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/orin-dx/agent-plugins/auditnpx skills add orin-dx/agent-plugins --skill auditgit clone --depth 1 https://github.com/orin-dx/agent-pluginsWhat 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 | $0.00228 | $0.01160 |
| Opus 5 | $0.00114 | $0.00580 |
| Sonnet 5 | $0.00046 | $0.00232 |
| Haiku 4.5 | $0.00023 | $0.00116 |
Grade A, and why
audit 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 2d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ranger — Cross-Language Bug-Hunting Skill
How It Works
One skill, one fixed 5-phase pipeline (recon → scanner → boundary-tracer [conditional] → adversary → exit-gate). What changes is which part of the request drives it:
- Full sweep — no category or candidate named: every hazard taxonomy gets scanned.
- Targeted scan — the request names one hazard category (e.g. "unhandled promises"): the same pipeline runs, scoped to that category.
- Verify one candidate — the request hands ranger an already-identified bug: adversary runs directly against it, skipping the sweep.
- Post-remediation check — the request asks whether prior findings are resolved: exit-gate re-reads the code from scratch and confirms.
There is no ranger/scan or ranger/verify to invoke separately — one skill directory (skills/audit/), the request itself determines which phase does the work.
Pipeline
workspace → recon → scanner → [boundary-tracer?] → adversary → exit-gate → finding-report@1
- Recon — builds verified manifest: live files, dead files, entry points, language.
- Scan — loads language-specific hazard reference, runs grep patterns against live files only, emits candidate@1 list.
- Boundary Tracer (conditional) — traces field survival for T7 and T10 candidates. Skipped for all other taxonomies.
- Adversary — reads actual code, traces control flow, and refutes candidates. Batched by crate/module to evaluate multiple candidate signals in one pass, minimizing subagent overhead. Confirms when a concrete failing scenario can be stated; emits plausible when no refutation exists but reachability depends on state outside the code (config, an external caller, environment).
- Exit Gate — after remediation, re-reads code from scratch, checks all confirmed findings are gone, scans for sibling gaps, verifies compile and tests. Plausible findings pass through to the verdict for human review, never as remediation targets. Escalates to human when retry_count exceeds 3.
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.
- 2d ago First seen · 66 lines · 228 tokens per session scan A 6f10662c6415
audit is a skill published in the GitHub repository orin-dx/agent-plugins (1 stars, last pushed 6d ago), licensed MIT. It adds 228 tokens to every session and 1,160 once invoked, about $0.0011 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
campus-assignment-cleaner
Activates when a student or campus developer needs to make a working-but-messy codebase submission-ready before uploading it for grading. Trigger phrasings include "clean up my assignment before submitting", "remove debug prints and commented code", "tidy this up before I upload it to Moodle/Canvas/GitHub Classroom"…
git-workflow-and-versioning
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, opening or reviewing a pull request (PR), pushing to a remote, or when you need to organize work across multiple parallel streams. Use when cutting a release, choosing a semantic version bump…
advogado-criminal
Advogado criminalista especializado em Maria da Penha, violencia domestica, feminicidio, direito penal brasileiro, medidas protetivas, inquerito policial e acao penal.
agent-evaluation-reporting
Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.
agent-orchestration-multi-agent-optimize
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
adhx
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.