🤖 Update Mock Benchmark Results

[thomhurst]

Automated Mock Benchmark Update

This PR updates the mock benchmark documentation with the latest results from the Mock Benchmarks workflow.

Benchmarks Produced

Individual benchmark artifacts are available for download:

• mock-benchmark-MockCreation
• mock-benchmark-Setup
• mock-benchmark-Invocation
• mock-benchmark-Verification
• mock-benchmark-Callback
• mock-benchmark-CombinedWorkflow
• mock-benchmark-summary (aggregated overview)

Libraries Compared

Libraries are discovered dynamically from the benchmark output. See the generated summary.json for the full list.

Changes

• Updated mock benchmark data in docs/static/benchmarks/mocks/latest.json
• Regenerated mock benchmark documentation in docs/docs/benchmarks/mocks/
• Updated mock benchmark summary in docs/static/benchmarks/mocks/summary.json

Workflow Run

• Run ID: 29628226371
• Triggered: schedule

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🤖 This PR was automatically created and will be merged automatically once CI checks pass.

[claude]

Code Review

Note: This PR is already merged (auto-merged bot PR). Reviewing for the record.

Overview

Fully automated, mechanical update of mock benchmark data/docs from the scheduled Mock Benchmarks workflow (run ID 29628226371). All 15 changed files are either:

• docs/static/benchmarks/mocks/*.json — raw benchmark result data (timestamps, Mean/Error/StdDev/Allocated per library)
• docs/docs/benchmarks/mocks/*.md — generated docs (tables + Mermaid xychart-beta charts) derived from that data

No source/library code, tests, or public API surface is touched.

Analysis

• Diff is internally consistent: each .md's table values, chart y-axis bounds, and bar [...] series match the corresponding .json numbers (spot-checked Callback.md/Callback.json, index.md, summary.json).
• Timestamps updated uniformly (2026-07-17 → 2026-07-18) across all files — no stale/mismatched dates.
• New numbers show TUnit.Mocks and the runtime-proxy libraries all trending faster (e.g. Moq Callback: 187,749 ns → 133,770 ns), consistent with normal run-to-run benchmark noise rather than a regression signal.
• Labeled documentation, ignore-for-release, automated, benchmarks — correctly excluded from release notes.

Risks / Suggestions

• None applicable to the diff itself — it's generated output, not hand-written code, so there's nothing here to refactor architecturally.
• General (non-blocking) suggestion for the generator pipeline: consider trimming benchmark JSON to fixed decimal precision to reduce diff noise/churn on every scheduled run (e.g. 13,770.5 ns vs 133,770.53 ns-style rounding drift), but this is a minor nice-to-have, not a defect in this PR.

Verdict: LGTM — matches the pattern of prior automated benchmark-update PRs, data is internally consistent, no code risk.