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.
git clone --depth 1 https://github.com/apisec-inc/apisec-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/rules/apisec-inc/apisec-skills/error-sanitization)<a href="https://agentmods.dev/rules/apisec-inc/apisec-skills/error-sanitization"><img src="https://agentmods.dev/badge/rules/apisec-inc/apisec-skills/error-sanitization/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/rules/apisec-inc/apisec-skills/error-sanitization"><img src="https://agentmods.dev/badge/rules/apisec-inc/apisec-skills/error-sanitization.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 | $0.00197 | $0.00197 |
| Opus 5.5 | $0.00079 | $0.00079 |
| Sonnet 5.5 | $0.00039 | $0.00039 |
| Haiku 4.5 | $0.00020 | $0.00020 |
Grade A, and why
error-sanitization 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.
What it actually says
Error sanitization rule
API error responses must never include stack traces, internal field names, database error messages, ORM errors, or any internal system detail.
Never generate error handlers that return err.message, error.stack, or raw exception objects directly to the client.
Insecure pattern to refuse: res.status(500).json({ error: err.message })
Safe pattern to generate instead: res.status(500).json({ error: 'Internal server error', requestId: req.id })
Always generate a generic user-facing message plus a unique request/correlation ID for internal tracing. The requestId allows ops teams to find the real error in logs without exposing it to clients.
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 · 25 lines · 197 tokens per session scan A 07f6fdedd854
error-sanitization is a cursor rule published in the GitHub repository apisec-inc/apisec-skills (21 stars, last pushed 23d ago), licensed MIT. It adds 197 tokens to every session, about $0.0008 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 2026-08-30.
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