Prompt Kit
The Context Layer Nobody is Talking About Prompt Kit
Prompt Kit: Skills Are Infrastructure Now
This kit operationalizes the core thesis of the article: your best prompting work is evaporating every session, and skills are how methodology compounds instead. These four prompts walk you through the full arc — identifying which tasks should become skills, building them from your actual outputs (not your intentions), hardening them for agent callers, and planning how to share them across teams so expertise becomes institutional instead of personal.
How to use this kit
Work through these prompts in order. Each one builds on the last. Prompt 1 identifies your highest-ROI skill candidates. Prompt 2 takes one of those candidates and builds a production-ready SKILL.md using the output-extraction method from the article. Prompt 3 stress-tests that skill against the agent-caller standard — because "works when I'm watching" and "works when agents call it at 2am" are categorically different bars. Prompt 4 zooms out to the team level: which skills are organizational infrastructure, who should build them, and how do you deploy them.
Run all four prompts in ChatGPT, Claude, or Gemini. These are conversational prompts that ask you questions before producing output — any capable AI assistant will handle them well. For Prompt 2, you'll get the best results by pasting in actual examples of your work, so use a model with a large context window.
You don't have to do all four in one sitting. Prompt 1 is a 30-minute exercise. Prompt 2 is a focused two-hour build session. Prompts 3 and 4 are for when you're ready to move from personal use to production pipelines and team deployment.
Prompt 1: Skill Backlog Audit
Job: Analyzes your recurring AI workflows and identifies which tasks should become skills, ranked by ROI.
When to use: When you suspect you've been re-explaining the same methodology across conversations but haven't mapped which tasks are actually worth encoding. This is your starting point.
What you'll get: A prioritized backlog of skill candidates, each scored against the three qualification criteria (recurrence, methodology-dependence, consistency-sensitivity), with a recommended build order.
What the AI will ask you: Your role, the types of work you do with AI regularly, which outputs vary in quality, and which prompts you find yourself rewriting.
Prompt 2: Skill Builder (Output-Extraction Method)
Job: Takes examples of your actual best work and reverse-engineers them into a production-ready SKILL.md file — extracting the implicit methodology you can't articulate from intention alone.
When to use: When you've identified a skill to build (from Prompt 1 or your own judgment) and you have 10-20 examples of your best output in that domain. This is the two-hour build session.
What you'll get: A complete, copy-paste-ready SKILL.md file with proper YAML frontmatter, a routing-optimized description field, methodology instructions, specified output format, edge case handling, and at least one example — built to the March standard, not the October standard.
What the AI will ask you: What skill you're building, your best work examples in that domain, and follow-up questions about the decisions embedded in those examples.
Prompt 3: Agent-Readiness Audit
Job: Takes an existing skill (or the output of Prompt 2) and stress-tests it against the four agent-caller criteria from the article, then produces a hardened version.
When to use: When you have a working skill that was built for human-directed use and you need to upgrade it for agent pipelines — or when you want to verify that a newly built skill meets the March standard before deploying it.
What you'll get: A diagnostic scorecard against the four agent-readiness criteria, specific failure scenarios the current skill would produce, and a redesigned SKILL.md that closes every gap.
What the AI will ask you: Your existing SKILL.md content and how the skill will be called (human only, agent pipeline, or both).
Prompt 4: Team Skill Deployment Planner
Job: Helps team leads and organizational leaders identify their Tier 1 (standards), Tier 2 (methodology), and Tier 3 (personal) skill priorities and build a deployment plan that turns individual expertise into institutional infrastructure.
When to use: When you manage a team or organization and want to move beyond individual skill use to team-wide deployment. This is the "expertise walks out the door when people leave" problem.
What you'll get: A tiered skill library plan with specific skills identified for each tier, ownership assignments, a build sequence, and a rollout plan — including who should build each methodology skill and how to extract their expertise.
What the AI will ask you: Your organization type, team size, what high-value work your team does, where quality varies, and what new hires take longest to learn.