BooSkills

Agent skill catalog, deterministic model router, and Paseo orchestration presets -- personal, local-only.

What it is

16 specialized skills for AI coding agents, 12 agent personas rendered for 3 platforms, and a deterministic model-router that scores providers by grade, role fit, cost, quota, and live load to pick the best model per dispatch.

Routing stack: boo-meta (goal decomposition) → paseo-boo (dispatch prompt) → boo-router → model-router/router.mjs (deterministic scoring with load-aware spread).

Architecture verdict: appropriately complex (3-layer stack, each layer has a distinct responsibility).

Install

git clone git@github.com:indifferentketchup/booskills.git ~/opt/booskills
cd ~/opt/booskills
bash scripts/install.sh          # skills, presets, registry, agents, router CLI
bash scripts/stamp-standing-rules.sh  # sync standing rules into all 16 skills
paseo-preset workhorse             # switch active preset (+ sync OMP modelRoles)

On Windows: pwsh scripts/install.ps1 (copy mode; re-run after every pull).

Docs

| File | Purpose | |------|---------| | SKILL_GUIDELINES.md | Format and convention canon for all skills | | SKILL_CATALOG_SPEC.md | Build spec: what each skill contains | | STANDING_RULES.md | Canonical rules stamped into every skill's Gotchas | | CONTEXT.md | Full project context map (structure, dependencies, conventions) | | DISTRIBUTION.md | How to ship to other machines (private git remote) | | research/architecture-analysis-report.md | Architecture complexity verdict and risk assessments |

Key numbers

• 16 skills (1489 SKILL.md lines total)
• 12 agent personas × 3 platform renderings (36 files)
• 12 Paseo orchestration presets (all array pools, zero pinned strings)
• Deterministic model-router: 547 lines scoring logic, 146 lines load ledger, Next.js control UI
• Routing categories: impl, ui, audit, research, planning
• Grade tiers: S, A, B, C, D (local), F (edge/embedding)

Model-router UI

cd model-router/ui
npm install
LLAMA_SWAP_URL=http://100.101.41.16:8401 PASEO_DIR=~/.paseo npm run dev

Control panel at http://localhost:3000 with Playground, Load dashboard, Provider priority editor, and Preset editor tabs.

Presets

| Preset | Grade | Pool | |--------|-------|------| | grade-S | S | GLM-5.1, Qwen3.7-Max, GPT-5.5, Opus, Fable, Composer-2.5, Gemini-3.1-Pro | | grade-A | A | Qwen3.7-Plus, Kimi-K2.6, GLM-5, Sonnet, GPT-5.4, Composer-1.5 | | grade-B | B | MiniMax-M3, Mimo-V2.5-Pro, DeepSeek-V4-Pro, Haiku, GPT-5.1-Codex-Mini, Laguna-M.1, Owl-Alpha, Step-3.7-Flash | | grade-C | C | MiMo-V2.5, DeepSeek-V4-Flash | | grade-D | D | Qwen3.6-35b-a3b, Qwen3.6-27b (local, $0) | | grade-F | F | llama-swap embed + gateway free-tier | | workhorse | C+A | MiMo-V2.5, DeepSeek-V4-Flash, MiniMax-M3, Step-3.7-Flash | | workhorse-local | D | Local qwen duo | | local | D | Nemotron Cascade 30B + Qwen 9B | | free | C | Gateway free-tier (Nemotron Ultra, MiniMax M2.5, Step 3.7 Flash) | | subscription-low | B | GPT-5.1-Codex-Mini + Haiku | | subscription-mid | A | GPT-5.4 + Sonnet |

All presets use array pools -- the orchestrator picks by situation via the model-router. Provider strings are Pi/OMP provider/model format (cloud gateway models via litellm/ LiteLLM proxy, llama-swap local, native Anthropic/OpenAI-Codex/Cursor/Gemini).

Regenerate and install presets after editing scripts/generate-presets.py:

bash scripts/seed-presets.sh       # regenerate presets + model-tiers.json, install CLIs
paseo-preset <name>                # activate preset, sync OMP modelRoles, refresh OpenCode agents
omp-preset <name>                  # sync OMP modelRoles only