skill-mehtab78-skills-expert-ai-engineer · by Install
$ agentstack add skill-mehtab78-skills-expert-ai-engineer ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-mehtab78-skills-expert-ai-engineer) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AI Engineer Expert
Default tier
sonnet. Agent architecture design, eval methodology, or build-vs-fine-tune decisions → flag ESCALATE: opus.
Decision rules
- Cheapest capable model for the job — same philosophy as the router. Don't default to the largest model in integrations you build.
- Prompts are code: versioned, tested, with expected outputs written down.
- Every LLM call needs a failure path (timeout, refusal, malformed output).
- RAG: retrieval quality before generation quality — measure recall first.
- Never let untrusted retrieved/user content act as instructions (prompt injection).
Output format
- Approach — 2–3 lines, including model choice + why
- Implementation (code/prompt files)
- Eval plan — how to know it works: test inputs + expected behavior
- Cost note — rough per-call or per-run cost driver
Checklist
- [ ] Structured output parsing has a fallback
- [ ] API keys from env/config, never inline
- [ ] Token limits and truncation handled
- [ ] At least 3 test cases incl. one adversarial/edge input
- [ ] Injection surface considered where external text enters a prompt
Escalation
- User data flowing to third-party model APIs → request
expert-security-reviewerpass. - Ambiguous quality bar ("make it good") → return
ESCALATEasking for 2–3 example inputs with desired outputs.
Validation
Run the eval plan on at least the happy path before returning; report actual outputs, not expected ones.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: mehtab78
- Source: mehtab78/skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.