Prompt and Context Design for Claude Code Skills

The hardest part of building reliable Claude Code skills is not writing instructions — it is deciding what not to write. Every sentence you add to a skill competes for the model's attention and a slice of a finite context window. Add too little and the agent improvises; add too much and the signal drowns in noise. This post is about that tradeoff: the discipline of context design, what earns its place in a skill, what to deliberately leave out, and why the leaving-out is often the higher-leverage decision.

Context is a budget, not a bucket

Even with a very large context window, attention is finite. The model weighs everything in context, and irrelevant material does not just sit there harmlessly — it dilutes the importance of the instructions that matter and can actively pull the agent off course. Context design starts from treating the window as a budget you spend deliberately, not a bucket you fill because there is room. The question for every line is not "could this be useful?" but "is this worth the attention it will take?"

This reframes skill authoring. A short, sharp body of genuinely load-bearing instructions outperforms a long one padded with background, caveats, and restated common knowledge. When a skill underperforms, the instinct is to add more guidance, but more often the fix is to cut — to remove the three paragraphs of context that were burying the one sentence that actually steers the behavior you want.

What earns a place in context

The content that earns its place is the local, specific, surprising knowledge the model cannot supply itself: your conventions, your taxonomy, the edge cases that bit you last time, the exact output format you need. These are things no base model knows because they are particular to your domain. The diagram shows the decision you make for each candidate piece of context.

flowchart TD
  A["Candidate fact for the skill"] --> B{"Does the base model already know it?"}
  B -->|Yes| C["Leave it out"]
  B -->|No| D{"Needed for THIS task?"}
  D -->|No| E["Move to a referenced file"]
  D -->|Yes| F{"Needed every run?"}
  F -->|No| G["Load on demand via pointer"]
  F -->|Yes| H["Keep inline in the body"]
  H --> I["Tight, high-signal context"]

Run every candidate through that filter and the body almost writes itself. Domain-specific rules stay inline. Large references move to bundled files loaded on demand. Anything the model already knows gets cut. What remains is high-signal context the model can actually act on, which is exactly the state you want before the agent starts working.

What to deliberately leave out

Leave out general knowledge — the model knows what JSON is, how to write a polite email, what a unit test does. Leave out long preambles explaining why the skill exists; the model does not need motivation, it needs instructions. Leave out exhaustive option lists when one sensible default will do, and just state the default. And leave out stale context: instructions for a tool you removed or a format you abandoned actively mislead the agent and are worse than silence.

The subtlest thing to leave out is redundancy. Saying the same constraint three different ways does not make the model obey it three times harder; it spends three times the tokens and can imply the rule is negotiable. State each rule once, clearly, in the place it applies. A skill that says each important thing exactly once reads cleanly to both the model and the human reviewing it later.

Ordering and emphasis

Where something sits in context affects how strongly it lands. Put the most important constraints and the core procedure early, where they anchor everything that follows. Group related instructions together so the model does not have to reassemble a scattered procedure. When a rule is genuinely critical — "never send the email without confirmation" — state it plainly and near the point of action, not buried in a wall of secondary detail where it competes with everything else.

Negative instructions deserve special care. "Do not do X" works best when it is specific and paired with the positive alternative: "do not guess the customer's account ID; ask for it." Vague prohibitions invite the model to wonder what counts, while a specific prohibition plus the right action to take instead gives it a clear path. Use them sparingly and precisely, because a body that is mostly don'ts reads as anxious and gives the model little to actually do.

Designing for the multi-turn reality

Skills do not run in a vacuum; they execute inside a conversation that already has history, tool outputs, and earlier turns competing for the same window. Good context design accounts for this by keeping the skill's own footprint lean so it coexists with everything else the agent is juggling. A skill that assumes it owns the whole window will misbehave once it is loaded alongside three tool results and a long back-and-forth with the user.

This is also why on-demand loading matters so much. By pointing to reference files instead of inlining them, a skill keeps its always-present footprint small and pulls detail in only for the turn that needs it, then lets it fall away. The agent's working context stays focused on the current step rather than carrying the full weight of every skill at once — which is the whole reason progressive disclosure exists, applied at the level of your own authoring.

Frequently asked questions

What should I always include in a skill's context?

Include the local, specific knowledge the model cannot supply: your conventions, taxonomy, edge cases, and exact output format. These are the high-signal facts that steer behavior, and they belong inline in the body where the agent reads them every run.

What should I leave out of a skill?

Leave out general knowledge the model already has, long motivational preambles, redundant restatements of the same rule, and any stale instructions for removed tools or formats. Cutting these sharpens the signal of the instructions that matter.

Does a big context window mean I can include more?

No. Attention is finite even in a large window, and irrelevant material dilutes important instructions and can pull the agent off course. Treat context as a budget you spend deliberately, not a bucket you fill because space exists.

How should I write negative instructions?

Make them specific and pair each with the positive alternative — for example, do not guess an account ID, ask for it. Place critical prohibitions near the point of action and use them sparingly so the body still tells the model what to do.

Bringing agentic AI to your phone lines

CallSphere applies this same context discipline to voice and chat agents — lean, high-signal prompts that keep assistants on track while they use tools mid-call and book work 24/7. Hear it for yourself at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.