mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
49 lines
2.3 KiB
Markdown
49 lines
2.3 KiB
Markdown
# Examples
|
|
|
|
This directory contains example outputs demonstrating how the agency's agents can be orchestrated together to tackle real-world tasks.
|
|
|
|
## Why This Exists
|
|
|
|
The agency-agents repo defines dozens of specialized agents across engineering, design, marketing, product, support, spatial computing, and project management. But agent definitions alone don't show what happens when you **deploy them all at once** on a single mission.
|
|
|
|
These examples answer the question: *"What does it actually look like when the full agency collaborates?"*
|
|
|
|
## Contents
|
|
|
|
### [nexus-spatial-discovery.md](./nexus-spatial-discovery.md)
|
|
|
|
**What:** A complete product discovery exercise where 8 agents worked in parallel to evaluate a software opportunity and produce a unified plan.
|
|
|
|
**The scenario:** Web research identified an opportunity at the intersection of AI agent orchestration and spatial computing. The entire agency was then deployed simultaneously to produce:
|
|
|
|
- Market validation and competitive analysis
|
|
- Technical architecture (8-service system design with full SQL schema)
|
|
- Brand strategy and visual identity
|
|
- Go-to-market and growth plan
|
|
- Customer support operations blueprint
|
|
- UX research plan with personas and journey maps
|
|
- 35-week project execution plan with 65 sprint tickets
|
|
- Spatial interface architecture specification
|
|
|
|
**Agents used:**
|
|
| Agent | Role |
|
|
|-------|------|
|
|
| Product Trend Researcher | Market validation, competitive landscape |
|
|
| Backend Architect | System architecture, data model, API design |
|
|
| Brand Guardian | Positioning, visual identity, naming |
|
|
| Growth Hacker | GTM strategy, pricing, launch plan |
|
|
| Support Responder | Support tiers, onboarding, community |
|
|
| UX Researcher | Personas, journey maps, design principles |
|
|
| Project Shepherd | Phase plan, sprints, risk register |
|
|
| XR Interface Architect | Spatial UI specification |
|
|
|
|
**Key takeaway:** All 8 agents ran in parallel and produced coherent, cross-referencing plans without coordination overhead. The output demonstrates the agency's ability to go from "find an opportunity" to "here's the full blueprint" in a single session.
|
|
|
|
## Adding New Examples
|
|
|
|
If you run an interesting multi-agent exercise, consider adding it here. Good examples show:
|
|
|
|
- Multiple agents collaborating on a shared objective
|
|
- The breadth of the agency's capabilities
|
|
- Real-world applicability of the agent definitions
|