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Found 1,232 Skills
Run the evo optimization loop with parallel subagents until interrupted.
After solving a non-trivial problem, detect generalizable learnings and propose skill updates so future interactions benefit automatically. Always active — applies to every interaction.
Governs the evolution of public APIs. Detects breaking changes, manages deprecation cycles, and generates migration guides for clients.
Horizontal Gene Transfer protocol for skills. Synchronizes best practices and architectural patterns across the skill library.
Smoke test for alicloud-platform-devops. Validate script compilation and one bounded DevOps metadata query path.
Convert an existing codebase in the current working directory into a ShinkaEvolve task directory by snapshotting the relevant code, adding evolve blocks, and generating `evaluate.py` plus Shinka runner/config files. Use when the user wants to optimize existing code with Shinka instead of creating a brand-new task from a natural-language description.
Explore and manage Azure DevOps work items: Epics, Features, User Stories, and Tasks. Provides project overview (tree), get epic, get feature, create work item, and edit work item modes. Use when the user wants to list epics, view features, browse work items, create tasks, edit user stories, or explore the backlog in Azure DevOps.
Compares Trailmark code graphs at two source code snapshots (git commits, tags, or directories) to surface security-relevant structural changes. Detects new attack paths, complexity shifts, blast radius growth, taint propagation changes, and privilege boundary modifications that text diffs miss. Use when comparing code between commits or tags, analyzing structural evolution, detecting attack surface growth, reviewing what changed between audit snapshots, or finding security-relevant changes that text diffs miss.
Complete Azure DevOps automation - boards, repos, pipelines, artifacts
Update specifications with discoveries made during development. Use when implementation reveals new requirements, constraints, or design changes.
Explicit child skill for extracting reusable improvements from real Ghidra work, resolving overlap, and promoting tracked assets when one task provides complete evidence.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.