Generate or audit a repository's AGENTS.md — the agent-policy file Claude Code, Codex, Cursor, Copilot, Kiro and Antigravity read — with every command traced to real evidence in the repo, generic virtue instructions stripped, and forbidden actions named. Use when a repo has no AGENTS.md, when an existing one is stale or ignored, when onboarding a codebase to agentic coding, or when an agent keeps running commands that don't exist.
Generate a real PowerPoint (.pptx) file from content, target audience, and a color palette. Produces structurally-appropriate decks (slide count, density, layout choice) tuned for the audience — execs, technical reviewers, sales prospects, investors, internal teams. Use when the user wants an actual editable .pptx, not just an outline.
Work with Figma and FigJam files via the Figma REST API — read existing file structure, list frames/pages, post comments, and produce FigJam-importable diagram specs. Use when the user mentions a Figma URL, asks to inspect/audit a Figma file, wants to comment on a design programmatically, or wants their architecture diagram in Figma.
Threat-model an AI agent deployment against the lethal trifecta — private data, untrusted content, and an exfiltration vector — producing a per-capability matrix, a named architectural fix for every unsafe path, and a pre-launch checklist. Use before shipping an agent, when reviewing MCP server or tool permissions, when the user asks about prompt injection or data exfiltration risk, or when deciding whether an agent's capability surface is safe to expose.
Adversarial review of just-generated code, run *after* an agent (or human) declares a feature done. Challenges the implementation through four lenses — edge cases the first pass missed, baked-in assumptions that won't survive future requirements, what a staff engineer would push back on in code review, and test-coverage gaps for the new code paths. Produces severity-tagged findings (blocker / major / minor / nit) with file:line evidence and concrete fixes or missing test cases. Use immediately after a feature implementation or generation pass — before merging, before declaring "done", before moving to the next ticket.
Generate a per-user or team-level report on how AI tools are being used — volume, breadth, and effectiveness signals (e.g. whether AI outputs landed in shipped artifacts vs were generated and discarded). Consumes usage-event logs exported from an MCP server or other telemetry source. Use when a manager or team lead needs to understand AI adoption beyond raw counts — including spotting "AI theater" where people invoke AI to claim usage but don't act on the outputs.
Write the spend spec for an agentic workflow — per-stage model tiers, token caps with hard abort rules, a graceful-degradation order, and cost-per-outcome as the governing metric. Use when the user asks what an agent loop or multi-agent workflow should be allowed to spend, wants token/cost budgets and caps for AI automation, or got a surprise bill from an agent fleet.
Design a supervised agent loop — the generate→verify→gate cycle, observable exit criteria, hard iteration cap, human gates at irreversible steps, and failure routes — as a loop spec a team can implement in any agent harness. Use when the user wants to automate a recurring task with an AI agent loop, design a work loop / review loop / research loop, or asks "how do I stop my agent from running forever or shipping junk".