Borrowing it
Nothing to install: this file belongs to dungnotnull/game-audio-feedback-accessibility-agent-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dungnotnull/game-audio-feedback-accessibility-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/game-audio-feedback-accessibility-agent-skillWrote 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/instructions/dungnotnull/game-audio-feedback-accessibility-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/game-audio-feedback-accessibility-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/game-audio-feedback-accessibility-agent-skill/claude-md.svg" alt="Measured on agentmods" 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 | $0.01270 | $0.01270 |
| Opus 5 | $0.00635 | $0.00635 |
| Sonnet 5 | $0.00254 | $0.00254 |
| Haiku 4.5 | $0.00127 | $0.00127 |
Grade A, and why
game-audio-feedback-accessibility-agent-skill CLAUDE.md 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 7d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Skill 270: game-audio-feedback-accessibility
Skill Identity
- Skill Name:
game-audio-feedback-accessibility - Tagline: Audio Feedback System Design for Visually-Impaired Accessibility — Audio-Based Game Accessibility & Sonification analysis & decision-support harness.
- Current Phase: Phase 0 — Architecture & Research
- Folder:
D:\972026\270-game-audio-feedback-accessibility\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Audio-Based Game Accessibility & Sonification. It gathers authoritative real-time and reference data, applies recognized domain methods, cross-references academic research, and delivers actionable outputs that are fully evidenced, risk/limitation-disclosed, and traceable to authoritative sources — continuously self-improving through an automated knowledge crawl pipeline.
Harness Flow Summary
/game-audio-feedback-accessibility invoked
│
├─ Step 1: sub-gather-requirements → Clarify the object of analysis, constraints, timeframe, available inputs, target audience, and language before any data fetching.
├─ Step 2: sub-evidence-collector → Fetch authoritative real-time and reference data for the object: current status/parameters, authoritative documents/standards, and recent developments from domain and academic sources.
├─ Step 3: sub-core-analysis → Design an audio-feedback system for visually-impaired players (sonification, cues, redundancy) so the game is playable without sight, grounded in accessibility science.
├─ Step 4: sub-knowledge-updater → Query SECOND-KNOWLEDGE-BRAIN.md for authoritative academic and professional evidence; surface citations with tier labels and flag gaps for the crawl pipeline.
├─ Step 5: sub-advisor → Synthesize all prior analysis into a risk-disclosed conclusion with a full evidence chain and recommended actions.
└─ Step 6: main (quality gate) → verify evidence hierarchy, disclosure, output polish
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.
- 7d ago First seen · 122 lines · 1,270 tokens per session scan A b83d29a593c8
game-audio-feedback-accessibility-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/game-audio-feedback-accessibility-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,270 tokens to every session, about $0.0064 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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