feat: Initial commit - Hermes Detective Agency concept

- Hermes Detective Agency: Open-ended mystery investigation game
- Roles: Chief (human), Witness (Kimi), Detective (Hermes)
- 5 difficulty levels, community cases, open-ended solving
- Scoring: Alignment %, Evidence %, Time
- Features: Retry, Journal, Observe mode
- Tech: Kimi Vision + Hermes Agent + Pollinations

Changelog:
- Research phase: Kimi capabilities, Hermes agent, image APIs
- Brainstorming: 14 ideas explored
- Comparison matrix: Detective selected as winner
- Concept finalized with all design decisions

docs/ideas/001-visual-narrative-agent.md

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+# Idea 001: Visual Narrative Agent
+
+**Date:** 2026-04-19  
+**Status:** Idea  
+**Tags:** hermes-agent, kimi-vision, storytelling, image-generation
+
+## Concept
+
+An agentic storytelling system where Hermes orchestrates a narrative loop with Kimi's visual analysis and built-in image generation skills to produce coherent visual stories.
+
+## User Flow
+
+1. User provides text prompt (e.g., "A lone astronaut discovers an ancient alien garden on Mars")
+2. Hermes plans story structure (scenes, pacing, visual style)
+3. For each scene:
+   - Hermes generates image prompt
+   - Generate image (Hermes built-in skill: manim / ascii)
+   - Kimi analyzes generated image
+   - Kimi's feedback refines next scene's prompt
+4. Return compiled visual story to user
+
+## Key Differentiator
+
+Most story-to-image tools: **Generate → Done**
+
+This concept: **Generate → Analyze → Refine → Loop**
+
+Kimi serves as the **visual reasoning engine** — tells Hermes if the generated image matches the intended scene, catches inconsistencies, and informs prompt refinement for the next scene.
+
+## Tech Stack
+
+| Component | Source | Role |
+|-----------|--------|------|
+| Hermes Agent | Nous Research | Orchestration, planning, decision loop |
+| Kimi Vision | Moonshot AI (via gateway) | Image analysis, visual feedback |
+| Image Generation | Pollinations AI | Free tier, multiple models (Flux, etc.) |
+
+### Image Generation Options
+
+| Provider | Free Tier | Quality | Use Case |
+|---------|-----------|---------|----------|
+| **Pollinations** ✅ | ✅ Yes | Good | Primary (simple, free) |
+| **Flux (local)** | ✅ Free | High | If GPU available |
+| **Hermes skills** | ✅ Free | Niche | Fallback/ASCII aesthetic |
+
+### Pollinations API (Primary)
+- **Endpoint:** `https://gen.pollinations.ai/image/{prompt}`
+- **Models:** flux, zimage, wan-image, qwen-image, etc.
+- **Cost:** Free tier (pollen credits), ~$1/1 Pollen paid
+- **Auth:** Optional for free tier
+
+## Strengths
+
+- ✅ Combines Hermes + Kimi + Pollinations natively
+- ✅ Agentic visual feedback loop is unique
+- ✅ Visual coherence check via Kimi ensures quality
+- ✅ Free tier = low barrier to test
+- ✅ User controls output format (default: image)
+
+## Weaknesses
+
+- ⚠️ Pollinations quality vs DALL-E/Midjourney (may need to test)
+- ⚠️ Kimi requires gateway access (no direct API key)
+- ⚠️ Loop adds latency (generate → analyze → refine)
+- ⚠️ Need to verify Pollinations reliability
+
+## Uniqueness Score
+
+**7/10** — Agentic visual feedback loop is novel, but need to verify if built-in image generation is compelling enough
+
+## Next Steps
+
+- [ ] Explore Hermes built-in image skills (manim, ascii)
+- [ ] Define output format options
+- [ ] Sketch technical architecture
+
+## Related Ideas
+
+- See: `002-xxx.md`, `003-xxx.md` for alternatives