 entirely.

Investigation rounds

Round 1 — graph expansion: from the task topic + seeds, gather candidate papers (titles, venues, years, why-relevant). Record the growing graph in investigation/citation-graph.json: {nodes: [{key?, title, year, source}], edges: [[from, to, "cites|extends|compares"]]}.
Round 2 — elite pool: score candidates on relevance to the task (core / adjacent / background) and quality signals; keep 10–50 in investigation/elite-pool.json. Relevance gate: fewer than 5 core+adjacent sources ⇒ append {"event":"stage_investigate","detail":"ABORTED: relevance gate"} to the ledger, reply ok: false, and stop — do not force a weak brief.
Round 3 — deep read: for each elite paper, write literature/<key>.md (provenance frontmatter, factual summary, findings relevant to the task) and append its entry to bibliography.jsonl. Run python3 .claude/scripts/bib_validate.py <run> and fix any errors.
Write investigation/round{1,2,3}.md documenting decisions per round.

The brief

brief.md sections: Problem · State of the Art · Gap · Constraints (from task.md) · Candidate Directions · Related Work. Every prior-work statement carries [EV:cite:<key>]. The brief is the Ideator's ONLY literature input — make the Gap and Candidate Directions concrete enough to generate from.

---name: citation-provenancedescription: Literature retrieval and provenance protocol for the problem-investigator. Defines the citation-graph rounds, elite-pool filtering, the degradation ladder for restricted networks, and bibliography.jsonl discipline.---# Citation provenance**Prime rule: a paper you did not retrieve and read does not exist.** Everycitation key in this pipeline traces to a `bibliography.jsonl` entry withprovenance and a `literature/<key>.md` note. No exceptions.## Retrieval ladder (degrade gracefully)1. **WebSearch** — scholarly queries ("<topic> site:arxiv.org", venue names, author names from seeds). Provenance: `websearch`.2. **WebFetch** — fetch abstract/HTML pages for candidates found in (1). Provenance: `webfetch`. If a fetch fails (403/timeout), keep the search snippet as `read_status: abstract-only` ONLY if the snippet contained the actual abstract; otherwise drop the paper.3. **Seeds** — files in `workspace/seeds/`. Provenance: `seed`.4. **User-provided notes** — material the user pasted or described. Provenance: `user-provided`.In `--offline` mode, skip 1–2 entirely.## Investigation rounds- **Round 1 — graph expansion**: from the task topic + seeds, gather candidate papers (titles, venues, years, why-relevant). Record the growing graph in `investigation/citation-graph.json`: `{nodes: [{key?, title, year, source}], edges: [[from, to, "cites|extends|compares"]]}`.- **Round 2 — elite pool**: score candidates on relevance to the task (core / adjacent / background) and quality signals; keep 10–50 in `investigation/elite-pool.json`. **Relevance gate**: fewer than 5 core+adjacent sources ⇒ append `{"event":"stage_investigate","detail":"ABORTED: relevance gate"}` to the ledger, reply `ok: false`, and stop — do not force a weak brief.- **Round 3 — deep read**: for each elite paper, write `literature/<key>.md` (provenance frontmatter, factual summary, findings relevant to the task) and append its entry to `bibliography.jsonl`. Run `python3 .claude/scripts/bib_validate.py <run>` and fix any errors.- Write `investigation/round{1,2,3}.md` documenting decisions per round.## The brief`brief.md` sections: Problem · State of the Art · Gap · Constraints (fromtask.md) · Candidate Directions · Related Work. Every prior-work statementcarries `[EV:cite:<key>]`. The brief is the Ideator's ONLY literature input —make the Gap and Candidate Directions concrete enough to generate from.

Frequently asked questions

Citation Provenance