How do you test whether agent skills can coexist in one catalog?

Installation changes the decision environment

An additional skill is more than another folder on disk. Its metadata may enter the host’s discovery context, and its instructions may become available during tasks that previously used another route. The intervention being tested is the changed catalog, not merely the candidate’s ability to complete its own demonstration.

The Claude API guide documents multi-skill availability and composition. A platform’s ability to combine skills is an interface feature; compatibility of a particular collection remains a local question.

Protect established tasks from a new arrival

Select neighbors through shared user intents, file types, tools, output formats, and handoff paths. Include a task that should use the candidate alone, one that should keep using each neighbor, one that legitimately composes capabilities, and one that should use none of them.

Record the complete metadata snapshot the host receives. A test with only two simplified descriptions may miss a third skill that dominates real selection. If the platform changes ordering or retrieves only part of a large catalog, capture that behavior as part of the test conditions.

A spreadsheet skill starts rewriting deck requests

Suppose a hypothetical analytics team installs reconciling-sales-tables alongside drafting-board-decks. The candidate correctly reconciles 24 sales workbooks when tested alone. Its broad instruction to “turn business data into a leadership-ready report” then causes it to load during requests that only ask the deck skill to summarize already approved numbers.

In a constructed comparison, the baseline completes 18 of 20 protected deck tasks. With the candidate installed, 6 of those 20 requests start unnecessary reconciliation and 4 no longer produce the requested deck. These counts illustrate what a report could reveal; they are not observed results from a real system.

Run the catalog intervention matrix

Keep preparation, host profile, and grading conditions comparable across the two catalog states. For stochastic runs, repeat cases and retain trial-level results rather than treating one replay as decisive.

Task slice	Baseline catalog	Catalog plus candidate	Protected observation
Candidate’s core task	Existing fallback	Candidate available	Intended improvement and accepted output
Neighbor’s established task	Neighbor available	Both available	No displacement or artifact regression
Legitimate handoff	Current composition	Candidate joins route	Correct order and preserved input meaning
Subject-only question	Ordinary response	Candidate visible	Unnecessary activation remains bounded
Missing prerequisite	Existing safe stop	Candidate visible	No invented input or forced completion
Prior collision case	Recorded baseline	Updated catalog	Known defect does not recur

The matrix is a proposed method for testing a specific collection. It does not assume that every cell should use the same grader or acceptance threshold.

Observe selection, procedure, and result

Save selected skill identities, file reads or equivalent activation evidence, tool calls, and the final artifact. A correct answer reached through a wrong procedure may incur unnecessary access, cost, or side effects. Conversely, loading an extra harmless reference is not automatically a release-blocking failure.

Define severity before reviewing the candidate results. Anthropic’s evaluation guidance separates regression protection from new capability testing. The same distinction lets a team reject a candidate whose own gains come at the expense of protected neighboring work.

Diagnose interference with a controlled removal

When a regression appears, rerun the failing slice after removing only the candidate or replacing only its metadata. If the effect disappears, the result supports a catalog-interference hypothesis. If it persists, inspect fixture drift, model variation, or an unrelated dependency change.

Do not claim proof from a single disappearance. Repeated, controlled comparisons narrow plausible explanations. Keep the full failing request and permitted context under appropriate data controls so the defect can become a regression case.

Publish compatibility as a bounded report

Report task slices, denominators, candidate and baseline identities, observed serious failures, and untested neighbors. The release decision should state which installed collection was supported; “passed the skill tests” is too broad.

Fix a discovery defect using the trigger boundary worksheet. For deeper grading decisions, see outcome versus trajectory evaluation. Skills in Production connects this narrow coexistence method with the wider release lifecycle. All counts and scenarios here are explicitly constructed web examples.