Borrowing it
Nothing to install: this file belongs to dungnotnull/gaming-device-power-management-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/gaming-device-power-management-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/gaming-device-power-management-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/gaming-device-power-management-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/gaming-device-power-management-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/gaming-device-power-management-agent-skill/claude-md/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/instructions/dungnotnull/gaming-device-power-management-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/gaming-device-power-management-agent-skill/claude-md.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 | $0.01765 | $0.01765 |
| Opus 5 | $0.00882 | $0.00882 |
| Sonnet 5 | $0.00353 | $0.00353 |
| Haiku 4.5 | $0.00177 | $0.00177 |
Grade A, and why
gaming-device-power-management-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 9d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Skill 261: gaming-device-power-management
Skill Identity
- Skill Name:
gaming-device-power-management - Tagline: Power Management Optimization for Gaming Devices — Gaming Device Power & Battery Optimization analysis & decision-support harness.
- Current Phase: Phase 6 — v2 Modular Architecture Upgrade (PRODUCTION READY v2.0.0)
- Folder:
D:\972026\261-gaming-device-power-management\
v2 Architecture (production runtime)
The skill ships an importable gdpm package (src/ layout) implementing a modular
agent harness. The markdown skills in skills/ remain the human/agent-facing
definitions; the package is the executable, validated, production runtime.
| Module | Responsibility |
|---|---|
gdpm.registry.SkillRegistry |
Load/resolve/validate skills from skills/*.md |
gdpm.router.Router |
Chain-of-thought routing over the 6-step pipeline |
gdpm.tools.ToolRegistry |
10 JSON-schema-validated, executable tools |
gdpm.hooks.HookBus |
Lifecycle hooks (logging, limitations, state sync, events) |
gdpm.agents |
5 sub-agents with LLM-path + deterministic-local fallback |
gdpm.orchestrator.AgentOrchestrator |
Drives pipeline + quality gates + degradation |
gdpm.settings |
Type-safe TOML config (/config/*.toml) + env overrides |
gdpm.context.ContextWindow |
Token-budget management with compaction |
CLI: gdpm run|validate|list|manifest|crawl. Docs: SKILL.md. Config: /config/*.toml.
References: /references (prompt templates + domain knowledge). Assets: /assets (schemas + diagram).
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Gaming Device Power & Battery Optimization. It gathers authoritative real-time and reference data, applies recognized domain methods (drain profiling, perf/power tradeoff, VRR/display, thermal/charging), 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.
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.
- 9d ago First seen · 152 lines · 1,765 tokens per session scan A a96fd40fac06
gaming-device-power-management-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/gaming-device-power-management-agent-skill (5 stars, last pushed 1mo ago), licensed MIT. It adds 1,765 tokens to every session, about $0.0088 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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