phdtaketaketake

PhD advisor matching and application triage assistant. Connection-first, evidence-first. Packaged as a Claude Code skill — also works on QClaw, Codex CLI, Cursor, and any LLM agent that can read SKILL.md. Find the right advisor by network strength, not h-index.

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⚠️ Product boundary. This is a 4.0-scale relative application-strength index, not an admission probability. Missing or blocked sources widen the confidence band instead of being guessed.

phdtaketaketake is an expert-designed heuristic decision-support system, not an empirically calibrated admission probability predictor. All thresholds (CAPEG weights, recency multipliers, program-difficulty components, strategy bucket cutoffs, etc.) are v1/v2 defaults — they should be recalibrated against real portfolios over time. See docs/DESIGN.md §11 for the design boundaries and §"Frozen scope" for what this skill explicitly will not do.

Install

Recommended: one-line installer (per host)

# Claude Code
npx @powerofjinbo/phdtaketaketake install --claude

# OpenAI Codex (user-level)
npx @powerofjinbo/phdtaketaketake install --codex

# Cursor (project rule)
npx @powerofjinbo/phdtaketaketake install --cursor --project .

# Or all detected hosts in one shot (run from a project root —
# Cursor is skipped if you don't pass --project)
npx @powerofjinbo/phdtaketaketake install --all --project .

The npx installer is a thin shim: it does NOT modify your shell, does NOT add a postinstall hook, and does NOT run pip for you. For each host:

• --claude prefers claude plugin marketplace add + claude plugin install if the claude CLI is on $PATH; otherwise prints the canonical slash-commands you can run inside Claude Code (or use --manual-copy to drop the package directly into ~/.claude/plugins/local/phdtaketaketake/).
• --codex copies the package into $HOME/.agents/skills/phdtaketaketake/ (user-level) or <project>/.agents/skills/phdtaketaketake/ (with --project).
• --cursor --project <path> writes <path>/.cursor/rules/phdtaketaketake.mdc (a Project Rule pointer to SKILL.md, not a copy).

After the host install, enable the Python CLIs in the Python environment of your choice:

python -m pip install -e <path-printed-by-installer>

This is intentionally explicit — the installer never picks a Python environment for you (conda / venv / system / pipx all coexist on real boxes; the user knows their setup).

Verify the install with the bundled health check:

npx @powerofjinbo/phdtaketaketake doctor
# Checks Node, Python, claude CLI, manifests, and version sync.

Manual / development install

If you want to clone the repo (e.g. to contribute, or to run from a checkout without npm/npx):

git clone https://github.com/powerofjinbo/phdtaketaketake.git \
  ~/.claude/skills/phdtaketaketake

cd ~/.claude/skills/phdtaketaketake
pip install -e .

CLIs on $PATH after pip install -e .

# Workflow A — advisor matching / triage
phdtaketaketake-discovery-plan   # per-field PI-search recipe
phdtaketaketake-collect-evidence # auto-fill evidence from OpenAlex / PubMed / DBLP / Semantic Scholar
phdtaketaketake-audit            # report evidence repair queue
phdtaketaketake-match            # rank candidates given profile + JSON
phdtaketaketake-export-schemas   # write Pydantic models out to JSON Schema (Draft 2020-12)

# Workflow B — CV optimization
phdtaketaketake-cv-template      # print the bundled LaTeX CV template (path or contents)
phdtaketaketake-cv-compile       # compile a CV .tex to PDF via latexmk / pdflatex

You can also invoke the Workflow A matching CLIs via python scripts/<name>.py … from a checkout (no install needed). The Workflow B CV tools live in the installed package; from a checkout, invoke them via python -m phd_matcher.cv.cli.template … and python -m phd_matcher.cv.cli.compile ….

Claude Code auto-discovers the skill on next session. For other agents, see Use with non-Claude agents below.

Don't have Claude Code? Install at claude.com/code.

Use

In any Claude Code / QClaw / Codex session, describe what you want in plain English (or Chinese) — you do not need to write JSON yourself:

"I'm applying for Physics PhDs this fall — ATLAS Higgs / detector ML, UCI undergrad GPA 3.85/4.0, two ATLAS big-collab papers, advisor Prof. X. Find me top 10–30 matching PIs and rank them by phdtaketaketake's evidence-first rules."

"我是 SJTU 材料系本科，研究方向 2D 材料 photodetector，GPA 88/100，导师是 Prof. Y，求美国 top-30 PhD 申请定位。"

The agent will:

1. Build your profile (asks for any missing key info)
2. Web-research candidate advisors at your target schools matching your research direction
3. Verify connection edges to your current advisor (co-author papers, academic genealogy, joint collaborations)
4. Run scripts/match.py for deterministic 4.0-scale scoring
5. Present ranked candidates with per-dimension breakdown and cited sources

What you need to give the agent

Required for a useful run (the agent will ask if you don't volunteer them):

• Field / subfield — physics / HEP, cs / NLP, bio / immunology, …
• Undergrad institution + GPA (with scale: 4.0 / 4.3 / 4.5 / 100 / UK honours)
• Research direction — 1–2 sentences
• Current advisor(s) — name + institution. Without this, connection-first matching loses its anchor; the matcher prints a stderr warning and the C pillar floors.
• Target school tier or list — top_10 / top_11_30 / top_31_60 / top_60_plus, or specific schools

Optional but improves output quality: papers (title / venue / status / author position / total authors), prior research experiences, specific candidate PIs you already have in mind, theory ↔ experiment crossover preferences, international friendliness needs.

The matcher is evidence-first: missing optional fields widen the confidence band — they don't crash and they don't get filled with guesses.

Parallel workflow: CV optimization

Once the matcher has surfaced your top candidates, the same skill can help you produce a LaTeX-formatted PhD-application CV, optionally tailored for the top targets:

"Now help me make a CV for these top 3 PIs." "用上面的 match.json 帮我做一份针对 Hartman 的 tailored CV。"

Two CLIs back this:

phdtaketaketake-cv-template --print > cv.tex   # bundled LaTeX template
phdtaketaketake-cv-compile cv.tex              # → cv.pdf via latexmk / pdflatex

The template ships with all sections present by default (Education, Research Experience, Publications & Presentations, Technical Skills, Teaching, Leadership, Honors); the agent fills it from your conversational input and, given a match.json, reorders the existing content + prunes weakly-relevant entries (with your confirmation before any deletion). Tailoring is reordering and user-approved pruning — never invention (no fabricated experiences, papers, or skills). PDF compile requires a TeX install (MacTeX / TeX Live / MiKTeX); minimal installs may need tlmgr install titlesec enumitem. Falls back cleanly to "paste into Overleaf" when local TeX is unavailable. Full agent contract in SKILL.md §"CV optimization (parallel workflow)" and references/cv_optimization.md.

Output per candidate

• match_score (0–4.0) — CAPEG composite
• application_strength (0–4.0, not a probability) — match + opportunity_adj
• risk_adjusted_strength = application_strength − band/2
• difficulty_adjusted_strength = max(0, risk_adjusted − program_penalty) — primary sort key (post-#5)
• lower_bound = application_strength − band — conservative reading
• 5-tier label (on difficulty_adjusted_strength): Far Reach · Reach · Target · Match · Safe
• Per-pillar: c_score / a_score / p_score / e_score / g_score
• Per-feature: o_score (opportunity) · program_difficulty_penalty + difficulty_reasons · research_fit_score + axes
• Evidence breakdown: total_signals / verified / missing / unsourced (with names of which signals fall in each)
• Strategy (post-#7): apply_bucket (priority / target / reach / only_if_space / drop) + recommended_action (apply / contact_first / investigate_evidence / deprioritize / skip) + outreach_angle (only if sourced material exists) + evidence_to_fix queue
• Why matched — cited from real searches: e.g., "co-authored 4 small-team papers with Prof. Wang in 2022–2024 (Google Scholar) · same ATLAS H→cc̄ working group (ATLAS Glance)"

Top-level CLI output also includes a strategy_summary rolling up the full portfolio (priority/target/reach/only_if_space/drop candidate IDs + evidence_fix_queue + portfolio_notes).

Architecture: no static cache, real data only

There is no bundled cache of advisors. PhD-advisor data is too dynamic and too vast for static datasets to be useful. Instead:

Cardinal rule: real data only

Every connection edge, every candidate fact must trace back to a real source the agent actually fetched (Google Scholar / OpenAlex / INSPIRE-HEP / PubMed / Math Genealogy / faculty pages / etc.). Fabrication is strictly forbidden — students use these rankings for real decisions, and made-up data is worse than no data. Missing signals are honest; the matcher widens its confidence band accordingly.

Allowed sources + forbidden behaviors are enumerated in references/data_integrity.md.

Design charter

The full design goals — what this skill is for, what it explicitly is not, and the roadmap — live in docs/DESIGN.md. One-line mission:

Generate auditable, discipline-calibrated, connection-first US PhD advisor / program rankings for STEM applicants — to support school and advisor selection, without pretending to be an admission-probability predictor.

Coverage

The scoring engine itself is field-agnostic, but the calibration is best-supported for fields the project was designed against.

Quality of agent retrieval scales the result quality — and data is always fresh, since nothing is cached.

Adding a tier YAML for a new field: see CONTRIBUTING.md. PRs welcome.

How it differs

Scoring philosophy

The matcher uses a 5-layered scoring pipeline — each layer is deterministic Python, every score traceable back to cited evidence:

match_score          = w_C·C + w_A·A + w_P·P + w_E·E + w_G·G        # CAPEG, tier-adaptive weights
application_strength = clip(match_score + opportunity_adj, 0, 4.0)   # adds admit-cycle availability
risk_adjusted        = application_strength − band/2                 # widens band → lower rank
difficulty_adjusted  = max(0, risk_adjusted − program_penalty)       # ← primary sort key
strategy             = bucket(difficulty_adjusted, evidence, …)      # → priority/target/reach/only_if_space/drop

Five CAPEG pillars on a 4.0 scale, tier-adaptively weighted by school competitiveness:

• Connection (C) — paths between candidate ↔ your current advisor: co-author (small-team vs big-collab differentiated), academic genealogy, shared grants, co-mentored students, working-group / analysis-contact overlap, committee/exam, same center, prior-institution overlap, conference sessions. v2 aggregation: strongest + 0.10·second_strongest, capped at 1.0, scaled by recency.
• Advisor influence (A) — reputation only (post-#6a refactor): h-index proxy + elite status + 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. Top-3 weighted aggregate.
• 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.

Three non-CAPEG dimensions:

• Opportunity (O) — admit-cycle availability: recruiting health + funding + lab capacity + accessibility. 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 to form difficulty_adjusted_strength (the primary sort key).
• 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.

5-tier label (applied to difficulty_adjusted_strength): Far Reach · Reach · Target · Match · Safe.

Full formulas: docs/scoring.md · Pipeline diagram: docs/scoring_pipeline.md · Skill instructions: SKILL.md · Profile + CandidateAdvisor schema: references/profile_schema.md.

Use with non-Claude agents

The skill is Claude-Code-native by default, but the matcher is plain Python and SKILL.md is framework-agnostic. The recommended paths all flow through the npm installer:

• OpenAI Codex / OpenCode / QClaw / OpenClaw-style AgentSkills hosts — these expect skills under .agents/skills/<name>/ (project-local) or $HOME/.agents/skills/<name>/ (user-level). Run:

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  npx @powerofjinbo/phdtaketaketake install --codex                  # user-level
  npx @powerofjinbo/phdtaketaketake install --codex --project .       # project-local
  

  AGENTS.md ships at the package root as a short pointer to SKILL.md so hosts that auto-read AGENTS.md (Codex, several AgentSkills hosts) pick up the same canonical contract.

• Cursor — modern Cursor uses Project Rules at .cursor/rules/*.mdc, not the legacy .cursorrules file. Run:

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  npx @powerofjinbo/phdtaketaketake install --cursor --project .
  

  This writes .cursor/rules/phdtaketaketake.mdc (a short pointer to SKILL.md, not a copy).

• --all and Cursor — npx … install --all installs Claude + Codex + Cursor when possible. Cursor needs --project <path> (its rule is scoped to a project tree); when you run --all without --project, Cursor is skipped with a notice rather than failing the whole command. To get all three in one shot from inside a project root:

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  npx @powerofjinbo/phdtaketaketake install --all --project .
  

• Other agents — point them at the installed SKILL.md. Most modern coding agents read it and execute the workflow correctly.

Example session

See docs/example_session.md for a walk-through.

License

MIT — see LICENSE.