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/agentic-dev3o/devx-plugins/auditnpx skills add agentic-dev3o/devx-plugins --skill auditgit clone --depth 1 https://github.com/agentic-dev3o/devx-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 | $0.00118 | $0.01470 |
| Opus 5 | $0.00059 | $0.00735 |
| Sonnet 5 | $0.00024 | $0.00294 |
| Haiku 4.5 | $0.00012 | $0.00147 |
Grade A, and why
auditing-agentic-systems 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 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.
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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic System Audit
Target: $ARGUMENTS (path to file, directory, prompt text, or description of the system to audit)
Workflow
Progress checklist:
Agentic System Audit:
- [ ] Step 1: Resolve audit target
- [ ] Step 2: Detect framework and identify components
- [ ] Step 3: Evaluate across six dimensions
- [ ] Step 4: Classify findings and assign severity
- [ ] Step 5: Produce scored gap analysis
Step 1: Resolve Audit Target
The target is one of:
- Agent definition file — markdown with frontmatter, JSON/YAML config, or code (TS/Py)
- Inference call site — code that builds messages and calls an LLM API
- Tool loop — code that iterates on tool calls until done
- Prompt text — a system prompt, user prompt template, or both
- Description — the user describes the system in natural language
If $ARGUMENTS is a path, read the file(s). If a directory, list it and ask the user to scope to specific files when more than ~5 are present. If it is prompt text, treat the text as the target directly. If it is a description, ask one clarifying question only when the framework or component shape is genuinely ambiguous.
Step 2: Detect Framework and Identify Components
Identify the runtime context to ground recommendations:
- Anthropic SDK (
anthropic,@anthropic-ai/sdk) — supports prompt caching, adaptive thinking, effort parameter, parallel tool use - OpenAI SDK (
openai) — tool calling, structured outputs, parallel calls, no native prompt caching as of writing - Vercel AI SDK (
ai,@ai-sdk/*) — provider-agnostic tool calling, streaming, structured generation - OpenAI Agents SDK (
@openai/agents) — handoffs, guardrails, tracing - Custom / framework-less — raw HTTP calls, hand-rolled loops
Identify the components present in the target: system prompt, user prompt, tool definitions, tool loop, memory/state, evals, guardrails, retries, streaming.
Step 3: Evaluate Across Six Dimensions
For each dimension, load the matching reference and walk the checklist against the target. Do not declare a finding without concrete evidence (a quote from the prompt, a line in the code, or a missing pattern that should be present).
What ships with it
7 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.
- yesterday First seen · 110 lines · 118 tokens per session scan A fd19610d0a43
auditing-agentic-systems is a skill published in the GitHub repository agentic-dev3o/devx-plugins (11 stars, last pushed 14d ago), licensed MIT. It adds 118 tokens to every session and 1,470 once invoked, about $0.0006 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 2026-08-30.
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