personas

Session-after-session product perfection loop. The strongest available model (Fable) directs — it walks the repo's context map context-by-context, proposes 5 challenged, high-value directions per context (features, design elevations, significant optimizations), gates them with the user until 10 are accepted, then orchestrates one Opus builder subagent per context in isolated worktrees while making every review/merge decision itself. All state lives in a linked Obsidian vault so any future session resumes the loop exactly where the last one stopped. Invoke with `/perfect [init|propose|build|status|reflect] [context-name]`.

Generate images with OpenAI gpt-image-2 (primary) or Leonardo AI (fallback), remove backgrounds, analyze with Gemini vision, and write SVG. For brand assets, UI illustrations, backgrounds, and icons.

Upgrade a generic UI icon or loading/empty state into a traced, motion-animated SVG. Generates flat trace-friendly art (via /leonardo tools), validates with Qwen vision, vectorizes to a clean multi-path SVG, and emits a Motion (framer-motion) reveal component. For icon + loading-state visual upgrades — NOT raw image generation (use /leonardo for that).

Hunts the highest-value surface of an LLM-powered app — the LLM call sites themselves — and drives them to their potential across three lenses. (1) Code quality of the AI plumbing (wrapping/chokepoint, logging/telemetry, caching/dedupe, schema+validation+self-repair). (2) Business value, by reusing the UAT Character method (representative users with jobs-to-be-done + a senior-quality bar + time-saved) but TESTING ONLY THE LLM PIECES — does each prompt's grounding and output clear the bar. (3) Model optimization as an alternative scenario — benchmark the same character inputs across models × thinking levels to find quality degradation/upgrade vs cost/latency. Everything is memorized in a linked **Obsidian vault** (one note per call site / character / model / session) so each scan builds on the last. Stack-agnostic engine; per-app specifics live in the repo's `tiger/` overlay (which IS the vault). Invoke with `/tiger init|scan|run|benchmark|recall|backlog [args]`.

Simulated User Acceptance Testing for the Personas desktop app, driven by Characters (representative users with jobs-to-be-done) rather than feature/code coverage. A capable LLM verifies each user journey in two chronological certification levels — L1 theoretical (over a code-derived surface model, cheap + mass-parallel) then L2 empirical (the LIVE app driven through the test-automation harness, serial) — judging through each Character's own consistent lens (time saved vs the manual way, and senior-in-role quality). Personas-specific: L1 reads context-map.json + the React/Rust source; L2 drives the running app via the test-automation server on :17320 (NOT a browser). Per-run specifics live in the repo's uat/ overlay. Invoke with `/uat init|update|run|promote [args]`.

Iteratively prototype a UI component through directional variants behind a tab switcher, then consolidate and refactor the winner. Use when the user wants to improve a component they consider a pillar of the app (visual appeal, creativity, UX clarity).

Personalized AI-native onboarding for xkazm04/personas — generated by Ascent from a scan on 2026-06-10 (currently L4 Integrated, 75/100). Run it to adopt the highest-leverage practices (D4, D9, D8, D2) that move this repo toward autonomous, LLM-driven development.

Production-readiness code review for Rust backend and React frontend changes

Fetch active Sentry issues for the Personas project, diagnose root causes from stack traces, apply code fixes, and mark issues as resolved. Covers both Rust backend (tracing::error!, panics) and React frontend (exceptions, unhandled rejections).

Systematically triage, challenge, and process a backlog of auto-generated idea files. Groups by context area, validates against codebase, filters BS, presents one approval gate, then executes autonomously with code review.

Analyze recent Claude Code sessions and write business-agnostic behavioral reflections to Obsidian

Load behavioral patterns and recent reflections from Obsidian to prime the current session with cross-project self-knowledge

Record feature demo walkthrough with Playwright, generate commentary subtitles, produce final video with ffmpeg