Borrowing it
Nothing to install: this file belongs to dungnotnull/game-highlight-video-compression-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-highlight-video-compression-agent-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/dungnotnull/game-highlight-video-compression-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-highlight-video-compression-agent-skill/claude-md)<a href="https://agentmods.dev/instructions/dungnotnull/game-highlight-video-compression-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/game-highlight-video-compression-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/game-highlight-video-compression-agent-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dungnotnull/game-highlight-video-compression-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 | $0.01604 | $0.01604 |
| Opus 5.5 | $0.00642 | $0.00642 |
| Sonnet 5.5 | $0.00321 | $0.00321 |
| Haiku 4.5 | $0.00160 | $0.00160 |
Grade A, and why
game-highlight-video-compression-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 2mo 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md - Skill 267: game-highlight-video-compression
Skill Identity
- Skill Name:
game-highlight-video-compression - Tagline: Compression Optimization for Game Highlight Videos - Game-Video Highlight Encoding & Compression evidence-backed analysis harness.
- Version: 2.0.0
- Current Phase: Phase 5 - Integration & Polish (PRODUCTION READY v2.0.0)
- Folder:
D:\972026\267-game-highlight-video-compression\
Problem This Skill Solves
This skill provides a structured, evidence-backed analytical workflow for Game-Video Highlight Encoding & Compression. It routes a request through a chain-of-thought router, gathers authoritative real-time and reference data, applies recognized domain methods (per-scene encoding, codec/rate-control choice, VMAF/SSIM validation), 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.
A deterministic tooling layer (config/, hooks/, tools/, scripts/)
grounds every LLM step in reproducible computation and provides an LLM-free
fallback orchestrator.
Harness Flow Summary
/game-highlight-video-compression invoked
|
Pre-Flight: config (config/) + language detection + logging (hooks/)
|
+-- Step 0: sub-router ........... classify query -> path + sub-skills (classify_query tool)
+-- Step 1: sub-gather-requirements structured requirements
+-- Step 2: sub-evidence-collector ... evidence bundle (probe_clip, fetch_evidence)
+-- Step 3: sub-core-analysis ....... codec/per-scene/profile (compute_bitrate_budget, validate_quality)
+-- Step 4: sub-knowledge-updater ... academic evidence + gaps (lookup_knowledge)
+-- Step 5: sub-advisor ............. risk-disclosed conclusion + scenarios
+-- Step 6: sub-quality-gate ........ enforce U1-U6 + G1-G4, auto-fix, limitations
|
Post-Flight: hooks (harness.complete) + structured event flush
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.
- 2mo ago First seen · 153 lines · 1,604 tokens per session scan A 87e4fb9c00cc
game-highlight-video-compression-agent-skill CLAUDE.md is an instructions file published in the GitHub repository dungnotnull/game-highlight-video-compression-agent-skill (5 stars, last pushed 3mo ago), licensed MIT. It adds 1,604 tokens to every session, about $0.0064 per session on Opus 5.5. 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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