How to Write High‑Quality Skills for Your Agent System

1. Determine if the task is worth turning into a Skill

Evaluate three signals: (1) Does the task involve expert intuition such as boundary judgment, risk identification, or priority trade‑offs? If so, it is a good candidate. (2) Is the task sufficiently complex that a single prompt or a few steps cannot solve it? Simple tasks should not become Skills because they add maintenance overhead. (3) Does the task recur frequently (e.g., weekly analysis, review, publishing, or generation)? Reusability is the main value of a Skill. When all three signals are present, invest in building a Skill.

2. Extract what the Skill should contain

After deciding to create a Skill, follow three sub‑steps:

Extract a decision tree that tells the Agent when to follow path A, when to switch to path B, and when to stop, downgrade, or request more information.

Identify anti‑patterns and things to avoid, such as writing unverified source content as fact, executing high‑risk changes without a plan, or fabricating missing information.

Provide templates or examples: use a template when output structure is strict, and use examples when style or organization matters.

3. Write clear instructions

Three principles guide the instruction writing:

Concise : Skills share the same token window with system prompts, conversation history, and other Skills, so each sentence must justify its token cost. Only task‑specific judgments, constraints, entry points, and resource navigation belong in the Skill.

Freedom matching : For high‑risk tasks (bulk file changes, database migrations, deployments) impose low‑freedom constraints and call fixed scripts. For analysis‑heavy tasks (code review, content planning, evaluation) keep higher freedom while defining process boundaries and quality standards.

Progressive disclosure : Do not dump all details into SKILL.md. Keep core flow, trigger conditions, and resource links in the main file; move detailed rules to references/, deterministic actions to scripts/, and keep the main file lightweight.

4. Equip tools and resources

For sub‑tasks with standard procedures, replace model guessing with an explicit workflow. A typical workflow includes: extract input → run checks → organize issues → fix and re‑verify → generate final result.

Close the verification loop: instead of “execute and finish”, write a feedback loop that runs a validator, reads results, fixes problems, re‑runs the validator, and proceeds only after passing.

For high‑risk operations, add a “plan‑first‑then‑execute” pre‑check.

When scripts are used, follow four guidelines: output structured data (prefer JSON), include repair hints in error messages, degrade gracefully instead of crashing, and aim for idempotency to avoid chaotic results on repeated calls.

5. Validate with real tasks and iterate

Adopt an iterative cycle:

Establish a baseline by running the Agent on a real task without any Skill, record typical errors and missing information as evaluation cases.

Draft the first Skill version, separating fixed procedures, judgment criteria, common errors, and script responsibilities.

Test the draft in a fresh session using the recorded cases; improved performance indicates the Skill is effective.

Continuously iterate until results stabilize, optionally building a benchmark suite to track improvements across scenarios.

The author, an experienced backend‑to‑Agent developer, shares these practices to help engineers design reliable, maintainable Skills for large‑scale Agent systems.