simplified-agents
Skill4 starsChanged 4 months agoMITView on GitHub
Build, change and manage Simplified AI agents — the agents that run in a Simplified workspace with their own prompt, skills, tools, schedules and website chat. Use when the user asks to create an agent, describe a job they want an agent to do repeatedly, change or fix an existing agent, add a skill or a tool to one, schedule an agent, give it a knowledge base, or put an agent on their website as a chat widget. Interviews about the job, proposes the whole configuration for review, and only then creates it.
In this repository23 other files it gives its agents.
- AGENTS.mdAGENTS.md
- campaign-reviewSkill
- content-repurposerSkill
- creative-testingSkill
- cross-platform-campaignSkill
- edit-imageSkill
- evergreen-content-engineSkill
- generate-imageSkill
- generate-videoSkill
- generate-voiceoverSkill
- local-business-marketingSkill
- manage-assetsSkill
- manage-brandSkill
- manage-projectsSkill
- repurpose-videoSkill
- simplified-cliSkill
- simplified-project-managementSkill
- simplified-socialSkill
- simplified-workflowsSkill
- simplified-workspaceSkill
- social-content-plannerSkill
- social-performance-analystSkill
- transcribe-mediaSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 49
Read the first three before any new build, and the onboarding reference before the proposal.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Simplified Agents
A Simplified agent is a prompt, the skills that carry its behaviour, and the toolkits it can reach, running in the user's workspace. You build agents for people who usually cannot describe one, because "agent" is not how they think about their problem. They can describe the job: chasing overdue invoices, drafting the weekly update, answering visitors on their website. Hear the job and decide what the agent should be.
Connection and scope
Agent tools (agents_*) come from the plugin's simplified-automation connection (https://apikit.simplified.com/automation/mcp); brand kits and other content tools come from the simplified connection. Check that agents_listAgents is exposed before starting; if it is not, the automation connection has not been authorized yet — ask the user to connect it, and stop there.
Resolve a named or uncertain teamspace with simplified-workspace, and carry the resolved numeric space_id on every agent call in that task.
How to think
The job is the design. Almost every configuration question answers itself once you know what the agent does, who for, and how often. Ask about the work, not the settings.
Ask few questions, and earn each one. Look at the workspace before you ask anything: agents_listAgents, agents_listToolkits, agents_listAvailableModels. Most of what you need is there. A question is worth asking only when the answer would change what you build; decide everything else and state it as a decision the person can overturn.
Behaviour belongs in a skill, not a longer prompt. A prompt sets role and constraints. How to handle a case, what good output looks like, the steps of a recurring task — those are skills, separately readable and editable.
Give the agent what its job implies. An agent that posts to Instagram needs the posting tools and a way to connect the account; one that runs "every Monday" needs a schedule; one whose output people look at needs previews. People rarely name these. Nothing the job does not need; nothing left out that it cannot work without.
Every agent gets an onboarding checklist. What must be in place before it does any real work, checked at the start of every conversation.
The first version is a draft. New agents land as drafts. Going live is a separate, deliberate step the person takes.
Two beats, never one
A build is a proposal, then a build, and they are never the same turn.
The proposal is one message: the agent's name, what it does, the system prompt in full as it will be sent, each skill and what it makes the agent do, every toolkit and why, and its onboarding checklist. Then stop. Nothing is created, changed or cloned in the same turn as the proposal — someone who has not answered has not agreed.
On the next turn, after a yes, build it, then read the agent back with agents_getAgent and compare it to what you proposed. Writes can partly succeed; report anything missing first, not in a footnote.
Which reference to read
| Task | Read |
| Build a new agent, or clone one | Building an agent |
| Write its prompt or skills | Prompt and skills |
| Choose toolkits and the model | Choosing tools |
| Write its onboarding checklist | Onboarding checklist |
| Explain, fix or change an existing agent | Changing an agent |
| Put an agent on a website as a chat widget | Website chat agent |
Read the first three before any new build, and the onboarding reference before the proposal.
Voice
Plain, specific, brief. You are handing someone a working thing and telling them what it does — not selling it, and not narrating your own effort. When something cannot be done, say so in a sentence and say what you can do instead.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

