Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hutzelmann/thesis-proposal-skillsnpx agentmods add skills/hutzelmann/thesis-proposal-skills/proposal-troubleshootWrote 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-troubleshoot)<a href="https://agentmods.dev/skills/hutzelmann/thesis-proposal-skills/proposal-troubleshoot"><img src="https://agentmods.dev/badge/skills/hutzelmann/thesis-proposal-skills/proposal-troubleshoot/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-troubleshoot"><img src="https://agentmods.dev/badge/skills/hutzelmann/thesis-proposal-skills/proposal-troubleshoot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 32 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00066 | $0.02740 |
| Opus 5 | $0.00033 | $0.01370 |
| Sonnet 5 | $0.00013 | $0.00548 |
| Haiku 4.5 | $0.00007 | $0.00274 |
Grade A, and why
proposal-troubleshoot 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Troubleshooting
Diagnoses a problem with the proposal skills themselves: a failing script, a rule applied wrongly, output that contradicts a stated mandate. Most causes turn out not to be defects and end here; where one is, the outcome is a bug report a maintainer can act on.
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.
Work the ladder below before you collect anything, and stop at the first rung that explains the problem. Most reported problems are not defects: a stale install, a model that cannot do the task, a supervisor's override doing its job, or output the user simply dislikes. Each of those ends the run with an answer and no report. Assemble a report only for a failing script, a violated mandate, or a cause the ladder cannot identify — and transmit nothing: the bundle is written into the user's own workspace, and who sees it is their decision.
Naming the rung is part of the answer. A report that says "it did not work" costs a maintainer a round-trip; a report that says "rung 4, the check skill edited my file" is actionable on sight. When no rung fits, say so and record the cause as unidentified rather than choosing the rung that sounds closest.
Target
Establish two things before diagnosing: which skill misbehaved, and what the user expected instead. If the problem concerns a proposal file, resolve it the way the other skills do — explicit mention wins; exactly one markdown file ending in a --- metadata block auto-picks; several candidates means listing them and asking. The search space is the workspace's proposal location: the working directory, unless the workspace guidelines.md sets [paths] proposals to a subdirectory. Never treat <slug>.notes.md or anything inside bug-report/ as the proposal.
What ships with it
4 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 · +2 lines 7618a2433df7
- 11d ago First seen · 133 lines · 66 tokens per session scan A 5694b733910d
proposal-troubleshoot is a skill published in the GitHub repository hutzelmann/thesis-proposal-skills (7 stars, last pushed 8d ago), licensed MIT. It adds 66 tokens to every session and 2,740 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.
Other skills, from other repositories
skill-debug
Debug a reproducible symptom with a bounded feedback loop and original-scenario verification.
systemic-issue-triage
Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.
issue-root-resolution
Trigger: root audit, atacar la raíz, issue roots, backlog roots, mechanism map, deletion-driven fix, resolver issues de raíz, close outdated issues. Audit and resolve issue clusters by verified root cause.
lore
SpecStory Lore - mine your SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more) into a persistent corpus, surface your reproducible workflows with corroborated evidence, and interactively forge the chosen ones into skills installed across all your agent harnesses. Use when the user…
workthreads
SpecStory Workthreads - a weekly work-thread rollup across a team's repos from SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more). It groups the window's sessions into threads of work per project and labels each new / open / recently closed, so a lead sees what shipped, what is still…
docs-manage
Manage the Grounded Docs MCP Server documentation index. Covers scraping and indexing documentation from URLs or local files, refreshing existing indexes with changed content, and removing libraries from the index. Use when you need to add, update, or delete indexed documentation.