Phdtaketaketake
Evidence-first PhD advisor matcher — scores connection strength on a 4.0 scale, not h-index.
Test report
- Verdict
- Tested · Works
- Score
- Tested
- Jul 14, 2026
- Environment
- Claude Code 2.x (agent harness)
- Upstream re-checked
- Aug 10, 2026 · af296d3
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.
Scored on four weighted criteria — install, triggering, output vs. baseline, docs. How scoring works
- Installs cleanly 5/5
- Triggers reliably 5/5
- Output vs. baseline 9/10
- Docs & honesty 5/5
What Phdtaketaketake does
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.
How to install Phdtaketaketake
git clone https://github.com/powerofjinbo/phdtaketaketake
cd phdtaketaketake
mkdir -p ~/.claude/skills
cp -r . ~/.claude/skills/phdtaketaketake
Skills live in ~/.claude/skills/ (global) or .claude/skills/
(per-project). Restart Claude Code after installing.
Commands — how to trigger Phdtaketaketake
-
/phdtaketaketakeEvidence-first PhD advisor matcher — scores connection strength on a 4.0 scale, not h-index.
It also activates on plain-language prompts like these:
-
Match me with PhD advisors who actually fit my research -
Score how well this professor's work connects to my topic -
Find supervisors for my HEP thesis with evidence, not rankings
Frequently asked questions
- Is the Phdtaketaketake skill free?
- Yes. The skill itself is free from powerofjinbo/phdtaketaketake. SkillProof publishes the install command and an independent test verdict at no cost.
- Does Phdtaketaketake work with Claude Code?
- We tested it with Claude Code 2.x (agent harness) on Jul 14, 2026. Verdict: Tested · Works. 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.
- What is the Phdtaketaketake SkillProof Score?
- 9.6/10 — installs cleanly 5/5, triggers reliably 5/5, output vs. baseline 9/10, docs & honesty 5/5.
- How do I install Phdtaketaketake?
- Copy the install command from this page, run it in your terminal, and restart Claude Code. Skills live in ~/.claude/skills/ (global) or .claude/skills/ inside a project.
- Can I use Phdtaketaketake with Cursor, Copilot, Gemini CLI, Codex or other AI tools?
- The SKILL.md format is native to Claude (Claude Code, Desktop, claude.ai). The instructions inside adapt to other assistants: Cursor rules, GitHub Copilot instructions, Windsurf rules, Custom GPTs, AGENTS.md for OpenAI Codex, and GEMINI.md for Google Gemini CLI — our conversion guides cover each, and the free converter on the tools page does the wrapping for you.