citecheck

name	citecheck
description	Score \cite pertinence in a LaTeX file. Use when the user runs /citecheck on a .tex file or asks to verify references against InspireHEP/arXiv abstracts.

citecheck

Score every \cite{...} in one or more LaTeX files against the cited paper's abstract (InspireHEP preferred, arXiv as fallback), surface low-scoring or title-mismatched citations for manual review.

Inputs

• tex_files — one or more absolute paths to .tex files passed by the user.
• Optional flags (parsed from the user message): --bib <path>, --refresh, --refresh-missing, --no-arxiv-fallback, --cross-check, --batch-size <n> (default 15), --parallel <n> (default 8).

All scripts live under the plugin's skills/citecheck/scripts/ directory. At the start of every bash block, set the SCRIPTS variable so the commands work whether the skill was loaded from a plugin or from the local .claude/skills/citecheck/ checkout:

SCRIPTS="${CLAUDE_PLUGIN_ROOT:+$CLAUDE_PLUGIN_ROOT/skills/citecheck/scripts}"
SCRIPTS="${SCRIPTS:-.claude/skills/citecheck/scripts}"

All outputs are written under .citecheck/ and .citecache/ rooted at the current working directory.

Steps

Repeat steps 1–11 for each tex_file in order, one at a time. Do not start the next file until the current one completes. Print [1/N] Processing <tex_file> … before each file's step 1.

1. Resolve bib. If --bib was given, use it. Otherwise walk up from tex_file until a bibliography.bib is found. Fail fast with a clear message if none.

2. Build bib index. Clear any intermediates from a prior aborted run first — .citecache/ (the abstract cache) is intentionally preserved.

   rm -rf .citecheck/.tmp
   mkdir -p .citecheck/.tmp
   python3 ${SCRIPTS}/parse_bib.py \
       <bib_path> .citecheck/.tmp/bib_index.json
   

3. Extract citations.

   python3 ${SCRIPTS}/extract_citations.py \
       <tex_file> .citecheck/.tmp/citations.json
   

   If the citations file is empty, print No \cite references found in <tex_file>. and skip to the next file.

4. Compute missing keys. Decides which bibkeys need a fresh fetch given the bib index, citations, and current cache. Skip rules:

   • bibkey not in bib_index.json → skip (will be marked abstract_status: "missing_bib_entry" in step 6).
   • bib entry has no title AND no arxiv_id AND no doi → skip (will be marked abstract_status: "no_bib_metadata").
   • cache hit with source == "fetch_error" → always re-fetch.
   • cache hit with source == "not_found" → re-fetch only when --refresh-missing is passed.
   • cache hit with any other source → re-fetch only when --refresh is passed.

   python3 ${SCRIPTS}/compute_missing.py \
       --citations .citecheck/.tmp/citations.json \
       --bib-index .citecheck/.tmp/bib_index.json \
       --cache-dir .citecache/abstracts \
       --out .citecheck/.tmp/missing_keys.json \
       [--refresh] [--refresh-missing]
   

5. Fetch missing abstracts.

   python3 ${SCRIPTS}/fetch_abstracts.py \
       --missing .citecheck/.tmp/missing_keys.json \
       --cache-dir .citecache/abstracts \
       --parallel <parallel> \
       [--cross-check] [--no-arxiv-fallback]
   

6. Build batches.

   python3 ${SCRIPTS}/build_batches.py \
       --citations .citecheck/.tmp/citations.json \
       --bib-index .citecheck/.tmp/bib_index.json \
       --abstracts-dir .citecache/abstracts \
       --out-dir .citecheck/.tmp/ \
       --batch-size <batch_size>
   

   Writes .citecheck/.tmp/batch_<n>_input.json (1-indexed). Each entry has {id: "c<i>", bibkey, bib_title, fetched_title, abstract, abstract_status, paragraph, section_heading, line}. abstract_status is one of ok, fuzzy, mismatch, not_found, fetch_error, missing_bib_entry, no_bib_metadata. The script prints the batch count on stdout.

7. Dispatch scorers. In a single message, issue one Agent call per batch with:

   • subagent_type: "citecheck-scorer"
   • description: "Score batch <n>"
   • prompt: "input_path: .citecheck/.tmp/batch_<n>_input.json\noutput_path: .citecheck/.tmp/batch_<n>_output.json"

   Concurrency budget: at most 8 parallel Agent calls per wave. For more than 8 batches, dispatch in waves of 8.

8. Validate outputs. For each batch, check that .citecheck/.tmp/batch_<n>_output.json exists and parses as a JSON list whose entries have {id, bibkey, score, reason}. On malformed/missing output, retry that batch's Agent call once. If the retry also fails, leave that batch's citations with score: null, reason: "scoring_failed" and continue.

9. Collate report.

   python3 ${SCRIPTS}/collate_report.py \
       --citations .citecheck/.tmp/citations.json \
       --scores-dir .citecheck/.tmp/ \
       --abstracts-dir .citecache/abstracts \
       --tex-path <tex_file> \
       --output-md .citecheck/<basename>.md \
       --output-json .citecheck/<basename>.json
   

10. Clean tmp. Remove .citecheck/.tmp/.

11. Print per-file summary. Read the report JSON (a dict, not a list) and print one line:

    python3 -c "
    import json, sys
    d = json.load(open('.citecheck/<basename>.json'))
    needing = d['scored_low'] + d['scored_borderline']
    title_issues = len(d['title_match_issues'])
    print(f\"{d['total_citations']} citations · {needing} needing review · {title_issues} title-match issues · report at .citecheck/<basename>.md\")
    "
    

12. Combined summary (multi-file only). After all files are processed, if more than one file was given, print a single totals line:

    Done. <total_citations> citations across <N> files · <total_needing> needing review · <total_title_issues> title-match issues
    

Invariants

• Never modify the .tex file or bibliography.bib.
• Subagents must only call Read and Write. Reject any other tool surface.
• Abstract cache persists across runs; scoring is always recomputed.
• If a step fails for one file, report the error and continue with the next file rather than aborting the whole run.