Build a quarterly business review from a client folder -- reports, usage exports, support tickets, meeting notes, emails. Extracts delivered value with receipts, surfaces risks before the client does, and drafts the QBR narrative plus expansion asks. For agencies, consultancies, and CS teams.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Prepare a QBR the way a top customer-success lead does: prove the value delivered with specifics, name the problems before the client names them, and earn the expansion conversation. Input: a folder of client artifacts for the quarter -- status reports, deliverables, usage/analytics exports, support tickets, meeting notes, email threads -- plus the engagement's stated goals if documented.
qbr-[client]-[quarter].md structured for a deck: executive summary, goals scorecard, wins with receipts, misses with fixes, next-quarter plan, and -- only where the evidence supports it -- 1-2 expansion recommendations framed around the client's goals, not your revenue. Hand to pptx or presentation-design-enhancer for the deck build.No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
The English summary is being prepared. Showing the original description.
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
The English summary is being prepared. Showing the original description.
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
The English summary is being prepared. Showing the original description.
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
The English summary is being prepared. Showing the original description.
Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
The English summary is being prepared. Showing the original description.
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
The English summary is being prepared. Showing the original description.