Agent Spec Skill

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.

Not quite this? Use agent-observability-spec when the agent is in production and needs tracing and alerting.

Required Inputs

Ask for these only if they aren't already provided:

• Job to be done — the outcome the agent owns, and the boundary of its authority.
• Tools/actions — what it can call (read APIs, write actions, code execution), and which are irreversible.
• Autonomy level — fully autonomous, propose-then-approve, or co-pilot.
• Risk surface — what's the worst thing a wrong action could do (spend money, send a message, delete data)?
• Success definition & escalation — how "done" is judged, and when it must hand off to a human.

Output Format

Agent Spec: [name]

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).

Quality Checks

• Every tool is marked reversible/irreversible, and every irreversible action has a human gate
• There is a hard max-steps and max-cost budget — the loop cannot run unbounded
• Escalation triggers are explicit (confidence, repeated failure, out-of-scope, high-risk)
• The safe default on uncertainty is "stop and ask", not "guess and act"
• Evaluation includes a safety metric (wrong/unauthorised actions), not just task success
• Non-goals and authority limits are stated, not implied

Anti-Patterns

• Do not give an agent irreversible actions without an approval gate — autonomy and irreversibility together is how agents cause real damage
• Do not omit a step/cost budget — an agent that can loop is an agent that can rack up cost or thrash forever
• Do not measure only task success — an agent that completes the task by taking a wrong action has failed
• Do not let the agent invent tool calls or arguments — validate against the schema and fail safe
• Do not skip the "what's the worst case" analysis — the risk surface determines how many guardrails you need

Based On

Tool-using / agentic design practice — bounded control loops, least-privilege tools, human-in-the-loop approval, and safety evaluation.

Example Trigger Phrases

• "Design an AI agent."
• "Define an agent's tools and guardrails."
• "Scope what an agent is allowed to do."
• "Write an agent spec/PRD."