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gigayaya

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• 10 skills
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• 6 hours ago last updated

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   Daa Core · gigayaya bundle

   Use when discussing DAA architecture, automation testing patterns, or when other DAA skills require foundational context about the three-layer Declarative Action Architecture
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   Daa Review · gigayaya bundle

   Use when reviewing existing test code for DAA compliance, identifying anti-patterns, or suggesting improvements to automation test architecture
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   Daa Install · gigayaya bundle

   Use when installing DAA rules into a project, setting up DAA conventions, or bootstrapping a project to follow Declarative Action Architecture for E2E test automation
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   Daa Generate · gigayaya bundle

   Use when writing or generating E2E test code, creating test layers, action layers, or physical layers for automation testing projects
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   Daa Architect · gigayaya bundle

   Use when designing a new automation testing framework, scaffolding project structure, or planning test architecture following DAA principles
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   Ab Review · gigayaya bundle

   Use when the user explicitly asks for an "AB", adversarial, two-sided, or "red-team" code review of code changes they have made — e.g. "AB review my diff", "do an AB review of my changes", "red-team this code change", "have two reviewers argue about my code". Dispatches two opposing sub-agents — one building the evidence-based case that the change is mergeable, one building the case that it must not merge — runs them in parallel, and returns both reports to the main agent to judge. The skill itself never issues the verdict. Manual-trigger only — never auto-invoke; always ask the user to choose the review scope first. Skip for a routine single-perspective code review.
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   Scope Research · gigayaya bundle

   Use when the user explicitly asks to research the scope of a requirement or proposed change against the current codebase — e.g. "research the scope of this requirement", "what would adding X touch in this codebase", "do scope research on this spec", "/scope-research". Surveys the codebase to surface concrete facts about which files/areas a change would touch and the relevant facts about each touchpoint (callers, prior similar changes, current test state, conventions in use, data/interface contracts), with an honest LOC range per touchpoint. Presents facts neutrally — does not assign t-shirt sizes, time estimates, or risk ratings. When unsure about something material, asks the user clarifying questions before writing the report. Manual-trigger only — never auto-invoke. Skip for trivial single-file or one-line changes where research adds no value.
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   Docs Drift · gigayaya bundle

   Use when the user wants to verify this plugin's docs are still in sync with its code after a change — e.g. "check for docs drift", "did the README drift", "are the docs still accurate", "I updated the plugin, verify the docs", "/docs-drift". Runs a deterministic checker for mechanical drift (skill/command catalogs in README and codemap, per-skill index files, dead relative links, English-only rule, version bump) and then does a semantic pass the script cannot — comparing each doc's prose description against what the skill actually does and the dependencies it actually ships. Proposes concrete fixes and only edits docs after the user approves. Manual-trigger only — never auto-invoke; never edit files without approval.
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   Session Reflection · gigayaya bundle

   Use when the user explicitly asks to reflect on, review, or learn from the current Claude Code session — e.g. "review this session", "reflect on this session", "why did that take so many tries", "what went wrong this session", or asks to turn this session's mistakes into project rules. Reads the session transcript, finds where Claude's output was rejected or required correction, distills the root causes, and proposes concrete project rules — integrating them into the project's existing rule system (CLAUDE.md, AGENTS.md, …) or proposing a simple one if none exists. Manual-trigger only — never auto-invoke; never write rule files without explicit user approval.
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    Markdown To HTML Article · gigayaya bundle

    Use when about to present long or complex AI-generated markdown to the user — typically >150 lines, >5 H2 sections, or content like code reviews, implementation plans, specs, research reports. Also use when the user explicitly asks to convert a markdown source into a readable HTML article. Produces a single self-contained HTML article (hero image, article lede, magazine typography, pull quotes, quiet asides, syntax-highlighted code, inline concept tooltips, dark mode) saved to ./claude-articles/. Zero install — the renderer is stdlib-only Python. Skip for short, simple, or plain-text content where the conversion cost outweighs the reader's time savings.
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