AI Agent Workflow Collection

Evaluate a multi-step AI agent's whole run — tool calls, intermediate steps, and final result — not just final-answer correctness, so you can pinpoint WHERE it went wrong. Use when building or debugging a tool-using or multi-step agent, when final-answer-only evals can't explain failures, or when a prompt/model change quietly makes the agent less efficient or more error-prone even though the answer still looks right.

Cut alert noise and make every page mean something — rewrite alerting rules to fire on user-felt symptoms (error rate, latency SLO burn, failed requests) instead of causes (high CPU, full disk), with duration windows and severity routing so only urgent, actionable conditions reach a human. Use when on-call is fatigued by low-value pages, when real incidents get missed in the noise, or when alerts fire on causes rather than impact.

Generate accurate architecture diagrams as Mermaid — straight from the codebase, not from imagination — by first choosing which view answers the question (container/component, sequence, ER, or state) and then reading the real entry points, module boundaries, service calls, and schema. Use when onboarding to an unfamiliar repo, documenting a system, or visualizing one complex flow.

Read-only review of authentication AND authorization flows — session/token model, cookie flags, CSRF, token rotation, password-reset/email-verification, OAuth redirect/state, and per-route object-level access checks — for exploitable gaps. Use before shipping login/session/token code, when adding a protected route or sharing-by-URL feature, or during a security pass. Reports findings by severity with location, impact, and the concrete fix; never edits code.

Rebase the current branch onto its base and walk every conflict methodically, resolving each by understanding both sides. Use when your feature branch has fallen behind main and you want a clean, linear history without clobbering changes.

Analyzes JavaScript bundle sizes, identifies large dependencies, and suggests optimizations for webpack, vite, rollup. Use when user asks to "analyze bundle", "optimize bundle size", "reduce bundle", "webpack analysis", or "tree shaking".

Design a canary / progressive rollout so a bad release reaches 1% of users instead of 100% — staged traffic with bake times, gating metrics compared against the concurrently-running stable baseline, and automated promote-or-rollback. Use when shipping a risky change, when you want automatic rollback on regression, or when moving off all-at-once deploys.

Draft a release changelog by summarizing merged pull requests since the last tag. Use when preparing a release or writing release notes.

Find the chunking strategy and size that maximizes retrieval quality for a specific corpus, by sweeping configurations against a fixed eval set instead of guessing. Use when RAG answers miss obvious content, when standing up a new corpus, or when picking chunk size/overlap.

Detect and break a circular import — map the exact cycle with a real tool, then break the right edge by extracting the shared piece into a leaf module, inverting a layering dependency, merging two falsely-split modules, or (last resort) deferring an import. Use when you hit an import cycle error, an undefined-on-import or 'cannot access before initialization' bug, or a bundler/linter flags a cycle.

Audit every Claude Code settings layer — user, project, local, and managed — and report the effective merged configuration with its risks: over-broad Bash allows, missing deny rules for secrets, bypassPermissions defaults, unvetted MCP servers and hooks, and rules that never match. Use before trusting a new repo's checked-in settings, or to harden your own before handing the agent more autonomy.

Provides guidance on reducing Lambda cold start times through binary optimization, lazy initialization, and deployment strategies. Activates when users discuss cold starts or deployment configuration.

Size and tune a database connection pool from the real constraint — the database's shared maxconnections and its core count — so total connections (per-instance pool × instance count) stay safely under the cap and a too-large pool stops adding latency. Use when the app throws 'too many connections' or pool-acquire timeouts, when the DB is saturated by connection count, or when deploying to serverless.

Design consumer-driven contract tests between services so an API provider can't break its consumers unnoticed — without slow, flaky full end-to-end environments. Use when independent services or teams integrate over an API, when integration bugs only surface in staging or prod, or when E2E suites are too slow and brittle to catch breaking API changes.

Diagnose and fix a failing cross-origin browser request — read the exact console error, work out whether it's a simple or preflighted request, and set the minimal correct Access-Control- response headers (including the wildcard-with-credentials trap and the OPTIONS preflight) on the server. Use when the browser blocks a fetch/XHR with a CORS error but the API itself works from curl or Postman.

Run the project's coverage tool and identify the highest-value untested paths — error branches, edge cases, and critical modules — then propose specific test cases for each gap. Use when you have a coverage report but don't know where new tests will pay off most.

Find genuinely unused code — unreferenced exports, unreachable files, and unused dependencies — and remove it safely with build/test verification. Use when trimming a codebase or untangling years of accreted cruft.

Diagnose a database deadlock from the engine's own deadlock report, reconstruct the lock cycle (A holds 1 wants 2, B holds 2 wants 1), name the root cause — almost always two code paths locking the same rows in different orders — and fix it with consistent lock ordering, shorter transactions, and a retry-the-victim safeguard. Use when the DB logs deadlock errors, when transactions intermittently fail under load, or when queries mysteriously block each other.

Plan and de-risk a major dependency, framework, or runtime upgrade — map the full version path, read every intermediate migration guide, and pin the breaking changes to your actual call sites instead of bumping the number and hoping. Use when a key dependency is several majors behind, when a security advisory forces an upgrade, or before a framework migration.

Design a reproducible dev environment (Dev Container / Docker) so onboarding is one command and 'works on my machine' dies — by detecting the project's real stack and versions, authoring a devcontainer.json (+ Dockerfile/compose) that pins the runtime to what the repo targets, wires dependent services, caches dependencies, and injects secrets instead of baking them. Use when new contributors struggle to set up the project, when environment drift causes inconsistent behavior, or when standardizing tooling across a team.

Instrument a service (or a chain of services) with OpenTelemetry so a single request can be followed end-to-end — context propagated across every hop including async/queue boundaries, spans at the boundaries that matter, deliberate trace-wide sampling, and traceid stamped on log lines. Use when latency or failures span multiple services, when you have logs but can't reconstruct a request's full path, or when adopting OpenTelemetry.

Tune a vector index — HNSW graph parameters and quantization — to hit a recall target at the lowest latency and memory, by sweeping settings against a fixed query set instead of trusting defaults. Use when vector search is slow or memory-hungry, when recall dropped after enabling quantization, or when standing up an index and you need defensible parameters.

Diagnose the health of an embedding set before blaming the retriever — checking normalization, dimensionality, near-duplicates, degenerate vectors, and corpus/query distribution mismatch. Use when retrieval quality is poor, after a re-embed, or before shipping a new index.

Retire stale feature flags by confirming each flag's decided final state, then collapsing every conditional to the winning branch and deleting the loser plus the now-dead code it reached. Use when temporary flags have outlived their rollout, when flag conditionals clutter the code, or during a flag-debt cleanup.