paper-relevance-filter

name

paper-relevance-filter

description

Scores a candidate paper against a keyword watchlist and a relevance-criteria document, returning KEEP/DROP/REVIEW with a one-line rationale and a 0-100 relevance score. Combines keyword-match strength, criteria-fit, and a historical-context check (was this paper or its preprint already covered in a recent digest). Domain-neutral - usable for any literature-scan workflow. Use after fetching candidate papers from bioRxiv, medRxiv, or PubMed and before clustering or synthesis. Trigger keywords - paper relevance, filter papers, keep or drop, score papers, relevance rationale.

paper-relevance-filter

Decide whether a fetched paper belongs in this week's digest. Output is a per-paper decision (KEEP / DROP / REVIEW), a 0-100 score, and a one-line rationale that the user can audit.

Workflow

- [ ] Step 1: Load relevance criteria + the watchlist + last-4-weeks kept-paper IDs
- [ ] Step 2: Score each paper on three axes (match, criteria, novelty)
- [ ] Step 3: Combine to a 0-100 score; map to KEEP / REVIEW / DROP via thresholds
- [ ] Step 4: Apply tie-breakers (cap output at requested max kept count)
- [ ] Step 5: Return decisions + a calibration summary

Step 1 — Inputs

The caller hands the skill:

• papers: list of normalized paper records (output of fetch-preprint-recent or fetch-pubmed-recent)
• watchlist: list of keywords/phrases (with optional weights — default weight 1.0)
• criteria: text from relevance-criteria.md describing what fits and what doesn't
• prior_ids: set of id values that appeared as KEEP in any of the last 4 digests (used for novelty)
• max_kept: target ceiling, e.g. 25 (the digest will not exceed this)

Step 2 — Three-axis scoring

For each paper, compute three sub-scores in [0, 1]:

Axis 1 — Match strength (0-1)

• 0.0 if no watchlist keyword appears in title or abstract
• 0.5 if a keyword appears once in the abstract only
• 0.7 if a keyword appears in the abstract more than once OR in title once
• 1.0 if a keyword appears in the title AND abstract, or multiple distinct keywords match

If keywords carry weights (some matter more than others), use the max weight among matched keywords as a multiplier capped at 1.0.

Axis 2 — Criteria fit (0-1) This is the qualitative axis. Read the abstract against the relevance-criteria document. The criteria typically state:

• In-scope concepts (the field/method the user actually wants)
• Out-of-scope concepts (look-alikes the keyword filter might let through)
• Required minimums (e.g., "must report empirical results", "must be primary research, not commentary")

Score:

• 1.0 — clearly in-scope, meets minimums, no look-alike traps triggered
• 0.7 — in-scope but borderline (review article on the topic; short report; preprint with no methods detail in abstract)
• 0.4 — partially in-scope (touches the topic but the paper's main subject is elsewhere)
• 0.0 — out-of-scope or trips an explicit exclusion (e.g., "exclude pure-theory papers" and the abstract is pure theory)

If the criteria document is silent on a paper's territory, default to 0.7 and flag for REVIEW.

Axis 3 — Novelty (0-1)

• 1.0 if id is not in prior_ids and the title doesn't fuzzy-match any prior title
• 0.5 if a prior preprint version exists in prior_ids (e.g., same DOI prefix 10.1101/... matched, this is a journal version) — KEEP-worthy but tag as "journal version of preprint covered YYYY-WW"
• 0.0 if exact id match in prior_ids (already covered)

Use normalized title (lowercase, strip punctuation, collapse whitespace) for fuzzy matching. A Levenshtein ratio > 0.9 against any prior title counts as a match.

Step 3 — Combine and threshold

score = 100 * (0.45 * match + 0.45 * criteria + 0.10 * novelty)

Match and criteria carry equal weight (a paper that mentions your keywords once but is wildly out-of-topic should not score higher than one that's deeply on-topic with a single mention). Novelty is a small finger on the scale — enough to demote already-covered work but not enough to drop a genuinely important journal-version-of-preprint update.

Decision thresholds (default; the calling agent may override):

Score	Decision	Notes
70-100	KEEP	Goes into the digest
50-69	REVIEW	Boundary cases — caller decides whether to escalate to user
0-49	DROP	Filtered out, reason logged in the dropped-papers section

Special-case override: if novelty == 0.0 (already in a prior digest), force DROP regardless of score. The papers section may still list it as "already covered" for traceability.

Step 4 — Tie-breakers when KEEP > max_kept

When more papers score ≥ 70 than max_kept:

1. Re-score with stricter axis-2 thresholds (review articles drop from 0.7 to 0.4; partial-fit drops from 0.4 to 0.2). This is the cleanest tightening.
2. If still over, sort KEEPs by score descending, take the top max_kept, and demote the rest to REVIEW (not DROP — they were good enough; just couldn't fit). Surface this in the calibration summary.

Never demote to DROP what scored ≥ 70 unless explicitly forced.

Step 5 — Return

{
  "decisions": [
    {
      "id": "10.1101/2026.05.07.123456",
      "decision": "KEEP",
      "score": 84,
      "axes": {"match": 0.9, "criteria": 1.0, "novelty": 1.0},
      "rationale": "Title + abstract hit 'protein language model' twice; in-scope (primary methods paper, empirical); novel.",
      "tags": []
    },
    {
      "id": "PMID:39000000",
      "decision": "KEEP",
      "score": 72,
      "axes"      
       
       
    
    
       
       
       
            
       
       
    
  
   
     
     
     
     
     
     
  

Common Patterns

Pattern A — Strict weekly digest: defaults above. Tight thresholds; max_kept=25.

Pattern B — Catch-up over multiple weeks: run per-week with the same prior_ids growing each iteration. Don't pool all 3 weeks of papers and filter once — you'll lose the historical-context signal.

Pattern C — Topic deep-dive (user wants more, not less): relax max_kept to a high number (e.g. 100), keep thresholds, return the full ranked list. Only do this on explicit user request.

Pattern D — Sanity-check the watchlist itself: run with prior_ids = [] and look at calibration.dropped. If the same theme keeps getting dropped for criteria reasons, the watchlist may be drifting away from intent.

Guardrails

1. Never decide based on title alone if the abstract is available. Titles overstate; abstracts qualify.
2. Never KEEP a paper whose abstract isn't returned. Tag it REVIEW so the user can decide; an empty abstract is a signal something went wrong upstream, not a green light.
3. Always explain the DROP. A one-line rationale per dropped paper is non-negotiable — without it the user cannot audit the filter or notice systematic blind spots.
4. Don't conflate "out of scope" with "low quality". This skill judges fit, not quality. A high-quality paper outside scope is a DROP; a low-quality paper inside scope is a KEEP with a rationale flag.
5. Don't auto-update the watchlist. If you notice the criteria are pulling in a class of papers the watchlist doesn't anticipate, surface it in the calibration summary; the user owns the watchlist edit.
6. Don't dedupe inside this skill. The caller is responsible for cross-source dedupe (a bioRxiv preprint and its PubMed publication are the same paper and must be merged before this skill sees them, otherwise novelty scoring breaks).
7. Don't use unbounded LLM judgment for axis 2. Keep the criteria-fit decision anchored to the explicit relevance-criteria.md text. If something's not in the criteria, the answer is REVIEW with rationale "criteria silent" — not "I think it fits."

Quick Reference

Decision	Score	Action by caller
KEEP	70+	Include in digest, cluster, synthesize
REVIEW	50-69	Surface in a "boundary cases" section, ask user
DROP	0-49	Log in dropped-papers list with rationale

Axis	Default weight	What it measures
Match	0.45	How strongly watchlist keywords appear in title/abs
Criteria	0.45	Qualitative fit against relevance-criteria.md
Novelty	0.10	Not already in last-4-weeks digests