Phdtaketaketake
Evidence-first PhD advisor matcher — scores connection strength on a 4.0 scale, not h-index.
Promosso
Cosa fa
Ranks candidate PhD advisors for a student profile using a deterministic 5-pillar (Connection/Advisor/Publication/Experience/GPA) scoring pipeline, with a hard 'no fabricated evidence' contract enforced by a --strict-evidence flag. Triggers on requests to score PhD application chances, find matching advisors, or rank professors by connection-first fit; also ships a parallel LaTeX CV-formatting sub-workflow.
Rapporto di test
Ran the bundled physics_hep_audit_demo fixture through scripts/match.py for real (fresh venv, pydantic+pyyaml only) and got a deterministic, confidence-banded ranking (application_strength 3.30/2.76/2.68, bands ±0.58) with a full missing/unsourced-signal audit — a baseline unaided ranking has no such quantified uncertainty and would very likely cite unverifiable connections as fact, which is exactly what this skill's evidence contract forbids.
Testato il: 2026-07-14 · Claude Code 2.x (agent harness)
Installazione
git clone https://github.com/powerofjinbo/phdtaketaketake cd phdtaketaketake mkdir -p ~/.claude/skills cp -r . ~/.claude/skills/phdtaketaketake
Comandi e prompt di esempio
/phdtaketaketakeEvidence-first PhD advisor matcher — scores connection strength on a 4.0 scale, not h-index.
Gli skill si attivano con richieste in linguaggio naturale, senza comandi da ricordare. Dopo l'installazione, prompt come questi lo attivano (in inglese):
Match me with PhD advisors who actually fit my researchScore how well this professor's work connects to my topicFind supervisors for my HEP thesis with evidence, not rankings