agent-powerups

Use when starting work in a repository with Agent Powerups installed, when a task may match a reusable local skill, command, workflow, hook recipe, AGENTS.md template, or MCP feature.

Use when validating rendered web pages, local dev servers, browser automation, screenshots, forms, auth sessions, or UI evidence with strict browser safety boundaries.

Use when designing or reviewing filesystem MCP access, path boundaries, allowed roots, method allowlists, and safe local file operations.

Use when implementing any feature or bugfix, before writing implementation code

Use when completing tasks, implementing major features, or before merging to verify work meets requirements.

Use when receiving code review feedback, before implementing suggestions — requires technical verification and reasoned pushback, not performative agreement or blind implementation.

Design high-quality MCP servers around workflows, narrow schemas, context-aware outputs, and actionable errors. Use when building or reviewing MCP tools for real agent tasks.

Use when mining PR review comments and text diffs for reusable writing, documentation, tone, and editorial improvement patterns.

Use when you have a spec or requirements for a multi-step task, before touching code.

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "no fluff", "be concise", "use less tokens", or similar.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Use when conversation context is bloated, constraints are being forgotten, or a compact handoff is needed before continuing work.

Use when diagnosing agent session history, interrupted tool loops, missing tool results, timing bottlenecks, or subagent trace correlation.

Use when optimizing agent runtime loops, card packs, MCP session lifecycle, tool-call count, or multi-agent orchestration patterns.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

Use when designing, running, debugging, or hardening deterministic eval suites for agent skills, prompts, tool workflows, or MCP-backed cases.

Use when adding or reviewing data quality tests for dbt models in warehouse-backed analytics projects. Covers dbt generic tests, singular tests (assert_*.sql), accepted-values macros, dbt_utils patterns, cross-system consistency tests, and warehouse-oriented validation. Use when writing data tests, creating assert_*.sql files, testing business logic, or validating referential integrity.

Use when creating or reviewing red-team eval plugins, attack templates, grader rubrics, safety fixtures, or model-risk test metadata.

Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

any input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report. Use when the user wants to build, inspect, or query a cross-file knowledge graph from code, docs, papers, images, or videos with upstream graphify.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Create or refactor high-quality skills with lean frontmatter, progressive disclosure, and optional bundled helpers. Use when authoring reusable agent workflows.

Use when a graph already exists and the user needs retrieval, tracing, explanation, or gap detection from graph memory before reopening the full corpus.

Use when deciding the lowest-cost context path for a mixed corpus, especially when choosing among direct reading, helper conversion, graph build, graph update, or graph query.

Use when a user needs to build or refresh persistent graph memory from a mixed corpus and the right path may include graphify, incremental update, or helper conversion before ingestion.

any input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report. Use when the user wants to build, inspect, or query a cross-file knowledge graph from code, docs, papers, images, or videos with upstream graphify.

Address actionable GitHub pull request review feedback. Use when the user wants to inspect unresolved review threads, requested changes, or inline review comments on a PR, then implement selected fixes.

Use when code needs restructuring without changing observable behavior.

Use when a browser-facing UI needs visual verification, a layout bug or visual regression is reported, accessibility of an interactive component needs checking, or a browser workflow must be exercised end-to-end.

Use when apx commands fail or assets are missing from listings, MCP servers fail to start, builds hit command-not-found or module-not-found errors, generated assets look stale, or setup works on one machine but not another.

Use when preparing release notes or a changelog entry from commits, PRs, or diffs, summarizing a sprint or version range, or scanning a range for breaking changes before publishing.

Use when a build, test, type check, or lint is failing — locally or in CI — and the fix must be the smallest safe diff with no opportunistic refactors or unrelated changes.

Use when a wide refactor or migration is too large to ship as one change — API renames across many files, framework or config migrations, or mechanical codemod-backed refactors that need reviewable batches.

Use when a tricky bug, non-obvious workaround, hidden gotcha, or undocumented behavior took real debugging effort to discover and should be captured as a reusable learned skill.

Use when modifying agent config files, committing configuration changes, onboarding a repository with existing agent configs, or running a periodic config hygiene check.

Use when an agent has modified logic, API routes, or data-transformation code, a bug was just fixed and must not be reintroduced, or behavior must stay identical across execution paths such as sandbox versus production.

Use when about to write a new doc, README section, or ADR, updating existing documentation with unclear scope, or spawning a documentation subagent that needs file context.

Use when starting work in an unfamiliar area of a codebase, spawning a subagent that needs targeted file context, a first search pass missed the relevant file, or the file scope of a task is unclear.