Conciseness

The body is efficient — it assumes competence, never explains concepts Claude already knows, and nearly every line is a command, schema, or decision rule. Minor trims are possible (the opening sentence and "When to Use" bullets partially restate the frontmatter description; "Robustness tip" is advice Claude could infer), which keeps it at "minor instances that could be trimmed" rather than the lean 5.

Actionability

Fully executable throughout: a copy-paste-ready scenario (the heredoc piped to `uv run python tests/uat/run_uat.py --agents gemini`), concrete baseline/target regression commands with `--branch master` and `--scenario-file`, and a concrete JSON output example showing exactly which fields to compare (`aggregate.total_tool_calls`, `total_duration_ms`). Specific examples cover the common cases.

Workflow Clarity

The six-step workflow has a clear sequence with explicit checkpoints and feedback loops: "If true, you're done", "Dig deeper on failure: Read `results_file`", and "If test fails, re-run with `--branch master` to compare". The regression section adds decision rules (primary vs secondary metrics, the >2x duration flag threshold) that make pass/fail evaluation explicit. This matches the top anchor with error-recovery loops.

Progressive Disclosure

Good structure with well-organized sections and a clearly signaled single-level pointer to full docs ("For complete CLI reference and output format, see `tests/uat/README.md`"). No bundle files exist, so nothing is buried or nested; however, the runtime JSON output structure (24 lines) and parts of the regression workflow could live in that external README to keep SKILL.md closer to an overview, leaving minor organization gaps.

Specificity

The description lists several concrete actions — "Run bot acceptance tests", "validate MCP tools work correctly from a real AI agent's perspective", "testing PRs, detecting regressions, or verifying tool changes end-to-end" — in proper third person voice. It stops short of a 5 because "Run bot acceptance tests" is somewhat self-referential and the action coverage beyond the three test types is minor rather than comprehensive.

Completeness

It explicitly answers both questions: what it does ("validate MCP tools work correctly from a real AI agent's perspective") and an explicit trigger clause ("Use when testing PRs, detecting regressions, or verifying tool changes end-to-end with Claude/Gemini CLIs") with concrete trigger phrases. This matches the top anchor exactly.

Trigger Term Quality

Natural user phrases are present: "testing PRs", "detecting regressions", "verifying tool changes end-to-end", plus named CLIs (Claude/Gemini). A few common variations users might say are missing (e.g. "acceptance testing", "integration test", "regression check"), which keeps it at the "good coverage, a few natural terms missing" anchor rather than 5.

Distinctiveness Conflict Risk

The niche is clear and fairly distinct — agent-perspective acceptance testing of MCP tools with real Claude/Gemini CLIs — with distinct triggers. There is minor overlap risk with generic testing/regression or PR-review skills, so it sits at "mostly distinct" rather than "clear niche with minimal conflict risk".