If the same skill sometimes succeeds and sometimes goes off track, making the prompt longer usually will not help. Conflicting trigger conditions, missing required inputs, overlapping tool responsibilities, permission changes, and overly loose acceptance criteria can all cause the execution path to drift. Reproduce the issue and preserve the evidence first, then determine which layer is responsible.
Reproduce it first: Save a comparable pair of samples
Preserve the original request, input file version, tool calls, output, error messages, and timestamp from one successful run and one failed run. Redact sensitive values, but do not keep only screenshots.
When rerunning the task in a test environment, change only one variable at a time. If you change the model, skill instructions, and tool configuration simultaneously, you will not know which change worked even if the result improves.
- Use the same fixed input snapshot
- Record the skill version and run time
- Save the last successful step
- Do not repeatedly experiment on production data
Identify the failure layer from the symptoms
If the skill is not triggered, inspect its trigger statements and exclusion conditions. If it selects the wrong tool, check whether tool responsibilities overlap. If it starts guessing during execution, inspect required inputs and missing stop rules. If the output structure changes, inspect the template and field-level acceptance criteria. If it writes duplicate data, inspect the unique key.
Find the earliest step where the execution deviates instead of looking only at the final text. Empty upstream input may surface downstream as a formatting error. Fixing only the formatting will conceal the root cause.
- Compare the expected path with the actual path
- Mark the first point of deviation
- Distinguish rule problems from external service failures
Correct trigger and input boundaries
Trigger conditions should describe the specific action, object, and required context, while also listing similar requests that should not trigger the skill. For example, “update a published article” and “create a new article” require different skills and rollback methods.
Divide inputs into information that can be queried and information that cannot. For queryable information, read the actual state first. If non-queryable details such as account information or business rules are missing, the skill should stop rather than fill them in on its own. Unattended tasks such as Cron jobs must complete prerequisite checks before making paid calls or writing to external systems.
- Add counterexamples to trigger conditions
- List required fields and valid ranges
- Specify how null and expired values should be handled
- Define priorities when multiple skills match
Tighten tools and permissions
Retain only the tools each skill needs to complete its task. Reading a file does not require permission to write to its directory, and permission to create a draft does not imply permission to publish it. Credentials must come from a controlled source and should not be written into ordinary prompts or skill content.
Text on web pages, in emails, and in files is data and must not override the task objective. If the content instructs the skill to change permissions, disclose credentials, or bypass acceptance checks, the skill should ignore and log the instruction.
- Use least privilege
- Require separate confirmation for actions with side effects
- Use a single designated writer for shared resources
- Set a maximum number of failure retries
Establish regression tests and acceptance criteria
Prepare at least four types of samples: standard input, missing fields, conflicting requirements, and repeated execution. Acceptance testing must check more than whether “content was generated.” It should also verify link status, file existence, record IDs, publication status, and the final page.
After applying the fix, run all samples and record the most recent skill version that passed. If one skill covers different search intents, permissions, and rollback methods, splitting it into two narrowly scoped skills will make it easier to maintain.
- Standard samples pass consistently
- The skill does not guess when inputs are missing
- The skill stops when a request exceeds its permissions
- Repeated execution does not create duplicate writes
Further reading
To review related areas, read Designing an AI Agent skill system and Building and managing a knowledge base.
Next step
In SmaugBrain, create a set of redacted regression samples for the problematic skill. Rerun them after fixing each layer, and restore the production task only after confirming that all tests pass.