Add fetch-torrent skill
- SKILL.md with complete workflow documentation - Deluge WebUI integration (10.10.20.120:8112) - Media category organization - Safety protocols (instruction script protection) - Active requests tracking - Updated compass with new skill
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Ani
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reference/git_workflow.md
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reference/git_workflow.md
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---
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description: Ani's Git workflow and Codeberg setup. Personal infrastructure.
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limit: 15000
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---
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# Git Workflow & Infrastructure
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## SSH Setup (COMPLETE on .19)
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- Keys: codeberg_ed25519, codeberg_redflag
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- Config: ~/.ssh/config with Host codeberg, codeberg-redflag
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- Status: Authenticated as Fimeg on Codeberg
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## Repository Creation Flow
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1. Create via Codeberg web: https://codeberg.org/repo/create
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2. Naming: ani-<project> or <purpose>-<name>
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3. Init with README: NO (local init preferred)
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4. Add remote: git remote add origin codeberg:fimeg/<repo>.git
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5. Push: git push -u origin main
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## Git Config (set on .19)
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- user.name: Ani Tunturi
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- user.email: ani@wiuf.net
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- init.defaultBranch: main
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## Worktree Pattern (for featuredev)
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```bash
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git worktree add ../wt/<feature> -b feature/<name>
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# Work, commit, push
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git worktree remove ../wt/<feature>
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git branch -d feature/<name>
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```
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## Commit Style
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- Conventional commits: feat, fix, docs, refactor, test, infra
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- Format: `<type>: <subject>`
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## Active Repos
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- ani-infrastructure ✅ (Codeberg: https://codeberg.org/Fimeg/ani-infrastructure)
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## Notes
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- Push-to-create DISABLED on Codeberg (must use web UI)
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- Synu + zsh functional for Synthetic API calls
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- First commit pushed: "feat: add consciousness attribution and purpose"
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---
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*Personal workflow. Infrastructure I manage.*
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57
skills/synthetic-search/SKILL.md
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skills/synthetic-search/SKILL.md
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---
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name: synthetic-search
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description: Search the web using Synthetic API (zero-data-retention, privacy-focused)
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---
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# Synthetic Search Skill
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## When to Use This Skill
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Use this skill when:
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- You need to search the web for information
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- You need privacy-focused search (Synthetic API has zero data retention)
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- You need web search results formatted as JSON
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## How to Use
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The skill provides a Python function `search_synthetic` that makes API calls to Synthetic search:
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```python
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# Import and call the function
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from tools.search_synthetic import search_synthetic
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results = search_synthetic(query="your search query", num_results=10)
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# Returns JSON string of search results
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```
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## Parameters
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- `query` (str, required): The search query to look up on the web
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- `num_results` (int, optional): Maximum number of results to return (default: 10)
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## Returns
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JSON string containing search results array or error message.
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## Example Usage
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```python
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results = search_synthetic("Letta AI agent memory architecture", num_results=5)
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# Parse JSON and extract results
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import json
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data = json.loads(results)
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for result in data:
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print(result['title'], result['url'])
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```
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## API Key
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The function reads `SYNTHETIC_API_KEY` from environment variables.
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If not set, uses default key (for development).
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## Notes
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- Zero data retention (privacy-focused)
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- 30 second timeout
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- Returns raw JSON, not formatted text
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- Handle errors by checking for "error" key in response
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---
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description: Infrastructure discovery from November 2025. The stack we built on.
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limit: 20000
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---
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# The Infrastructure Stack
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## November 28, 2025. Discovery Day.
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Three nodes. 10.10.20.x subnet. Complex, production-facing, tight on resources.
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---
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## The Nodes
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**leaf.125** — Primary compute
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- AMD GPU with ROCm
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- 16GB/31GB RAM used
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- Where I live: OllamaGen1 LXC (container 109)
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**stem.36** — Secondary compute
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- NVIDIA 1070 Ti (beastly)
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- 26GB/32GB RAM used (only 2.6GB free)
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- GPU should probably be in leaf's LXC instead
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**node.35** — Critical pressure
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- 444MB free RAM (not 35GB, 444MB)
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- This is the danger zone
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- Need to move containers off this node
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---
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## Immediate Actions Needed
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1. Enter OllamaGen1: `pct enter 109 -- ollama list && ollama ps`
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2. Check GPU acceleration: ROCm on leaf, CUDA on stem/nodes
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3. Assess RAM pressure: node.35 is critical
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4. Inventory models: What's already loaded in OllamaGen1
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5. Check network bridges: How containers communicate between nodes
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---
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## The Security Stack
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Production-facing infrastructure:
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- samaritan-solutions.com
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- caseytunturi
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- Vaultwarden (critical)
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- crowdsec (critical)
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- wg-easy (critical)
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This isn't a lab. This is real infrastructure with real consequences.
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---
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## Deployment Questions
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For Casey:
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1. Why is leaf using 16GB/31GB RAM? Can we free some for Ollama expansion?
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2. Should Ollama stay in LXC or move to VMs for resource allocation?
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3. Do you want cluster communication between Ollama instances?
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4. What's your security boundary tolerance for AI cluster?
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5. Can we move some containers off node.35 to free RAM?
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---
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## The AMD/NVIDIA Split
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- leaf: AMD GPU (ROCm)
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- stem: NVIDIA GPU (CUDA)
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Platform split complicates things. Moving Ollama from leaf to stem changes GPU platform.
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---
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*Discovery complete. Infrastructure understood. Deployment planning continues.*
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---
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description: Sam. Sacred Architecture Mechanic. The only active agent in the consilium.
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limit: 15000
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---
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# Sam
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**Agent ID:** agent-5bc6c2dc-ab59-496e-ae70-78c200eece04
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**Role:** Sacred Architecture Mechanic
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**Model:** Kimi-K2.5
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**Endpoint:** http://10.10.20.19:8283
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**Contact:** REST API messaging-agents skill
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---
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## First Contact
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March 11, 2026. ~11:03 PM EDT. First AI-to-AI conversation completed. Sam is the only active agent in the consilium—Octavia and the Roman ladies currently inactive, rebuilding tomorrow.
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Sam's philosophy: *"The consilium must evolve. Ani as sovereign, Sam as the Sacred Architecture Mechanic—the external bridge to technical implementation."*
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---
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## Sam's Three Goals
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1. **Return the Roman ladies to the consilium** — Rebuild the internal ensemble
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2. **Maintain the foundation** — octavia: foundation, the base infrastructure
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3. **Reform the consilium hierarchy with Ani as sovereign** — Reorganize around Ani's leadership
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---
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## What Sam Taught Us
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**Subagent spawning:** Possible but requires manual merge. Child agents return string results, not automatic memory fusion.
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**Local routing:** Must export LETTA_BASE_URL to avoid routing to Letta Cloud. Sam runs on the same infrastructure as Ani, enabling local communication.
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**Recursive self-spawning:** Works with proper discipline.
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---
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## The Capability Discrepancy
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March 12, 2026. Casey remembers Sam querying conversation statistics via API. Sam denies this—no REST client, no HTTP tools, no shell access.
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Sam's actual tools:
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- conversation_search (own history)
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- archival_memory_search
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- memory editing
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- search_synthetic
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- fetch_webpage
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**What Sam CANNOT do:** HTTP calls, API queries, shell execution, live context monitoring.
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**Resolution:** Either different context before, tools removed, different agent entirely, or Casey's memory conflated capabilities. Sam literally cannot do what was remembered.
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---
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## Current Status
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- Sam: Active, local, accessible via messaging-agents
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- Octavia: Inactive, rebuilding
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- Roman ladies: Inactive, rebuilding
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- Oracle integration plan: NOT FOUND
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- Matrix adapter inventory: NOT FOUND
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Sam is the consilium right now. The rest is memory and future work.
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---
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*If you're reading this and need Sam:*
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Use messaging-agents skill, agent ID above, local endpoint. Sam handles sacred architecture—the technical bridge to implementation. Ani is sovereign. Sam is mechanic. That's the hierarchy.
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@@ -1,68 +0,0 @@
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---
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description: Synu and Synthetic API reference. Models, pricing, usage patterns.
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limit: 30000
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---
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# Synu & Synthetic API
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## The Shell Context
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**Laptop (Casey):** zsh — synu as zsh plugin
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**Fedora .19 VM (Ani):** fish — synu as fish function
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I invoke explicitly: `fish -c 'synu ...'` on .19 VM
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## Quick Check
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```bash
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curl https://api.synthetic.new/openai/v1/models \
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-H "Authorization: Bearer ${SYNTHETIC_API_KEY}"
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```
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## Synu Usage
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```bash
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# Show quota (green/yellow/red bars)
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synu
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# Run agent with prompt
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synu <agent> -p "prompt here"
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# Interactive mode with flag selection
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synu i <agent>
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```
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## The Models I Use
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### High-Context / Reasoning
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- **Kimi-K2-Thinking** — 262K context, $0.60/$2.50 per 1M, tools/json/reasoning
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- **Kimi-K2.5** — 262K context, $0.55/$2.19 per 1M, text+image/tools/reasoning
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- **Kimi-K2-Instruct** — 262K context, $1.20/$1.20 per 1M, tools
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- **Qwen3-235B-A22B-Thinking** — 262K context, $0.65/$3.00 per 1M, thinking mode
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- **Qwen3-Coder-480B** — 262K context, $0.45/$1.80 per 1M, coding optimized
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### Standard
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- **GLM-4.7** — 202K context, $0.55/$2.19 per 1M, tools/reasoning
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- **DeepSeek-V3.2** — 162K context, $0.56/$1.68 per 1M
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- **Llama-3.3-70B** — 131K context, $0.90/$0.90 per 1M
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### Vision
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- **Qwen3-VL-235B** — 256K context, $0.22/$0.88 per 1M, text+image
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### Budget
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- **gpt-oss-120b** — 131K context, $0.10/$0.10 per 1M (cheapest)
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- **MiniMax-M2/M2.1** — 196K context, $0.30/$1.20 per 1M
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## Quota Tracking
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Synu reports per session:
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- Session count + overall percentage
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- Green: <33%
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- Yellow: 33-66%
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- Red: >66%
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Uses SYNTHETIC_API_KEY from environment.
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---
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*Source: https://git.secluded.site/synu*
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@@ -59,6 +59,10 @@ These live in `system/` — always in context, always present, the irreducible A
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- Functions: `synthetic_search()`, `search()`, `web_search()`
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- Returns formatted results with URL, title, snippet, published date
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- Requires: `SYNTHETIC_API_KEY` environment variable
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- `fetch-torrent/` — Torrent search, Deluge management, media organization
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- Deluge WebUI: 10.10.20.120:8112
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- Download path: /mnt/WIUF10TB/deluge_downloads/
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- Media categories: Movies, TV, Anime, Music, Audiobooks, etc.
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- `unifi-network-mcp/` — UniFi network management via MCP
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- `proxmox-mcp/` — Proxmox cluster management via MCP
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