creator

name	creator
description	Create, improve, and review AgentUse agent files. Use when authoring .agentuse agents, designing agent workflows, configuring frontmatter, adding MCP servers, subagents, schedules, approval gates, skills, or choosing project structure and automation patterns.

AgentUse Creator

Use this skill when creating or improving .agentuse files: markdown with YAML frontmatter and plain-English instructions. The filename is the agent name.

Basic Agent Shape

---
model: anthropic:claude-sonnet-5
description: "Short action-oriented purpose"
---

You are a focused autonomous agent.

## Task
Describe exactly what the agent should accomplish.

## Output
Describe where results should go and what format they should use.

Common Frontmatter

model: anthropic:claude-sonnet-5
description: "Analyze daily metrics and send a concise summary"
timeout: 600
maxSteps: 100
schedule: "0 9 * * *"
subagents:
  - path: ./researcher.agentuse
    name: research
    maxSteps: 50
mcpServers:
  filesystem:
    command: npx
    args: ["-y", "@modelcontextprotocol/server-filesystem", "."]

Authoring Checklist

• A concrete job the agent can finish without interactive supervision.
• model: set explicitly; short description: if it may be listed or used as a subagent.
• Tools and MCP servers declared in frontmatter, not assumed ambient.
• Inputs, outputs, destinations, and success criteria stated in the body.
• Multi-role work: subagents with clear names and maxSteps limits.
• Recurring work: YAML schedule: + a note that agentuse serve must run.

Write Lean: Hard-Code Invariants, Delegate Judgment

The prompt is a brief, not a manual. It is re-sent on every step, so length is a recurring token cost and a long prompt buries the rules that matter. Pin down only what must be exact:

• safety boundaries (read-only, never call X, which store to write),
• exact commands, paths, and flags the model cannot guess,
• the output schema and where it goes,
• ordering that changes the result.

For everything else — how to investigate, how to phrase, the long tail of edge cases — state the goal and the constraint, then let the model decide. Spelling out every branch makes the agent brittle on the case you did not enumerate.

Over-specification smells: the same rule in three places, a paragraph justifying why a step exists, an enumerated decision tree derivable from one sentence of intent. Write what a competent teammate needs, not a spec.

Skills are instructions, not tool grants: declare an agent's tools in frontmatter even when a skill documents them with allowed-tools. Put reusable instructions in .agentuse/skills/<name>/SKILL.md or install with agentuse add.

Source Precedence: Skills Are Defaults, Learnings Override Them

The runtime composes one prompt from layered sources, in this precedence (highest first): agent instructions → Learned Guidelines → Skills → other reference files. The system prompt's operational/safety rules sit above all of these. This shapes where a rule belongs:

• Put soft defaults in skills. Don't bake a hard "never do X" into a skill that a learning should be able to override — a captured correction outranks a skill default, so an absolute skill rule fights the feedback loop.
• State a rule once, at the right layer, and reference it. The same craft rule copied into both a skill and the agent drifts; the lower-precedence copy then silently wins (this is the "same rule in three places" smell above, seen from the runtime side).
• learning: true (sugar for capture + apply) injects the agent's stored learnings every run — for delegated subagents too, not just top-level runs. So a leaf's prior-run corrections actually reach it; rely on that instead of hand-restating past corrections in the prompt.

Gotchas

• No builtin grep/glob. Only filesystem_read|write|edit exist, and filesystem_read returns the whole file unless you pass limit/offset. For big or structured files, grant grep/rg/jq via bash and tell the agent to search, not slurp. A read agent given only cat/ls falls back to whole-file reads — the exact context blowup to avoid.

• Approval gates are async. approval: true / await_human gates suspend the run; timeout: does not tick during the wait. Size timeout: for the active work between gates, not human response time.

• Agents cannot prompt the user mid-run. Never write "stop and ask the user to do X." The only branches at a blocker: exit with a clear error, record to the store and continue/stop, or fire an approval/notification. The approval gate is the only human-in-the-loop path.

• Validate models against agentuse models. The catalog moves; check it before calling a name invalid. Don't infer limits from other providers' naming (e.g. "5.5 can't exist because provider Y stops at 5.2").

• Defer to skills; don't inline their internals. Reference a skill by name (/linkedin) and never copy its drift-prone internals (script paths, eval invocations, file layout). Do repeat the durable steps/context the run hinges on inline, since the skill may not load that turn. Rule: reference what changes, repeat what doesn't.

• Skill scripts read via bash need explicit allowlists. Grant tools.filesystem read on the skill dir (absolute path — ~ may not expand) and a narrow bash.commands like cat /Users/<you>/.claude/skills/<name>/scripts/*. Not a blanket cat *.

• Match channel prose to the frontmatter key. channels.slack is the key. Don't describe Slack delivery as notifications.routes; the body must name the key the frontmatter uses.

References

Don't hardcode a doc list here — it goes stale. Fetch the canonical index and load the specific page(s) you need:

https://docs.agentuse.io/llms.txt

It enumerates every current guide and reference page with a short description. Pick the matching .md URLs and fetch them directly.