Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add itsribbZ/Godspeed --skill localgit clone --depth 1 https://github.com/itsribbZ/GodspeedWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/itsribbz/godspeed/local)<a href="https://agentmods.dev/skills/itsribbz/godspeed/local"><img src="https://agentmods.dev/badge/skills/itsribbz/godspeed/local/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/itsribbz/godspeed/local"><img src="https://agentmods.dev/badge/skills/itsribbz/godspeed/local.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01029 |
| Opus 5.5 | $0.00000 | $0.00412 |
| Sonnet 5.5 | $0.00000 | $0.00206 |
| Haiku 4.5 | $0.00000 | $0.00103 |
Grade A, and why
local scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Toke Local — Workbench
This skill is the entry point for Toke's local LLM automation. It runs alongside the existing Brain router — Brain classifies the prompt's tier, Local decides whether to handle it on-device.
Architecture
prompt
▼
brain.classify() ← existing Toke brain
▼
tier ∈ {S0,S1,S2}? ────────────────────► local Qwen via Ollama
▼
tier ∈ {S3,S4,S5} ──► Claude (default) confidence_monitor
▼
is_critical (< 12.5%) ?
│
yes │ no
▼ ▼
claude_override return
gate (y/n)
▼
approved? → Claude
declined? → use local
Canonical paths
- CLI:
$TOKE_ROOT/automations/local/local_cli.py - Manifest:
$TOKE_ROOT/automations/local/local_manifest.toml - Decisions:
~/.claude/telemetry/local/local_decisions.jsonl
Commands
local query "prompt"
Run a prompt through the full pipeline: brain classification → local-eligible check → Qwen generation → confidence monitor → optional override.
python "$TOKE_ROOT/automations/local/local_cli.py" query "summarize the routing manifest"
local ping
Health check: is Ollama serving? Is the configured model present?
python "$TOKE_ROOT/automations/local/local_cli.py" ping
local stats [N]
Show aggregate stats from the last N decisions: override rate, approval rate, avg confidence, token savings vs all-Claude.
python "$TOKE_ROOT/automations/local/local_cli.py" stats 100
local config [key=value]
Show or update local_manifest.toml settings. With no args, prints current config.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 115 lines · 0 tokens per session scan A 74c050c4a447
local is a skill published in the GitHub repository itsribbZ/Godspeed (1 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,029 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
cortex-prompt
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".
model-strategy
Rollenbasierte Multi-Modell-Strategie. Modellkatalog liegt in einer lokalen Konfigurationsdatei statt im Skill-Text -- Rollen (Denker/Entscheider, Dauer-Worker, Entwurfsgenerator, Reviewer/Abnahme, Eskalation), Reasoning-Effort als eigene Achse, Abnahmepflicht für Entwurfsmodelle, Anbieterwechsel für unabhängige…
agy-prompting
Internal helper — how to tighten a user request into a sharp prompt for the Antigravity CLI (agy / Gemini 3.x with native web search and agentic tools).
refine
Crossover layer between the user's intent and LLM execution: takes a raw prompt (terse, frustrated, ambiguous, or shorthand), resolves every vague reference to a concrete artifact, recalls past decisions and lessons, and compiles a verifiable execution contract BEFORE any code is touched. Invoke explicitly as $refine…
prompt-optimizer
Optimize prompts for Claude's latest models (Claude 5 family — Opus 5, Sonnet 5, Fable 5 — plus the still-active Opus 4.8, Sonnet 4.6, and Haiku 4.5) using Anthropic's official prompt-engineering guidance. Use when users want to improve, refine, debug, or create prompts or system prompts for Claude. Triggers include…
ai-integration
Guidelines for building AI-powered features into applications. Covers tool-use agent patterns, Anthropic API integration, prompt engineering for app-internal agents, and best practices. Referenced by planner and generators. Not directly user-invocable.