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].
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryProduct & designStars0
About this skill
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].
Quick start
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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]`.
About this listing
Category
Product & design
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI