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 ShieldNet-360/secure-vibe --skill error-handling-securitygit clone --depth 1 https://github.com/ShieldNet-360/secure-vibeWrote 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/shieldnet-360/secure-vibe/error-handling-security)<a href="https://agentmods.dev/skills/shieldnet-360/secure-vibe/error-handling-security"><img src="https://agentmods.dev/badge/skills/shieldnet-360/secure-vibe/error-handling-security/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/shieldnet-360/secure-vibe/error-handling-security"><img src="https://agentmods.dev/badge/skills/shieldnet-360/secure-vibe/error-handling-security.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 | $0.00067 | $0.01436 |
| Opus 5.5 | $0.00027 | $0.00574 |
| Sonnet 5.5 | $0.00013 | $0.00287 |
| Haiku 4.5 | $0.00007 | $0.00144 |
Grade A, and why
error-handling-security 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Error-Handling Security
Rules (for AI agents)
ALWAYS
- Catch exceptions at the boundary — HTTP handler, RPC method, message consumer —
and decide there what crosses to the client and what stays server-side. Record
the failure with a correlation ID under
logging-security's policy; do not bypass its redaction rules to attach "full context", which is how request bodies, cookies and tokens end up in logs. - Put only client-actionable information in an external error: a stable error code, a short human-readable message, and the correlation ID. Anything the caller cannot act on belongs in the log entry that shares that ID.
- Keep responses consistent within an error class, and make security-equivalent
outcomes externally indistinguishable. Different classes may and should differ —
400 VALIDATION_FAILED,401,403,409,429are not a finding. What must not differ is a pair of outcomes whose distinction reveals protected state: whether an account exists, whether a record the caller may not see is present.auth-securityowns making the login paths cost the same; this rule owns what they return. - Disable developer error pages and detailed exception rendering in every
non-development environment, and route unhandled errors through the framework's
production error handler. This is a wiring decision, not the same thing as an
environment variable reading
production— the setting and its name differ per framework, andreferences/framework-error-pages.mdhas them. - Render every external error through one helper, so the sanitization rules live in a single place instead of being re-derived at each handler.
- Consult
api-securityfor the status code and response envelope,logging-securityfor the internal record, andauth-securityfor making two authentication failures cost the same. This skill owns one decision — what crosses the boundary.
NEVER
- Serialize exception or debug internals into an external response: stack traces, SQL fragments and constraint names, filesystem paths, internal hostnames, dependency or framework versions.
- Silently swallow an unexpected exception — a bare
except: pass,catch {},rescue nil. Let it reach the nearest designated error boundary, which logs it and converts it to a sanitized response. A narrowly-typed, commented suppression of an expected condition is fine; the finding is the broad or unexplained one. - Withhold client-actionable validation detail in order to make the API harder to
explore, or smuggle server state into it. Which field failed and why is the
public contract — that is what RFC 9457 problem details and the GraphQL
errorsarray are for — and it belongs in the response. Record existence, authorization state, and SQL or schema internals are not the contract and do not.
What ships with it
4 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 · 112 lines · 67 tokens per session scan A 8ed08f3f4d5a
error-handling-security is a skill published in the GitHub repository ShieldNet-360/secure-vibe (22 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 1,436 once invoked, about $0.0003 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.
Other skills, from other repositories
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
ai-agent-evaluation-benchmarking
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
ai-agent-tool-routing
Autonomous AI agent tool router register: schema registration, runtime parameter coercion, idempotency keys, and error recovery policies.
ai-prompt-regression-testing
Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
documenter-vitepress
Use when setting up or developing a Julia based documentation site with DocumenterVitepress.jl. Also use when the user mentions DocumenterVitepress, VitePress for Julia docs, or wants to preview docs locally with hot reload.
agent-teacher
A teaching aid that explains technical ideas with a small runnable code example and a guided walkthrough. It is meant for learning how something works, not fixing existing code.