Turn a rough ask into a precise, reusable prompt.
Pins down the role, task, inputs, constraints, output format, and failure modes so the model does what you meant.
Inside: The full role / task / inputs / constraints / output / failure-mode scaffold.
Install it in one line, or paste it in.
~/.claude/skills/ and runs automatically when it is relevant.Connect your context. Set it to your motion.
| Set this | What it is | Example |
|---|---|---|
| Target model | The platform you will paste into | |
| Your defaults | House style for every output | "Plain languageno preamblecite sources" |
| Reusable context | Facts the model should always have | Your companyyour audienceyour product |
Everything the skill does, in full.
Takes a rough description of what you want an AI to do and returns a precise, structured prompt you can paste into any model. It pins down the role, the task, the inputs, the constraints, the output format, and the failure modes, so the model has nothing to guess.
- 11. Extract the job. What is the AI producing, for whom, and what does good look like.
- Assign a role. A "you are a..." that matches the job.
- Specify the inputs. What you will give it, in what form.
- Set the constraints. Length, tone, what to avoid, what never to do.
- Lock the output format. A template the model fills, not a vague ask.
- Add the failure modes. "If you do not have X, say so and stop. Do not invent."
- Add one example if the task is fuzzy. A model copies an example faster than it parses a paragraph.
You are a customer-research analyst. Summarize the call transcript below for a product team that has not heard the call. Output exactly these sections: - One-line verdict (did this account love it, like it, or struggle) - 3 quotes that prove the verdict, with the speaker role - The one feature request that came up - The next step the rep committed to Rules: quote the customer's own words, never paraphrase a quote. If a section has no evidence in the transcript, write "none stated." Do not infer sentiment the words do not support. Transcript: [paste]
Where an operator takes this next.
One well-built prompt is a single task solved. Here's what it looks like running as infrastructure.
A good prompt gets the same answer twice. The system is what gets it a thousand times without you watching.
Feed the prompt into Clay as an enrichment column so every new call transcript gets summarized the same way automatically, not one at a time.
A scheduled Claude task applies the prompt to every call logged that day and posts the summaries to Slack for the product team each morning.
Keep the prompt in a shared doc or repo with a changelog, so when the output drifts, you can see exactly which rule changed and roll it back.
One skill is the on-ramp.
A single skill does one job. Chained into a playbook, or run as a full build, it becomes a system. Here is where this one plugs in.