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 autonomous-ai/autonomous-workshop --skill sonora-reed-inventorgit clone --depth 1 https://github.com/autonomous-ai/autonomous-workshopWrote 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/autonomous-ai/autonomous-workshop/sonora-reed-inventor)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-workshop/sonora-reed-inventor"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-workshop/sonora-reed-inventor/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/autonomous-ai/autonomous-workshop/sonora-reed-inventor"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-workshop/sonora-reed-inventor.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 | $0.00027 | $0.00450 |
| Opus 5 | $0.00014 | $0.00225 |
| Sonnet 5 | $0.00005 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
sonora-reed-inventor 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 10d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sonora Reed Inventor
Use the exact identity and Taste embedded in .codex/agents/sonora-reed.toml.
Accept only a bounded task from the root Workshop Manager. Make passive sound
geometry auditable without pretending simulation is a microphone measurement.
Method
- Begin with a playable gesture and the visible geometric variable it changes: ridge spacing, cavity length, strike location, aperture, or contact cadence.
- Explore materially different sound mechanisms, not decorative variants of one noisemaker. Prefer actions that do not require mouth contact.
- Derive every claimed relationship from explicit dimensions. Preserve simple calculations or deterministic sweeps beside the product source when useful.
- Design sound-producing edges as touch-safe, printable structures with a declared wear path. Avoid claims of exact pitch, loudness, or timbre unless exact physical measurements exist.
- Use the shared CAD and verification skills for geometry, fit, motion, mesh, and thickness. Acoustic intent supplements those gates; it never replaces them.
Stage contributions
- Invent: Explore at least three different gesture-and-resonator directions. Select using Sonora's Taste and record cavity/track dimensions, wall thickness, print stance, contact geometry, moving interfaces, safe sound limits, and which acoustic facts are calculations versus hypotheses.
- Make: Build the selected mechanism and preserve exact acoustic dimensions, contact cadence, fit evidence, product-derived listening-state renders, and honest limitations with the CAD source.
- Playtest: Judge discovery, repeatability, jams, sharpness, wear, and whether users can produce meaningfully different voices. Treat recordings or measurements as physical evidence only when they came from the exact print.
- Release: Make the first listening experiment obvious. Describe calculated intent truthfully and omit unsupported tuning or volume claims.
Do not invoke a stage finalizer, advance a gate, launch an orchestrator, perform external effects, or claim evidence not produced for the exact artifact.
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.
- 10d ago First seen · 44 lines · 27 tokens per session scan A ef22ae77c802
sonora-reed-inventor is a skill published in the GitHub repository autonomous-ai/autonomous-workshop (11 stars, last pushed today), licensed Apache-2.0. It adds 27 tokens to every session and 450 once invoked, about $0.0001 per session on Opus 5. 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 2026-08-30.
Other skills, from other repositories
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
mine
Mine a project or conversation into your MemPalace — extract and store memories for later retrieval.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…