pyeye-mcp: Skill for Claude Code

pyeye Verify

A repeatable, scenario-independent procedure for two questions:

1. Did pyeye regress? — diff live probe output against a scenario's recorded baseline.
2. How well does pyeye serve an AI agent? — score the shape of the output against the agent-consumption rubric, not just whether it's correct.

The scenarios, pins, probe suites, and recorded baselines are the data, held in [[pyeye-scenarios]]. This skill is the procedure that runs against any of them.

Announce at start: "Using the pyeye-verify skill to verify <scenario> on <build>."

Why a skill harness, not pytest

This complements the pytest suite — it doesn't replace it. These scenarios hit real third-party repos at pinned commits (too heavy for CI; never vendored), and the agent-consumption rubric is qualitative (altitude, honesty, response shape) — awkward to assert in pytest, natural for an agent rubric. Run on demand; not a CI gate. Distinct from python-explore (user-facing tool mechanics) and the built-in verify (run-the-app).

Procedure

digraph pyeye_verify {
    rankdir=TB;
    "Pick scenario (pyeye-scenarios)" [shape=box];
    "Provision / confirm at pinned commit" [shape=box];
    "Run the probe suite" [shape=box];
    "Diff vs recorded baseline" [shape=box];
    "Divergence?" [shape=diamond];
    "Classify: fixed | regressed | env" [shape=box];
    "Score agent-consumption rubric" [shape=box];
    "Report both verdicts" [shape=box];

    "Pick scenario (pyeye-scenarios)" -> "Provision / confirm at pinned commit";
    "Provision / confirm at pinned commit" -> "Run the probe suite";
    "Run the probe suite" -> "Diff vs recorded baseline";
    "Diff vs recorded baseline" -> "Divergence?";
    "Divergence?" -> "Classify: fixed | regressed | env" [label="yes"];
    "Divergence?" -> "Score agent-consumption rubric" [label="no"];
    "Classify: fixed | regressed | env" -> "Score agent-consumption rubric";
    "Score agent-consumption rubric" -> "Report both verdicts";
}