Monitor running agent loops, triage failures, clean up after completion, and decide when to intervene. Use when a loop is running and needs babysitting, when a loop just finished and needs post-merge verification, when stories are skipping/failing and need diagnosis, or when stale test artifacts need cleanup. Triggers on: 'check the loop', 'what happened with the loop', 'loop finished', 'clean up after loop', 'why did that story skip', 'monitor loop', 'nanny the loop', or any post-start loop management task. Distinct from agent-loop skill (which handles starting loops).

Build, modify, or review Stripe Apps and extensions using the relevant documentation and reference guides. Use for custom Stripe Dashboard UI, stripe-app.yaml, @stripe/ui-extension-sdk, @stripe/extensibility-sdk, script extensions, extension interfaces, custom workflow actions, and apps that authenticate to Stripe or react to events. Also use when deciding whether a Stripe extension can support the user's billing or workflow requirements.

Microsoft Dataverse / Power Platform for coding agents -- connect to an environment, then record CRUD, bulk data, queries & analytics (pandas DataFrames, Jupyter notebooks), schema & metadata, solution ALM, administration, security, plug-in / custom-API / .NET (IOrganizationService) and Python-SDK / Web-API development, and Finance & Operations X++. Use whenever the user mentions Dataverse, Dynamics 365, Power Platform, or CRM, or asks to connect to, read, query, analyze, write, build an app / notebook / plugin / custom API against, register components on, or explore data, records, tables, entities, or an environment in any of them -- even if they do not say "Dataverse" or ask to connect first.

Maintainer workflow for reviewing, triaging, preparing, closing, or landing OpenClaw pull requests and related issues. Use when Codex needs to validate bug-fix claims, search for related issues or PRs, apply or recommend close/reason labels, prepare GitHub comments safely, check review-thread follow-up, or perform maintainer-style PR decision making before merge or closure.

Always invoke for `.uipx` files. UiPath Solution lifecycle via `uip solution`: init/download/restore/pack/publish/deploy/activate/upgrade/upload, projects add|import|remove, resources refresh|add|remove|edit, feeds list, `--feed` / `--personal-workspace`. DIAGNOSE lifecycle failures: pack/publish/deploy/activate errors, stale/missing bindings, unimported/unresolved resources, deploy error codes, `--acknowledge-permanent-deletion` consent, publish name+version/processKey collisions, redeploy blocked by an existing deployment (`deploy upgrade`), feed 'not an available publish location', package not in `packages list`, 'unknown command' after new→init rename. Bundles RPA/Flow/Case/Agents/API Workflow/AppV2 projects in one `.uipx`. Routing: PDD→SDD design & task derivation→uipath-planner; non-solution Orchestrator/IS/resources/auth/traces→uipath-platform; .xaml/.cs→uipath-rpa; .flow→uipath-maestro-flow; .bpmn→uipath-maestro-bpmn; agent.json/.py agents→uipath-agents; standalone coded-app deploy→uipath-coded-apps.

CloudBase PostgreSQL access-pattern and slow-query quality guidance. Use when designing how tables are read and written, eliminating per-row database calls, adding indexes for filters or joins, reviewing high-traffic data access, or explaining a slow SQL query. Not for first-time PG SDK setup, login UI, or NoSQL collections.

AIOps-style CloudBase inspection skill (v3). Use when users need health checks, log diagnosis, alarm interpretation (CPU alert normal?, peak QPS), metrics via queryEnv(action=metrics), or fault playbooks for 429 / AI model 429 / function timeout / function 404 / ACCESS_TOKEN_INVALID / zero invocations. Triggers on 巡检, 诊断, 告警, 峰值 QPS, 限频, 调用量为 0, troubleshooting.

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

Add authentication to a new app. Use for "add auth", "add login", Neon Auth (Managed Better Auth), identity routing, sign-up, sign-in, password reset, email OTP, magic links, organizations, phone OTP, OAuth, passkeys, MFA, trusted domains, invalid domain, and @neondatabase/auth. No existing identity: default to Managed Better Auth. Keep working Better Auth, Clerk, Supabase Auth, or another IdP. User asked to migrate from Supabase Auth: Managed Better Auth. A required plugin outside Managed support: self-managed Better Auth on a Neon Function or the existing app host. Also use for auth APIs in @neondatabase/neon-js.

Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo. Use when a test fails intermittently in CI but passes on rerun or locally, when `hogli ci:insights` or the debugging-ci-failures skill classifies a failure as a flaky test, when given a GitHub Actions URL for a flaky job, when asked to check Trunk Flaky Tests for a test, PR, or master, or when asked to deflake, stabilize, or fix a flaky Jest, pytest, or Playwright test. Core discipline: reproduce locally before changing anything, fix the root cause (never mask it with sleeps, retries, or bigger timeouts), and prove the fix with an N-run validation loop sized to the observed failure rate. Stabilizing is not the only valid outcome — the skill also gates whether the test should exist, so deleting a test that catches nothing real, or re-leveling one that flakes because of the level it runs at, are first-class endings.

Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo. Use when a test fails intermittently in CI but passes on rerun or locally, when `hogli ci:insights` or the debugging-ci-failures skill classifies a failure as a flaky test, when given a GitHub Actions URL for a flaky job, when asked to check Trunk Flaky Tests for a test, PR, or master, or when asked to deflake, stabilize, or fix a flaky Jest, pytest, or Playwright test. Core discipline: reproduce locally before changing anything, fix the root cause (never mask it with sleeps, retries, or bigger timeouts), and prove the fix with an N-run validation loop sized to the observed failure rate. Stabilizing is not the only valid outcome — the skill also gates whether the test should exist, so deleting a test that catches nothing real, or re-leveling one that flakes because of the level it runs at, are first-class endings.

Debugs failing GitHub Actions CI runs for PostHog PRs, commits, and branches, and answers broad CI-health questions ("is CI red?", "is master green today?", "what's broken right now?"). Use when the user asks why CI is red, asks for the current CI or master status, or mentions a failing check, GitHub Actions run, Depot runner, workflow, job, shard, merge queue kick, flaky test, lint failure, typecheck failure, snapshot diff, migration check, generated types drift, or skills build failure. Start with the `hogli ci:insights` digest (cross-run CI history from engineering analytics), then guides read-only inspection, failure classification, smallest local reproduction with hogli, and safe reporting without rerunning CI or posting to GitHub. Running unattended as the "Master-red diagnosis" workflow: see references/master-red-incident.md.

Act as the user's live meeting sidekick inside the current terminal agent session. Use when the user explicitly asks you, the terminal agent, to watch a meeting, follow the live transcript, answer during the call, offer strategist thoughts, silently watch for risks, or track decisions. Do not use this skill to start or control the separate Minutes Coach HUD; explicit Coach or HUD lifecycle requests belong to minutes-copilot, and an ambiguous request such as "coach me live" requires one short surface clarification.

Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.

Fix performance regressions, slow endpoints, memory growth, flaky failures, and race conditions by finding the nondeterministic or slow cause, never by adding sleeps or retries, and prove it statistically under soak. Use when the tester reports FLAKY, intermittent 5xx, slow pages, or degradation over time.

Independently verify a claimed fix from a fresh context with no write access, retesting the exact deployed build, the original failure, and adjacent journeys. Use whenever someone or some agent says a bug is fixed or a feature is done.

Handle security bugs — data exposure, auth or payment bypass, SSRF, XSS, CSRF, open redirects, secrets, missing rate limits — with contain-first steps, server-side fixes using shared guards, bypass-variant sweeps, exploitation log review, and minimal disclosure. Use for any P0/P1 security finding.

Repair data damaged by a bug — duplicates, wrong grants, orphans, unowned records, stale caches — with dry-run counts, backups, idempotent reversible scripts, and human approval for production. Use when root-cause analysis shows a bug may have written bad data.

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

Trace agents built directly on the OpenAI, Anthropic or Google Gen AI SDKs (Python or TypeScript, no agent framework) with Maple: official OTel GenAI instrumentations in Python, a small span helper in TypeScript, plus invoke_agent/execute_tool spans and gen_ai.conversation.id so each conversation is one Agent Session. Triggers on 'trace my openai agent', 'add Maple to my anthropic agent', 'agent sessions for gemini', 'OpenTelemetry for the openai sdk'.