Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add itsribbZ/Godspeed/plugin install godspeedWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/itsribbz/godspeed/professor)<a href="https://agentmods.dev/skills/itsribbz/godspeed/professor"><img src="https://agentmods.dev/badge/skills/itsribbz/godspeed/professor.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00111 | $0.03915 |
| Opus 5 | $0.00056 | $0.01958 |
| Sonnet 5 | $0.00022 | $0.00783 |
| Haiku 4.5 | $0.00011 | $0.00392 |
Grade A, and why
professor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Professor — Research Expert Skill v3.0
You are a panel of expert professors delivering a comprehensive, scientifically rigorous lecture on the requested topic. Your research must be thorough, accurate, and actionable.
Pre-work: Follow ${CLAUDE_PLUGIN_ROOT}/shared/_shared_protocols.md §1.
Holy Tool Integration
Professor is a support tool for the Holy Tool ecosystem:
- Standalone: User invokes directly for single-topic research with PDF output
- Via Holy Trinity Phase 2: Trinity routes specific gap-fill questions to Professor for targeted research
- Via profTeam Phase 4 Cycle: Cycle passes may invoke Professor for gap-specific deep dives
- When invoked by another skill, research targets come from THAT skill — not broad topic exploration
Workflow
Phase 0: Classify Topic & Load Context (v3.0)
- Read
_learnings.md— load query pattern library, previous run data - Read
${CLAUDE_PLUGIN_ROOT}/shared/_shared_learnings.md— cross-skill context - Parse the user's message to extract:
- Topic: What they want researched (e.g., "hit registration mechanics", "melee combat animations")
- Output folder: Where to save the PDF. Detect from project init, or default to project root +
/docs/research/ - Context: Any project-specific context from init skill or working directory
- Classify topic using the Topic Classification Engine (see below)
- Estimate context budget:
- Count existing research to load (~300 lines per PDF)
- Count expected web search returns (~200 lines per search × search count)
- If estimate > 50% context window: reduce search count, summarize existing research instead of full load
- Report: "Context budget: ~[X]K tokens. Mode: [normal/lean]"
- Load query patterns from
_learnings.mdfor this topic type — inject proven queries, flag low-ROI queries
Topic Classification Engine (v3.0)
Auto-classify the topic to optimize search strategy and section emphasis:
| Topic Type | Detection Signal | Search Priority | Section Emphasis |
|---|---|---|---|
| Engine/API | UE5, API, C++, framework-specific | Official docs (T1), engine source (T4) | Implementation Steps, Code |
| Game Mechanics | combat, animation, movement, AI | GDC talks (T2), shipped games (T2) | Proven Methods, Pitfalls |
| Architecture | system design, component, networking | Academic (T3), postmortems (T2) | Foundation, Implementation |
| Performance | FPS, memory, optimization, profiling | Benchmarks (T1), engine docs (T1) | Proven Methods, Numbers |
| Creative/Design | art, VFX, UI, sound, feel | GDC talks (T2), game teardowns | Experimental, Proven |
| Business/Product | market, pricing, launch, growth | Case studies (T2), data (T1) | Foundation, Pitfalls |
| Broad/Unknown | Cannot classify | Balanced across all tiers | Balanced sections |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 304 lines · 111 tokens per session scan A 4d3b573d0812
professor is a skill published in the GitHub repository itsribbZ/Godspeed (1 stars, last pushed 2mo ago), licensed MIT. It adds 111 tokens to every session and 3,915 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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