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 agentmods add skills/robzilla1738/roberts-skills/fuzznpx skills add robzilla1738/roberts-skills --skill fuzzgit clone --depth 1 https://github.com/robzilla1738/roberts-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/robzilla1738/roberts-skills/fuzz)<a href="https://agentmods.dev/skills/robzilla1738/roberts-skills/fuzz"><img src="https://agentmods.dev/badge/skills/robzilla1738/roberts-skills/fuzz.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 | $0.00076 | $0.01641 |
| Opus 5 | $0.00038 | $0.00821 |
| Sonnet 5 | $0.00015 | $0.00328 |
| Haiku 4.5 | $0.00008 | $0.00164 |
Grade A, and why
fuzz 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fuzz & Property Harness
Static hunting finds suspicious code. This skill proves it — or finds what static review missed — by executing the code against generated inputs and checking that invariants hold. It's the dynamic counterpart to the bughunt lenses.
Use it standalone to harden a function, or as the confirm step of a hunt: convert a
Probable finding into Confirmed with a failing test.
This skill runs code. The loop is: discover → pick technique → generate harness → execute → shrink → emit. It writes only test/harness code, never product fixes.
The loop
| Step | Do |
|---|---|
| 1 Discover | Find fuzzable targets. For a whole-module pass, run python3 ${CLAUDE_PLUGIN_ROOT}/skills/bughunt/scripts/bughunt.py census --functions — it shortlists pure-ish functions (no obvious side effects) with their params, the best fuzz candidates. For a Probable finding from a hunt, the target is the cited function. |
| 2 Pick technique | Choose property / fuzz / differential / metamorphic (table below) and design the oracle. |
| 3 Detect & generate | Detect the project's test runner and property/fuzz library (table below). Generate a harness in the project's own conventions. If the library isn't installed, ask before installing it — never add a dependency silently. |
| 4 Execute | Run it via the project's runner. A real run that fails is the proof; a green run is evidence the property holds for the explored space. |
| 5 Shrink | Let the framework minimize the failing input (most shrink automatically), or minimize by hand to the smallest case that still fails. |
| 6 Emit | Keep or present the shrunk case as a named regression test, and record a findings-JSON entry with verified: {by:"fuzz", method:"property-test", verdict:"upheld"} so triage/merge can fold it into the report as Confirmed. |
When there's no test infrastructure
If the project has no test runner or the property library can't be installed (offline, user
declines), degrade gracefully: generate the harness and the concrete boundary inputs,
hand-trace the most suspicious case, and present the ready-to-run harness with a one-line
"install X and run Y to confirm" — clearly marked not executed. Never report a fuzz
finding as Confirmed unless you actually ran it.
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.
- 5d ago First seen · 109 lines · 76 tokens per session scan A 81895bf7887b
fuzz is a skill published in the GitHub repository robzilla1738/roberts-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 76 tokens to every session and 1,641 once invoked, about $0.0004 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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