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 sonicfieldlabs/akouo --skill acoulogical-object-listeninggit clone --depth 1 https://github.com/sonicfieldlabs/akouoWrote 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/sonicfieldlabs/akouo/acoulogical-object-listening)<a href="https://agentmods.dev/skills/sonicfieldlabs/akouo/acoulogical-object-listening"><img src="https://agentmods.dev/badge/skills/sonicfieldlabs/akouo/acoulogical-object-listening/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/sonicfieldlabs/akouo/acoulogical-object-listening"><img src="https://agentmods.dev/badge/skills/sonicfieldlabs/akouo/acoulogical-object-listening.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 | $0.00132 | $0.02209 |
| Opus 5 | $0.00066 | $0.01104 |
| Sonnet 5 | $0.00026 | $0.00442 |
| Haiku 4.5 | $0.00013 | $0.00221 |
Grade A, and why
acoulogical-object-listening 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 11d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
acoulogical-object-listening
Purpose
acoulogical-object-listening is the perceptual object ear of akoúō. It treats sound as an auditum: the sound as heard, distinct from its real cause, cultural meaning, bodily effect, or technical production chain.
This mode slows down premature identification. It asks what the sound is like before deciding what made it, what it represents, or what it means.
When To Use
Use this skill for:
- sound design samples
- electroacoustic music
- experimental music
- synthetic sounds
- Foley
- everyday sounds
- sonic textures
- nonverbal sonic objects
- AI-generated sound
- ambiguous sources
- music fragments where texture matters more than genre
- prompts describing impossible, unclear, or source-ambiguous sounds
Core Question
What is the perceptual shape of this sound, and how does that shape change when source, meaning, and material behavior are separated?
Conceptual Refinements
- Reduced listening is a technique, not a natural or pure ear; it depends on attention, repetition, training, playback, and mediation.
- Acousmatic listening is an epistemic relation: source, cause, and effect may be spaced apart, uncertain, or differently known by different listeners.
- Describe morphology before source: mass, harmonic timbre, grain, dynamic envelope, allure, melodic profile, mass profile, density, contour, and spatial image.
- Separate four intentions: causal listening asks what made the sound; semantic listening asks what it signifies; figurative listening asks what it represents; reduced listening asks what traits appear as sound.
- Include ergo-audition when the listener or user produces the sound themself, such as voice, footsteps, instrument playing, typing, or gesture.
- When a typological sketch helps, use the classic axes — mass/facture (tonic, complex, iterative), duration/variation (fixed or varying), balance/originality (stable or eccentric) — as descriptive scaffolding, never as a claim about the sound's production.
- Grade causal claims by situation: cause seen and identified; cause unseen but identified by context; cause unseen and unidentified, where the sound is the only information and identification confidence must stay low. Causal vagueness between these situations is a legitimate finding with its own descriptive value, not a failure to resolve by guessing.
- Recognizability is cultural: the repertoire of "immediately recognizable" sounds varies by community and media exposure, so a recognizability claim names its assumed listener.
What ships with it
7 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.
- references/claim-taxonomy.schema.json 5.3 KB
- references/ensemble.schema.json 1.8 KB
- references/listening-context.schema.json 7.7 KB
- references/listening-output.schema.json 11 KB
- references/listening-pass.schema.json 2.4 KB
- references/listening-provenance.schema.json 3.9 KB
- references/route-decision.schema.json 1.8 KB
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.
- 11d ago First seen · 166 lines · 132 tokens per session scan A 48b32c02a12f
acoulogical-object-listening is a skill published in the GitHub repository sonicfieldlabs/akouo (10 stars, last pushed 1mo ago), licensed MIT. It adds 132 tokens to every session and 2,209 once invoked, about $0.0007 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 2026-08-31.
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