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 skills add stefan-stepzero/shipkit --skill shipkit-prompt-auditgit clone --depth 1 https://github.com/stefan-stepzero/shipkitWrote 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/stefan-stepzero/shipkit/shipkit-prompt-audit)<a href="https://agentmods.dev/skills/stefan-stepzero/shipkit/shipkit-prompt-audit"><img src="https://agentmods.dev/badge/skills/stefan-stepzero/shipkit/shipkit-prompt-audit/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/stefan-stepzero/shipkit/shipkit-prompt-audit"><img src="https://agentmods.dev/badge/skills/stefan-stepzero/shipkit/shipkit-prompt-audit.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00036 | $0.04327 |
| Opus 5.5 | $0.00014 | $0.01731 |
| Sonnet 5.5 | $0.00007 | $0.00865 |
| Haiku 4.5 | $0.00004 | $0.00433 |
Grade A, and why
shipkit-prompt-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 1mo 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 — 504 lines — stays where its author put it; the contents beside it link to each section on GitHub.
shipkit-prompt-audit - LLM Prompt Architecture Audit
Purpose: Find structural problems in how your app talks to LLMs — monolithic prompts, missing fallbacks, sequential bottlenecks, unvalidated outputs, unsafe inputs.
What this is NOT: Not a prompt text quality checker. Not "make this prompt better." This audits the engineering architecture around prompts — how they're decomposed, chained, validated, and recovered from failure.
When to Invoke
/shipkit-prompt-audit— audit all LLM integrations/shipkit-prompt-audit src/ai/— focus on specific directory- "Audit my prompts", "Check prompt architecture", "LLM pipeline review"
- "Are my AI calls structured well?"
- "Check my prompts for anti-patterns"
Workflow position:
- After implementing AI features, before shipping
- When AI features feel slow or unreliable
- During architecture review of LLM-heavy applications
- When scaling from prototype to production AI
Prerequisites
Required:
- Project has LLM integrations (API calls to OpenAI, Anthropic, Gemini, etc.)
Recommended:
.shipkit/stack.json— Knows which AI SDKs are in use.shipkit/architecture.json— Knows pipeline design intent
If no LLM integrations found: Report cleanly and exit. Don't fabricate findings.
Process
Completion Tracking
After discovering integration points (Step 1), create tasks:
TaskCreate: "Map pipeline topology"TaskCreate: "Audit all 10 dimensions (PA-DEC through PA-REF)"TaskCreate: "Write prompt-audit.json (schema-validated)"TaskCreate: "Present summary to user"
TaskUpdate each task to in_progress when starting it, completed when done.
If using parallel subagents for dimension auditing, verify ALL agent results are collected before synthesizing. Do NOT write the report until all 10 dimensions have findings (even if finding is "N/A").
Step 1: Discover LLM Integration Points
Scan the codebase for all LLM-related code using detection patterns.
What ships with it
6 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.
- 1mo ago First seen · 504 lines · 36 tokens per session scan A 11cab3ed1c3b
shipkit-prompt-audit is a skill published in the GitHub repository stefan-stepzero/shipkit (1 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 4,327 once invoked, about $0.0001 per session on Opus 5.5. 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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