Performs QA testing on the feature implemented in the current branch. Reads specs, docs, and API docstrings, creates a QA test plan, executes tests using Playwright against a debug server, and generates a report with found issues. Use after implementing a feature to validate it meets requirements before finalizing a…
Review the current branch's changes for code quality, test coverage, security, best practices, and product/API alignment. Use when asked to perform a code review.
Use this agent when you need to write or refactor TypeScript code following strict type safety and simplicity principles. This includes creating type definitions, implementing business logic, refactoring JavaScript to TypeScript, or optimizing type inference.
Use when: adding a new chart type to flint-chart, implementing chart templates across Vega-Lite, ECharts, Chart.js, gallery examples, test cases, or VLM rendering verification.
Reviewer role in the FigMirror loop. Audits a draft figure against the L1 reference image, L2 aesthetic library, and optional 3D insert; outputs ONE strict JSON object (anchor.whatisright + qualityfloor + fidelity.verdict + focusthemes). Vision-only audit — must NOT read data.txt, drawer notes, or any path outside the…
Drawer role in the FigMirror loop. Produces a self-contained matplotlib script + rendered PNG + iter notes that match a reference paper figure's STYLE (not its data). Reads the reference image, the user's data, the L2 aesthetic library, and optional 3D insert; runs an iter-0 anchor-measurement pass; self-checks the…
Stage-0 image cropper for FigMirror. Cleans the user-supplied reference screenshot before Drawer/Reviewer style analysis by preserving the raw upload, cropping away captions/page text/screenshot margins/neighboring panels when safe, writing referenceclean.png plus a before/after crop check and report. Dispatched…
Use this agent when architectural decisions are being made, system designs are being created or modified, code changes impact system architecture, or when evaluating scalability, maintainability, and architectural integrity. This agent should be used PROACTIVELY during architectural reviews.
Apply security patches from security-auditor findings. Requires audit report as input. Always proposes patches for human review — never applies without approval.
Performs ultra-granular per-function deep analysis for security audit context building. Use when analyzing dense functions, data-flow chains, cryptographic implementations, or state machines.
Turns an insights narrative into a concrete dashboard design spec — metric selection, layout grid, chart types, color plan, and linter evaluation criteria. Stage 2 of the dashboard pipeline. Produces no XML.
Scores a rendered dashboard screenshot against the design-standards rubric and the run's design spec, producing a lint report with XML-level fixes. Stage 4 of the dashboard pipeline — run after the human drops a render screenshot.
Analyzes a dataset against a business question using Python, producing an analytics plan and a standalone insights narrative. Use as stage 1 of the dashboard pipeline, or on its own when the user only wants analysis.