phdtaketaketake

**Every connection edge, every candidate fact, every signal value MUST trace back to a real source you actually fetched via web search.** Fabrication is strictly forbidden — students use these rankings to decide where they spend years of their life. Made-up data is worse than no data. The contract: • ✅ Verified via web search → record value + structured EvidenceSource (URL + source_type + claim + supports_fields) • ✅ Searched but found nothing → leave the field empty / set signal to "missing" • ❌ Guessed from training memory → NOT ALLOWED • ❌ Inferred from name patterns / school proximity / "feels likely" → NOT ALLOWED • ❌ Estimated without any web search → NOT ALLOWED Two enforcement layers: • Risk-adjusted ranking — wide confidence bands move candidates down the sort order. The agent literally cannot get a top rank with unsourced claims; the band widens and `risk_adjusted_strength = strength − band/2` drops below better-evidenced peers. • --strict-evidence flag — when run with this flag, scripts/match.py rejects any candidate that has unsourced claims (a value set without an EvidenceEntry). Missing signals (no value, no evidence) are still allowed — they're honest "I couldn't verify" states. Use this when the user is making real application decisions. The matcher's confidence band (±0.2 / 0.4 / 0.6 / 0.8 — see §Confidence calibration below) handles missing data gracefully, AND the risk-adjusted ranking subtracts band/2 from the sort key — so wide bands move candidates down the list. A wide band on real data is far more useful than a narrow band on made-up data. Full allowed-source list and forbidden-behavior catalog: references/data_integrity.md. Read it before doing any connection research. --- This skill ranks candidate PhD advisors using a **5-layer deterministic pipeline** — each layer composes the layer below; every score traces back to cited evidence: ` • CAPEG match_score = w_C·C + w_A·A + w_P·P + w_E·E + w_G·G (tier-adaptive weights; w_C > w_A in every tier — connection-first invariant) • application_strength = clip(match_score + opportunity_adj, 0, 4.0) • risk_adjusted_strength = application_strength − band/2 • difficulty_adjusted_strength = max(0, risk_adjusted_strength − program_penalty) ← PRIMARY SORT KEY (post-#5) • strategy bucket = bucket(difficulty_adjusted, evidence, …) → priority / target / reach / only_if_space / drop (purely derivative — never modifies any score) ` 5 CAPEG pillars on a 4.0 scale: • Connection (C) — verified path between candidate PI ↔ student's current advisor: small-team coauthor, big-collab paper overlap, working group, analysis contact, genealogy, shared grant, co-mentored student, committee/exam, same center, prior-institution overlap, conference session. v2 aggregation: `strongest + 0.10·second_strongest`, capped at 1.0, scaled by recency. • Advisor influence (A) — PI reputation only (post-#6a): 0.40·influence + 0.30·elite_status + 0.30·grad_placement_quality. Funding and recruiting moved to Opportunity (O). • Publication (P) — field-aware tier × author-role × status × recency × contribution-bonus, with big-collab and consortium guardrails (min(0.10, n/100) cap on alphabetical co-authorship). • Experience (E) — `0.20·lab_prestige + 0.30·duration + 0.50·output`, strongest single experience. • GPA (G) — direct on 4.0; 4.3 / 4.5 / 100 / UK honours normalized. 3 non-CAPEG dimensions: • Opportunity (O) — admit-cycle availability: `recruiting_health • active_funding_quality + lab_capacity + grant_timing + availability. Drives opportunity_adj (replaces v1 pi_adj`); not_recruiting forces application_strength=0. • Program difficulty (D) — per-program penalty 0–0.8 from school- tier admit rate + cohort size + admission model + funding structure • faculty count + international friendliness. Subtracted from risk_adjusted_strength to form difficulty_adjusted_strength (the primary sort key, replaces v1 tier_adj). • Research fit (R) — structured 6-axis tie-breaker: `0.30·topic • 0.20·method + 0.15·system + 0.15·temporal + 0.10·grant + 0.10·background`. Never a 6th pillar; sorts ties only. Pipeline diagram: docs/scoring_pipeline.md. Full formulas: docs/scoring.md. Per-feature references in references/.

Users on QClaw / Claude Code / any agent platform will not write JSON themselves. The expected entry shape is conversational: > "我是 2027 fall 申请 Physics PhD,方向是 ATLAS Higgs / detector ML。 > 本科 UCI,GPA 3.85/4.0,有两篇 ATLAS big-collab paper,导师是 Prof. X。 > 请帮我找美国 top 10–30 的匹配 PI,并按 phdtaketaketake 的 evidence-first > 规则给出排序和申请策略。" > *"I'm applying for biology PhDs this fall, focusing on cancer > immunology. Berkeley undergrad, GPA 3.9, one first-author Cell paper, > advisor is Prof. Y. Find me 8 advisors at top US programs."* Your job as the agent: translate this into the structured StudentProfile + candidate-discovery workflow below. Do not ask the user to fill JSON. Do not ask for the schema upfront. Ask for missing facts in plain English, one round at a time.

If the user's first message is missing any of these, ask before doing deep research — running the pipeline without them produces low-confidence output: • Field / subfield (e.g. "physics / HEP" → resolves to FieldProfile) • Undergrad institution + GPA (with scale: 4.0 / 4.3 / 4.5 / 100 / UK honours) • Research direction (1–2 sentences — the matcher uses this for research_fit) • Current advisor(s) (name + institution — drives the C pillar; without this, connection-first matching is degraded and the matcher prints a stderr warning) • Target school tier or list (top_10 / top_11_30 / top_31_60 / top_60_plus, OR a list of school names)

• Papers: title, venue, status (published / accepted / submitted / preprint / in_prep), author position, total authors. Without this, P pillar floors out; user gets an honest "no publication evidence" rather than a guess. • Experiences: lab name, duration months, output (paper / poster / thesis). Without this, E pillar floors. • Specific candidate PIs: if the user already has a target list, skip Step 3 (discovery_plan) and feed candidates straight to collect_evidence. If not, run discovery_plan first. • Theory / experiment crossover preferences (physics-specific): affects research_fit.theory_experiment_fit signal. • International friendliness needs (visa / funding constraints): affects program_difficulty interpretation.