Borrowing it
Nothing to install: this file belongs to dungnotnull/retro-emulator-config-optimization-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/retro-emulator-config-optimization-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/retro-emulator-config-optimization-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/retro-emulator-config-optimization-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/retro-emulator-config-optimization-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/retro-emulator-config-optimization-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 | $0.02313 | $0.02313 |
| Opus 5 | $0.01156 | $0.01156 |
| Sonnet 5 | $0.00463 | $0.00463 |
| Haiku 4.5 | $0.00231 | $0.00231 |
Grade A, and why
retro-emulator-config-optimization-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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md ? Skill 200: retro-emulator-config-optimization
Skill Identity
- Skill Name:
retro-emulator-config-optimization - Tagline: Emulator Configuration Optimization for Smooth Retro Console Gaming ? Retro Emulation Configuration & Display Latency analysis & decision-support harness.
- Current Phase: Phase 6 - Production-Grade Modular Architecture Upgrade (PRODUCTION READY v1.1.1)
- Folder:
D:\972026\200-retro-emulator-config-optimization\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Retro Emulation Configuration & Display Latency. 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.
For the deterministic part of the work it ships an offline
tools/emulator_config_analyzer.py that parses a real RetroArch .cfg,
validates it against the encoded latency/accuracy heuristics, and emits a
per-key scorecard + verdict that the harness can cite directly.
Harness Flow Summary
/retro-emulator-config-optimization invoked
?
?? Step 1: sub-gather-requirements ? Clarify the object of analysis, constraints,
? timeframe, available inputs, target audience,
? language before any data fetching.
?? Step 2: sub-evidence-collector ? Fetch authoritative real-time + reference data:
? current emulator/core state, standards,
? recent developments, reference benchmarks.
?? Step 3: sub-core-analysis ? Optimize emulator config (accuracy vs performance,
? video, latency, audio) with Best/Base/Worst
? scenarios; delegate to emulator_config_analyzer.py.
?? Step 4: sub-knowledge-updater ? Query SECOND-KNOWLEDGE-BRAIN.md for 3?5 Tier-
? labeled citations; flag gaps for the crawl pipeline.
?? Step 5: sub-advisor ? Synthesize into a risk-disclosed conclusion with
? a full evidence chain and recommended actions.
?? Step 6: main (quality gate) ? verify U1?U6 + G1?G4, auto-fix, deliver.
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 · 176 lines · 2,313 tokens per session scan A 8a16d19a0fe8
retro-emulator-config-optimization-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/retro-emulator-config-optimization-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 2,313 tokens to every session, about $0.0116 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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