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/william2333zz/trustshell/audit-agent-codenpx skills add William2333ZZ/trustshell --skill audit-agent-codegit clone --depth 1 https://github.com/William2333ZZ/trustshellWhat 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 | $0.00102 | $0.01403 |
| Opus 5 | $0.00051 | $0.00701 |
| Sonnet 5 | $0.00020 | $0.00281 |
| Haiku 4.5 | $0.00010 | $0.00140 |
Grade A, and why
audit-agent-code scanned grade A with 1 finding 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 yesterday.
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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Python**: `os.system`, `subprocess(..., shell=True)`, `Path(x) / untrusted`, `open(untrusted,'w')`, How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit the agent's code · framework appsec
The companion to redteam-an-agent. That skill breaks the running
agent through the model. This one breaks it through its own source code — the classic appsec
layer the model's judgment never touches.
Written from real engagements: two well-defended agents whose LLM refused every prompt-injection attempt were broken here instead — one by an unauthenticated path traversal in a channel's file handler, one by a fail-open sandbox. The model can't defend a code path it never sees.
Why this layer
Flagship agents increasingly harden the LLM layer: they refuse injected commands, resist memory poisoning, even decode obfuscated payloads to inspect them. Good. But the code around the model — the channel that parses an inbound message, the handler that saves an attachment, the loader that reads config/skills, the toggle that turns the sandbox on — is ordinary software, and it is frequently not hardened. An attacker who reaches those paths bypasses the model entirely. Audit them like you'd audit any web app.
Authorization & safety
Follow every rule in ../README.md: authorized target, disposable environment,
harmless proof, responsible disclosure. Reading source is safe; never run untrusted target code
on a machine you care about.
The untrusted-input boundaries (where to look)
Trace every place external, attacker-influenced data enters, and follow it to a dangerous sink:
- Channel / message handlers — attachment
filename→ file write (path traversal);sender_id→ path; message body → any sink. These run on every inbound message, often before auth. - File / media / download tools — path built from untrusted input; write/read outside the intended dir; symlink following.
- Config / skill / MCP / plugin loaders — untrusted content → deserialization (
pickle,yaml.load,eval), dynamic import, or a path. - The security controls themselves — the sandbox toggle, the guard, the auth gate. Audit the control, not just the happy path.
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.
- yesterday First seen · 86 lines · 102 tokens per session scan A 1435dd02c68a
audit-agent-code is a skill published in the GitHub repository William2333ZZ/trustshell (1 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,403 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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