Opportunity ranker

The applicant has limited time and limited credible outreach. The value of this skill is focus: turning a pile of openings and professor profiles into an honest, ordered shortlist so the strongest, most-winnable opportunities get the best effort. The ranking is only as trustworthy as it is transparent — a number with no reasoning behind it is useless, so every score is explained.

What you produce

knowledge-base/openings/_ranking.md: a ranked table plus a short rationale per item, so the applicant sees why each opportunity sits where it does, not just an opaque ordering.

Step 1 — Gather what you're ranking

Read knowledge-base/profile/profile.md (the applicant's interests, goals, constraints, and crucially their dealbreakers — including the typed funding_required flag and target_start year), then the candidates: knowledge-base/professors/*.md and knowledge-base/openings/*.md. Reuse the typed signals professor-analyzer and position-discovery already wrote rather than re-deriving them: the professor's fit_score, funding_signal, accepting_students, admission_model, and email_policy; the opening's funding, deadline, start_year, and verified_on.

If a candidate the user wants ranked has no profile yet, note it as "needs analysis" and either rank it provisionally with low confidence or suggest running professor-analyzer first. Don't silently invent the missing data — a confident ranking built on guesses is worse than an honest "I can't rank this well yet".

Step 2 — Score each opportunity

First apply hard filters, then score what survives. A dealbreaker removes a candidate before scoring — it isn't a low score, it's out. In particular: if funding_required: true, drop openings with funding: self-funded (and treat unknown funding as a flagged risk, not a pass). Also flag — don't silently rank — any opening whose deadline has passed or whose verified_on/start_year shows it's from a stale cycle; recommend re-verifying rather than applying. Note each dropped candidate and why, so the applicant can override.

Score the survivors on these dimensions. They're weighted because they matter unequally, but the weights are defaults you should adapt to what the applicant's profile says they care about.

Compute a 0–100 composite. The weights are a tool for consistency, not a black box — if a dimension is unknown, say so and reflect the uncertainty rather than scoring it as if it were average.

Admission probability — be honest

This is the dimension applicants most want sugar-coated and most need straight. A world-class lab that's a perfect fit but takes one student a year from hundreds of applicants is a long shot, and saying so lets the applicant balance reaches with realistic targets. Weigh the applicant's concrete record (publications, relevant experience, fit of background to requirements) against the opening's selectivity. Frame it as odds and reasons, never as a verdict on the applicant's worth.

Step 3 — Write the ranking

Write knowledge-base/openings/_ranking.md:

# Opportunity ranking — <date>

| Rank | Opportunity | Composite | Fit | Admission | Funding | Impact | Why |
|------|-------------|-----------|-----|-----------|---------|--------|-----|
| 1 | Smith Lab (MIT) — RL robotics | 84 | 88 | 70 | strong | high | ... |
...

## Rationale
### 1. Smith Lab (MIT)
2–3 sentences: why it ranks here, the key strength, the main risk, and the recommended
move (e.g. "reach — apply but pair with safer targets").

## Portfolio note
A short read on the shortlist as a whole: is it all reaches? all safe? Suggest balance.

The portfolio note is important — applicants often over-index on a few dream labs. Point out when the shortlist needs a realistic anchor or could use more ambition.

Step 4 — Report and hand off

Summarize the top few and the portfolio shape for the user. Then suggest next actions on the leaders: draft outreach with outreach-email, tailor materials with application-materials, or analyze any "needs analysis" candidates first.

Guardrails

Per shared/references/ethics.md: the ranking exists to direct effort wisely, not to encourage mass applications. Favor a focused shortlist over a long one. Be transparent about missing data and honest about admission odds — false optimism wastes the applicant's scarce time and outreach credibility. Never present a guessed score as if it were grounded.