agent-spec-bfcc8e — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited agent-spec-bfcc8e (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
An agent is a model plus tools plus a loop — and the danger lives in the tools and the loop, not the model. This skill specifies an agent so its authority is explicit: what it can do, what needs a human yes, and what happens when it's wrong. Scope and guardrails first; cleverness second.
Ask for these only if they aren't already provided:
1. Goal & scope — the job in one sentence; explicit non-goals and authority limits.
2. Tools / actions — a table; mark each action's reversibility and required permission.
| Tool | Purpose | Reversible? | Gate |
|---|---|---|---|
| search_kb | read context | yes | none |
| send_email | notify | no | human approval |
3. Control loop — plan → act → observe → reflect; the stopping condition; and a hard max-steps / max-cost budget so it can't loop forever.
4. Guardrails & approval gates — which actions require a human yes (default: anything irreversible, outbound, or spending), input/output validation, and allow/deny lists. Pair irreversible actions with a dry-run preview (see action-runner).
5. Memory & state — what it remembers within a task vs. across tasks, and where (link a professional-brain for durable memory).
6. Escalation & handoff — the triggers that stop the agent and route to a human (low confidence, repeated failure, out-of-scope request, high-risk action).
7. Evaluation — task success rate, action correctness, and safety (false-action rate). Define with an ai-eval-plan, and test on adversarial/trap tasks.
8. Failure handling — timeouts, tool errors, hallucinated tool calls, and the safe default (stop and ask, never guess on a high-risk action).
Tool-using / agentic design practice — bounded control loops, least-privilege tools, human-in-the-loop approval, and safety evaluation.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.