Hep Rumor Mill

HEP Rumor Mill Skill

Conversational interface over the prm CLI. It pulls the HEP-theory postdoc rumor mill (a public Google Sheet) for a year, resolves each offer-holder's InspireHEP profile, and analyzes their publication records so the user can study the job market and benchmark their own profile.

Two first-class uses:

• Institute intelligence: for an institution, what do the people it gave offers to look

like (citations, papers, venues, subfield mix, PhD-age, named fellowships)?

• Self benchmarking: place the user's own InspireHEP profile on the accepted cohort and

surface gaps, overall and per target institute.

Data is layered across three public, no-auth sources, keyed off the InspireHEP author id that the rumor sheet links for each person:

• InspireHEP: primary record (HEP papers, journals, citations, PhD year, advisors, ORCID).
• OpenAlex: cross-disciplinary augmentation for everyone with an ORCID, so ML / stats /

CS venues (ICML, NeurIPS, journals) that InspireHEP misses are caught.

• ORCID public API: the author's own self-claimed work list, used to rescue people whose

OpenAlex-by-ORCID cluster is contaminated by author-conflation (see below). Author-curated, so no conflation; citations are backfilled per DOI via OpenAlex.

• Semantic Scholar: name-based fallback (and h-index cross-check) when a person has no ORCID.

Read this before reporting: the data is self-reported and incomplete

The rumor mill is filled in by volunteers. Offers that were never reported are simply absent, per-institute counts are usually single digits, and the same person appears multiple times (one row per institution, and Offered then Accepted/Declined). Treat the output as a qualitative signal, not a statistic, and always state N and how many were resolved. The analysis is descriptive: it describes what kind of profile landed where; it never predicts whether a given profile will or will not get an offer. See references/analysis.md.

Quick Reference

| Intent | Command | Reference | |--------|---------|-----------| | Pull a year's rumor sheet | prm fetch --year YEAR [--sheet-id ID] | references/sheets.md | | Resolve offer-holders' records | prm enrich [--year Y] [--status Accepted] [--limit N] [--no-cross-disc] | references/schema.md | | Show one person's record | prm profile {recid \| --name NAME} | references/metrics.md | | Institute cohort profile | prm institute "NAME" [--status Accepted] [--year Y] | references/metrics.md | | Benchmark your own profile | prm analyze --me RECID [--institutes "A,B,C"] [--year Y] [--refresh] | references/analysis.md | | Emit Korean report skeleton | prm report --year Y [--me RECID] [--out PATH] | references/analysis.md |

Every command takes --json for machine-readable output. Run via uv run prm ... from the skill directory. The local store lives at ~/.local/share/hep-rumor-mill/rumor.db (override with PRM_DATA_DIR).

Typical workflow

1. prm fetch --year 2026: download the sheet, tag each institution's country/region, store

entries. Reports total rows, unique people, institutions, and the Offered/Accepted/Declined breakdown.

1. prm enrich --year 2026 --status Accepted: resolve each accepted person across sources and

compute metrics. Network-heavy and polite; it is resumable (--limit N caps a run, re-run to continue) and reports failures and any unresolved people. Start with --status Accepted since that is the cohort you usually benchmark against.

1. Explore: prm institute "Perimeter Institute" --status Accepted, prm profile 1804701,

prm analyze --me {your recid} --institutes "Perimeter Institute,CERN,DESY".

1. prm report --year 2026 --me {your recid}: emit a Korean markdown skeleton with the

data-backed tables filled and clearly-marked blanks for the qualitative judgment, which you complete by reading the offer-holders' actual papers (see references/analysis.md).

Data-quality flags surfaced by the tool

These are computed automatically; surface them when you report, do not hide them.

• n_large_collab: InspireHEP papers with > 50 authors. Big experimental / collaboration

papers inflate a person's paper and citation counts; a high value means the headline numbers do not reflect lead-author output. Pair it with n_first_author.

• OpenAlex author-conflation: OpenAlex sometimes merges several same-named people (and a

shared ORCID) into one author entity, pulling in unrelated disciplines (crystallography, neuroscience, economics). enrich drops clearly distant-field works, and when contamination is heavy it discards the whole OpenAlex cluster and rescues the real list from the ORCID public API instead (author-claimed, conflation-free), saying so in the run notes.

• interdisciplinary: set when the author's InspireHEP arxiv_categories include a

non-physics category (cs, eess, stat, q-bio, econ, q-fin), or when a material share (>= 15%, >= 2 papers) of the record is in adjacent fields (CS / ML / stats / data science) or ML venues. Mathematics and mathematical physics are excluded on purpose (normal for formal hep-th). Physics-family fields, including OpenAlex's coarse "Physics and Astronomy", never trigger it on their own.

• Citation undercount for arXiv-only preprints: a work that exists only as an arXiv preprint

(no DOI, not in InspireHEP) has its citations backfilled by a Semantic Scholar title match, which is a conservative floor (lower than Google Scholar); works Semantic Scholar does not index stay at 0. So citation totals for interdisciplinary candidates with many ML preprints still read low. Lean on the paper list and venues, not the citation total, for these people. Without an S2_API_KEY the in-pipeline backfill is best-effort (Semantic Scholar rate-limits hard); set the key for reliable backfill.

Notes

• The rumor sheet is a public Google Sheet exported as CSV; no login. Year-to-sheet-id mapping

lives in prm/sheet.py and references/sheets.md. Only 2026 ships known; add older years by extracting the embedded sheet id from each postdocrumor/{year}-rumors page.

• Journal prestige tiers (Tier A / B / C / preprint) are an editable opinion seeded with hep-th/ph

convention. They live in prm/metrics.py and are documented in references/metrics.md.

• Be considerate with the APIs: enrich paces InspireHEP requests and OpenAlex paging. Semantic

Scholar rate-limits hard and is used for the no-ORCID author fallback and for arXiv-only citation backfill; set S2_API_KEY (the same env var reference-search uses) to raise the limit and make the backfill reliable. Without it the pipeline still works, backfill is just best-effort.

• prm analyze --me uses the cached enriched record when one exists; pass --refresh to force a

fresh re-enrich (otherwise a manual citation correction is preserved across runs).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Axect
• Source: Axect/skills
• License: MIT

Install and usage instructions live in the source repository linked above.