Self-referential loop until task completion with configurable verification reviewer — from yeaight7/agent-powerups
Use when reviewing code (or your own plan) to allocate attention based on the danger of the change.
Audit agent configuration files for security vulnerabilities and misconfigurations. Covers settings.json, .mcp.json, .codex/config.toml, AGENTS.md, hooks, plugin manifests, and…
Use at the beginning of a new task. Ensures you fully understand the requirements, boundaries, and acceptance criteria before writing code.
Vendor-neutral routing guide for choosing the right model tier by task type. Mechanical work uses a smaller/faster model; implementation uses a standard model; architecture,…
Use when connecting to a managed codebase-context MCP/session service, checking stale maps, or safely using MCP-provided repository context.
Fix broken builds, type errors, or test failures with the smallest safe diff — no opportunistic refactors, no cleanup, no unrelated changes.
Use when creating or reviewing red-team eval plugins, attack templates, grader rubrics, safety fixtures, or model-risk test metadata.
Use to isolate a bug from a large application into a standalone, runnable script or single test case.
Analyze BigQuery usage, identify cost hotspots, repeated failures, and practical optimization opportunities.
Use when evaluating prompts, LLM outputs, red-team suites, or model behavior with local eval configs and safe provider/cost controls.
Parallel execution engine for high-throughput task completion — from yeaight7/agent-powerups
Use when creating or modifying dimensional dbt models in warehouse-backed analytics projects. Covers a four-layer warehouse architecture (sources/staging/core/marts), naming…
Deterministic 3-cycle loop for gathering codebase context before acting. Broad search → exact source and tests → target-specific docs and setup.
Use when validating rendered web pages, local dev servers, browser automation, screenshots, forms, auth sessions, or UI evidence with strict browser safety boundaries.
Audit documentation for broken file paths, outdated commands, and renamed variables.
Inspect and test web UIs using screenshots, browser automation, accessibility snapshots, and console/network signals with structured visual evidence collection.
Diagnose NaN losses, out-of-memory errors, and shape mismatches in deep learning or ML pipelines.
Use when confronted with an unknown failure in CI or production to rapidly categorize the issue before deep debugging.
Use to identify and safely delete unused functions, classes, exports, and files.
Use to collapse over-engineered abstractions, remove unnecessary layers, or consolidate redundant logic.
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…
Use when an MCP server is about to be enabled, added to agent config, or upgraded -- a staged server config is a candidate for activation, a third-party server is proposed from a…
Use to restructure code while guaranteeing that all existing tests continue to pass.
Coordinate multi-system deployment steps as a review-first runbook with explicit approval before any external write or promotion action.
Inspect GitHub PR checks, fetch actionable failure logs, summarize the breakage, and propose a local fix plan before changing code.
Maintain short, focused Markdown files per subsystem to provide agents with isolated context.
Use when designing, running, debugging, or hardening deterministic eval suites for agent skills, prompts, tool workflows, or MCP-backed cases.
Use when mining PR review comments and text diffs for reusable writing, documentation, tone, and editorial improvement patterns.
Run large codebase migrations in reviewable local batches with codemods, checkpoints, and verification. Use when a wide refactor is too risky to ship as one monolithic change.
Run a review and CI loop around a pull request with explicit approval gates for code changes, remote writes, and follow-up actions.
Coordinate multiple subagents through dependency-aware waves, stage handoffs, monitoring, and verification loops. Use when the work is too large or too mixed for a single agent.
Use after fixing a bug to generate a blameless post-mortem summary for human review.
Use when completing a task or running out of context limit. Ensures the next session or human engineer has exactly what they need to resume work instantly.
Use when the task is to understand an unfamiliar codebase, locate key entry points, or summarize architecture before editing.
Use when optimizing agent runtime loops, card packs, MCP session lifecycle, tool-call count, or multi-agent orchestration patterns.
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…
Use to audit and remove unused or redundant third-party dependencies from package manifests.
Design agent tool sets with stable names, narrow schemas, deterministic output shapes, and explicit error paths. No catch-all tools unless unavoidable.
Full autonomous execution from idea to working code — from yeaight7/agent-powerups
Evaluate metric and semantic model changes for BI/reporting breakage and business meaning drift.
Use when modifying dbt metrics or semantic models to ensure mathematical correctness and backwards compatibility.
Use when a bug was recently introduced but you don't know which commit caused it.
Verify that all hyperparameters, metrics, and data references are properly logged.
Use when migrating APIs, libraries, or patterns across a large codebase. Ensures safe, step-by-step progress rather than risky mega-commits.
Use before tagging a release or deploying to production to ensure all quality gates have passed.
Use when a hook is about to be enabled or modified -- a hook recipe proposed for activation, a pre/post-tool or lifecycle hook added to agent settings, a git pre-commit hook added…
Ensure the project README provides immediate, exact commands for setup, testing, and deployment to help agents and humans bootstrap quickly.
Use when a CI pipeline fails to extract the actual error from thousands of lines of logs.
Check version bump, changelog, compare range, package metadata, validation commands, publish steps, and rollback plan before cutting a release.
Use when designing or reviewing filesystem MCP access, path boundaries, allowed roots, method allowlists, and safe local file operations.
Use continuously during long tasks. Teaches how to read less, output less, and keep the LLM context window lean and fast.
Use before submitting a PR or considering a task done to evaluate the 'blast radius' of your changes.
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 debugging complex runtime failures, distributed systems, or issues where a local debugger cannot be attached.
Check existing repo capability, external libraries, MCP options, and maintenance risk before writing custom code. Decide adopt/wrap/build with explicit criteria.
Use when writing technical documentation that needs to be readable by both humans and AI models, converting existing docs to HADS format, validating a HADS document, or o — from…
Review SQL for business logic correctness, semantic drift, aggregation risk, and silent definition changes.
Create or refactor high-quality skills with lean frontmatter, progressive disclosure, and optional bundled helpers. Use when authoring reusable agent workflows.
Use when diagnosing agent session history, interrupted tool loops, missing tool results, timing bottlenecks, or subagent trace correlation.