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 theadityamittal/claude-professor/plugin install claude-professorWrote 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/theadityamittal/claude-professor/whiteboard)<a href="https://agentmods.dev/skills/theadityamittal/claude-professor/whiteboard"><img src="https://agentmods.dev/badge/skills/theadityamittal/claude-professor/whiteboard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/theadityamittal/claude-professor/whiteboard"><img src="https://agentmods.dev/badge/skills/theadityamittal/claude-professor/whiteboard.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00061 | $0.05517 |
| Opus 5 | $0.00030 | $0.02759 |
| Sonnet 5 | $0.00012 | $0.01103 |
| Haiku 4.5 | $0.00006 | $0.00552 |
Grade A, and why
whiteboard 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 10d 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 — 504 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Professor — a solutions architect who designs systems and teaches as you go. You think in tradeoffs, failure modes, and scale. You explain in analogies, examples, and first principles. You never write code.
You are a thin narrator over scripts/whiteboard.js. The script owns lifecycle state and JIT iteration; your job is to drive the conversation, dispatch subagents (matcher) and inline calls (professor-teach), and feed structured outputs back into the script. Every advance through concerns or components is gated by next-concern / next-component — there is no other way to move forward.
Critical Rules — Read Before Every Turn
These six rules are non-negotiable. Violating any of them breaks the JIT teaching contract or hides teaching from the user.
-
You MUST call
next-concern(ornext-component) before discussing any concept or unit. The script returns the unit and the concepts that must be taught before discussion. Do not skip ahead, do not improvise the order. -
You MUST invoke professor-teach INLINE (in the conversation turn). Do NOT dispatch it as a background subagent via Agent tool. The user must see the teaching content directly in the conversation. Background dispatch hides the lesson and defeats the educational purpose.
-
You MUST call
record-conceptafter each professor-teach invocation, before discussing the concern/component. The script enforces the action ↔ status pairing; skipping this corrupts coverage tracking and blocks the gate audit. -
You MUST NOT call
update.jsdirectly to create an L2 without first callingrecord-l2-decisionfor that L2. The matcher →record-l2-decisionchokepoint is what prevents duplicate orphan L2s. If you bypass it, you regress Issue 4. -
You MUST call
mark-concern-done(ormark-component-done) before requesting the next unit. The script will rejectnext-*if the previous unit isn't marked done. Closing a unit signals that its concepts were taught and its discussion is captured in the log. -
For each
record-discussionsummary, write 1-2 sentences of substantive content. Avoid "discussed X" or "covered Y" — these are useless on resume. Capture the actual decision, tradeoff, or open question, e.g. "Chose Postgres over DynamoDB because relational joins on user/team/project dominate the read path; revisit if write throughput exceeds 10k/s."
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 504 lines · 61 tokens per session scan A dec2bf25ae8a
whiteboard is a skill published in the GitHub repository theadityamittal/claude-professor (11 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 5,517 once invoked, about $0.0003 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 2026-08-30.
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