Build once, run on any agent: A practical guide to agent skills

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Andrea Griffiths·@AndreaGriffiths11 November 12, 2026 | 6 minutes Share: How many times this week did you re-explain the same task to an agent?

How many times did you ask your colleague with deep institutional knowledge how to solve a specific problem?

That context shouldn’t be lost in a chat or locked in one person’s head. Agent skills let you capture repeatable instructions so you and your agents can reuse them. The skill you write for yourself tonight can later become part of a team’s pipeline. An agent skill is a versioned directory centered on a SKILL.md file that describes when and how to do a repeatable task. You can share those instructions across agents, but the environment still needs to provide the tools, credentials, and permissions to carry them out. A skill can describe how to query a database, for example, but it doesn’t grant access to that database. I covered this topic live with Shishir Tewari, Senior Manager, Data Engineering at Procore, during GitHub Universe 2026. Our session was titled “Build once, run on any agent: a practical guide to agent skills”. Define one job and when it applies A skill works best when it has a clear job, input, and output. For example, let’s say you’re building an agent that checks AI-generated changes against the original request. If the same agent assesses its own work, its explanation might influence the review. With Proof Agent, we use a separate verifier to examine the changes and provide evidence for its findings. The skill’s frontmatter names the task and describes its purpose: --- name: proof-agent description: Adversarial verification of AI-generated work. Spawns an independent verifier to check for false claims, broken code, and security issues. --- At the center is a simple rule: self-verification is not verification. The When to Verify section in Proof Agent’s SKILL.md says when to invoke the skill. The remaining instructions explain what the verifier reviews and what evidence to include in its findings. Coordinate multiple skills for a larger workflow When the judgment that makes a data pipeline correct lives in four different people’s heads, those four people are the roadmap. Each change to the pipeline depends on their availability and knowledge of the data. Data Forge is an example of how to capture parts of that judgment in skills that work together. In the support-desk pipeline example, we start with a request for metrics that count incidents created and resolved. Before building the pipeline, the forge-init skill discovers details such as source tables and naming conventions, then writes them to a resolved manifest. The other skills read that manifest for the project context they need. The forge-builder skill then coordinates the work from the issue through to a pull request. It uses forge-dbx to query the data platform and forge-lint to apply the team’s standards as it designs and builds the pipeline. A person reviews the proposed decisions along the way, and the resulting pull request contains the pipeline code, schema changes, and tests. Each skill has a defined responsibility, and they share project context instead of rediscovering it independently. When a step changes, you can change one skill and leave the rest alone. Skills fit bounded, repeatable work that benefits from reusable judgment, such as a checklist, release, migration, or metric pipeline. If a workflow needs a stable interface, shared state, an audit trail, long-running control, or permissions enforced outside the model, an application is a better fit. Keep project configuration out of the skill We don’t want to hardcode every project’s names, database, or conventions into the skills. In the Data Forge support-desk example, a manifest describes the project, metric store, and platform so the skills can use the right project details without baking them into their instructions: project: name: "support-desk" store_name: "support_metric_store" platform: engine: "duckdb" pipeline_framework: "spark_py