Install
$ agentstack add skill-aws-samples-sample-agent-skill-eval-sample-agent-skill-eval ✓ 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.
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
Skill Eval — Agent Skill Evaluation Framework
Evaluate Agent Skills across four dimensions: safety (audit), quality (functional), reliability (trigger), and cost efficiency (Pareto classification).
Quick Start
skill-eval audit /path/to/skill # Is it safe?
skill-eval report /path/to/skill # Full grade (audit + functional + trigger)
skill-eval functional /path/to/skill # Quality: with-skill vs without-skill
skill-eval trigger /path/to/skill # Reliability: activation precision
Decision Tree
- "Is this skill safe?" →
skill-eval audit - "Full evaluation with grade" →
skill-eval report - "Full repo security review" →
skill-eval audit --include-all - "Write eval cases" →
skill-eval init, then editevals/ - "Compare two versions" →
skill-eval compare - "Check for regressions" →
skill-eval snapshot, thenskill-eval regression - "Track changes" →
skill-eval lifecycle --save --label v1.0
Commands
| Command | Purpose | |---------|---------| | audit | Security & structure scan (secrets, permissions, spec compliance) | | functional | Quality eval — runs prompts with and without skill, grades output | | trigger | Reliability eval — tests activation precision for relevant/irrelevant queries | | report | Unified grade combining audit (40%) + functional (40%) + trigger (20%) | | compare | Side-by-side comparison of two skills on the same eval cases | | snapshot | Save current audit as regression baseline | | regression | Check for score regressions against baseline | | lifecycle | Version tracking and change detection | | init | Generate eval scaffold from SKILL.md frontmatter |
For detailed flags and examples, see references/cli-reference.md.
Eval File Format
Functional evals (evals/evals.json):
[{"id": "case-1", "prompt": "...", "assertions": ["contains 'expected'"], "files": ["files/input.csv"]}]
Trigger queries (evals/eval_queries.json):
[{"query": "relevant question", "should_trigger": true}, {"query": "unrelated question", "should_trigger": false}]
Scoring
Grades: A (90+), B (80-89), C (70-79), D (60-69), F (<60). Findings deduct: CRITICAL −25, WARNING −10, INFO −2.
For the full security check reference and OWASP mapping, see references/security-checks.md.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: aws-samples
- Source: aws-samples/sample-agent-skill-eval
- License: MIT-0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.