feat: 完善两阶段 Agent 自进化 Pipeline、Skills 与 Benchmark (#129)

2026-07-30 15:59:58 +08:00
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+# Isolated Agent tuning pipeline
+
+The Web App now includes a runnable example that coordinates Agent creation, Benchmark construction, and score-driven Agent optimization without sharing private evaluation context between phases.
+
+## Web App
+
+The new draft-screen example launches each phase in an independent Penguin CLI Session derived from the active Project environment. Its Benchmark uses hidden context-to-action mappings so the optimizer can demonstrate measurable improvement from score-linked feedback.
+
+Benchmark construction now uses provisional Pilot evaluations to adjust one difficulty dimension at a time before freezing and recording the Formal Baseline.
+
+## Skills
+
+The Agent creation, Benchmark design, evaluation, and optimization Skills now define clearer ownership and access boundaries. Benchmark builders and optimizers delegate each Case run through an explicit Evaluator request protocol, while private Rubrics and Gold answers remain confined to the evaluation worker.
+
+Benchmark design now separates mutable Pilot calibration from the frozen Formal Baseline and prevents Rubric-only score reductions around observed answers.
@@ -25,3 +25,5 @@ Changes since v0.1.4. The version number is assigned at release, when this folde
 - [2026-07-27] Windows: the `win32-x64` package bundles MinGit under `git/`, so `exec_command` has a real bash even on a machine with no Git for Windows — the shell stops depending on what happens to be installed. A user's own Git for Windows still wins (its MSYS userland is the fuller one); the bundle is the floor, and PowerShell is now reached only by npm installs. GPLv2 obligations are recorded in a new root `THIRD-PARTY-NOTICES.md`. ([details](2026-07-27-windows-bundled-shell.md))

 - [2026-07-27] Sites: the 0.1.4 release post in both languages, with a capture script for its screenshots. ([details](2026-07-27-sites-and-blog.md))
+
+- [2026-07-25] Agent tuning: the Web App gains an end-to-end example for creating, benchmarking, and optimizing an Agent through isolated CLI Sessions, while the built-in tuning Skills tighten phase ownership, private evaluation boundaries, and Evaluator dispatch contracts. ([details](2026-07-25-agent-tuning-pipeline.md))