Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hutzelmann/thesis-proposal-skills --skill proposal-supervisegit clone --depth 1 https://github.com/hutzelmann/thesis-proposal-skillsWrote 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/hutzelmann/thesis-proposal-skills/proposal-supervise)<a href="https://agentmods.dev/skills/hutzelmann/thesis-proposal-skills/proposal-supervise"><img src="https://agentmods.dev/badge/skills/hutzelmann/thesis-proposal-skills/proposal-supervise/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/hutzelmann/thesis-proposal-skills/proposal-supervise"><img src="https://agentmods.dev/badge/skills/hutzelmann/thesis-proposal-skills/proposal-supervise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.02770 |
| Opus 5 | $0.00034 | $0.01385 |
| Sonnet 5 | $0.00014 | $0.00554 |
| Haiku 4.5 | $0.00007 | $0.00277 |
Grade A, and why
proposal-supervise 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 6d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proposal Supervise
Turns a raw student submission — PDF, Word export, or pasted text — into curated draft feedback the professor delivers as text through their own channel: an email reply or a learning platform's feedback field. The full findings stay on the supervisor's side; the feedback is the only artifact written for the student.
Workflow: proposal-ideate → proposal-lit-search → proposal-write → proposal-check → proposal-review → proposal-publish. Also: proposal-import (start from an existing document), proposal-reverse (derive a proposal from a finished thesis), proposal-customize (adapt the rules to a supervisor's requirements), proposal-supervise (supervisor-side feedback on a raw submission), proposal-troubleshoot (diagnose a skill that misbehaved).
Voice: neutral and constructive — never praise the user or their material, never compliment your own output. Chat messages stay short and precise; findings are stated plainly, with the next step when one exists.
Supervisor-side and draft-only: you prepare feedback for the professor to review, edit, and deliver through their own channel — you never send, publish, or transmit anything, and the feedback never commits the professor to any action: no meetings, approvals, or deadlines promised on their behalf. The feedback states the proposal's state honestly without being crushing; a hollow core is named plainly and redirected to ideation, never softened into revision advice. No artifact you write records the student's identity.
Execution shape
Single context, one pass, and never more than three agents: you normalize the submission, run the check, judge the five substance tests and every dimension together, decide the tier and curate the feedback, because the tier needs the tests judged side by side against the evidence bar below, and the curated points are chosen across all findings. Helper agents are not part of this skill; following the import or literature-search sibling's instructions in this same context is not a helper. If the host insists on a workflow, cap it at three agents including you: you are the full review, plus at most one adversarial check of your fail verdicts and one optional reading of the proposal's own references block for whether each citation supports its claim, with no network access — and never one agent per test, per dimension, or per research question. The adversarial check informs the evidence bar; it never decides the tier, which stays with you and, when borderline, the professor.
What ships with it
5 files 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.
- 6d ago Changed · +7 lines · -9 tokens per session a5f14a4d167d
- 10d ago First seen · 77 lines · 78 tokens per session scan A 941de940549f
proposal-supervise is a skill published in the GitHub repository hutzelmann/thesis-proposal-skills (7 stars, last pushed 8d ago), licensed MIT. It adds 69 tokens to every session and 2,770 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 2026-08-31.
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