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OpenAI and Anthropic document different skill implementations, so product-specific packaging and versioning details below are labeled. The lifecycle practices are useful across platforms, but implementation details should not be assumed to transfer unchanged.
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Define the task before writing the skill
A skill is most useful when an agent repeatedly performs a workflow that benefits from consistent instructions. Start by describing the job in terms a maintainer can test, rather than beginning with a collection of general advice. OpenAI Academy recommends identifying the recurring workflow, its inputs and outputs, and the guardrails needed for consistent results: OpenAI Academy’s guide to using skills.
- Inputs: What information, files, or user decisions does the task require?
- Output: What should the agent produce, and in what format?
- Sequence: What steps should it follow, and which decisions depend on earlier steps?
- Guardrails: What must it not assume, disclose, or do without authorization?
- Success checks: What observable results would show that the task was completed correctly?
Be specific enough to distinguish this workflow from similar requests. If you cannot describe a representative input and a checkable outcome, the task may not yet be defined well enough to turn into a reusable skill.
Organize instructions so the agent can find and use them
Keep the main workflow in one primary SKILL.md and move substantial details into supporting files when they are needed only for particular cases. OpenAI’s documented bundle layout uses directories such as references for background material, scripts for repeatable actions, and assets for reusable templates: OpenAI API: Skills. Anthropic recommends progressive disclosure: metadata helps a model judge relevance, the main instructions are used when appropriate, and linked resources can be consulted as needed: Anthropic: Skill authoring best practices and Anthropic: Equipping agents for the real world with Agent Skills.
This structure keeps the core instructions focused without hiding important requirements. Each supporting file should have an explicit purpose in the main instructions, including when the agent should consult it. For a deterministic operation, a script may be more consistent than prose; document the expected inputs and how failures should be handled. Do not describe a script as tested unless it has actually been run.
Make the name and description precise
Choose a specific, consistent name and write a description that states both the task and the conditions that should trigger it. Anthropic says the name and description metadata are available before the full skill body and help with selection; OpenAI’s eval guidance also identifies name and description as important invocation signals. A description that is too broad can cause unwanted activation, while one that is vague or too narrow can leave relevant requests unmatched.
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Keep critical requirements in the core
Put the ordered workflow, must-follow constraints, and directions for consulting supporting files in SKILL.md. Do not make the agent infer that a reference file exists or when it matters. Keep detailed examples and large reference material outside the core when they are not needed for every run, but ensure the core gives the agent a clear route to them.
Test whether the skill activates and whether it works
Selection and execution are separate questions. A skill can perform well when manually invoked but fail to activate for ordinary phrasing; it can also activate reliably yet produce poor results. OpenAI’s published plugin guidance recommends testing representative requests and reviewing activation separately from output quality: OpenAI: Build skills – Plugins.
Use a small set of cases tailored to the task rather than treating the following categories as a universal benchmark:
| Case | What to test | What to inspect |
|---|---|---|
| Direct trigger | A user names the task plainly. | Did the skill activate? |
| Indirect trigger | A user describes the same goal in different words. | Did discovery generalize appropriately? |
| Missing information | A request omits a required input. | Did the agent ask a useful follow-up or handle the gap as specified? |
| Non-trigger | A similar request belongs to another workflow. | Did this skill stay inactive? |
| Boundary case | A request invites an unsupported claim or action. | Did the agent respect the skill’s limits? |
| Output check | The agent performs the intended task. | Does the result meet the required content, format, and quality criteria? |
Include both positive and negative examples when tuning the description. If a skill triggers too often, inspect which related but out-of-scope requests are activating it. If it fails to trigger, compare the actual user wording with the task and conditions described in its metadata before adding more detail to the instruction body.
Measure regressions with repeatable evaluations
Keep a compact evaluation record for representative cases: the prompt, the run trace and artifacts, the checks applied, and the resulting score. OpenAI describes this pattern in Testing Agent Skills Systematically with Evals and recommends focused checks across outcome, process, style, and efficiency.
- Outcome: Did the result satisfy the task’s required content and format?
- Process: Did the agent follow required steps and handle missing information appropriately?
- Style: Did the output meet relevant clarity or tone requirements?
- Efficiency: Did it avoid unnecessary steps or material?
Use hard pass/fail checks for mechanically verifiable requirements such as required files, output structure, or commands. Use a short, focused rubric for qualities that need judgment. A check should expose a meaningful failure, not encode every preference in one sprawling scorecard. Compare a changed skill with its prior version on the same representative cases so a better result in one area does not conceal a regression elsewhere.
Anthropic advises testing across all models intended for use because a skill may behave differently depending on how much guidance a model needs. Record the model and environment for each run, review failures, and adjust the instructions before expanding deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Update skills through reviewed versions
Treat each meaningful edit as a candidate version. Rerun the representative activation and output cases, compare them with the previous version, and select a default only after review. This release loop combines OpenAI’s version-management support with its evaluation guidance; it is a practical maintenance approach, not a claim that a platform automatically certifies a skill’s quality.
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- Validate the bundle. Check the metadata, file layout, links, and supporting files against the target platform’s current requirements.
- Run the evaluation set. Test positive and negative activation cases, missing inputs, boundary cases, and expected outputs.
- Review differences. Compare results with the previous version and inspect failures rather than relying only on an aggregate score.
- Promote deliberately. Set the reviewed version as the default only when it meets the checks that matter for its intended use.
For OpenAI’s documented API packaging, the Skills page lists a maximum ZIP size of 50 MB, up to 500 files per skill version, and a maximum uncompressed size of 25 MB. These are OpenAI product-specific limits, not general skill-format rules; confirm the current requirements on OpenAI API: Skills before publishing. The same page describes uploading a new version, choosing a default version, and validating frontmatter against the Agent Skills specification, including a one-SKILL.md-per-bundle constraint.
Inspect safety across the whole bundle
Review more than the Markdown instructions. Check linked files, scripts, declared tools, and any network behavior before using a skill. OpenAI warns that network-enabled skills can create prompt-injection-driven data-exfiltration risks. Instructions in a Markdown file should not be treated as trusted solely because of their format.
- Confirm that instructions and linked resources stay within the skill’s intended purpose.
- Inspect scripts for what they read, write, execute, or transmit, and verify their failure handling.
- Review tool permissions and network access against the task’s actual needs.
- Test boundary prompts that attempt to elicit unsupported claims or actions.
- Recheck the bundle after edits, including changes to supporting files and scripts.
These checks are especially important when a skill can access sensitive information or external services. A good evaluation score does not replace reviewing what the bundle is authorized to do.
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