Agent skill · Code Review & Quality

quic-channel-grading

QUIC channel quality grading with BBRv3 congestion control analysis. Classifies network paths into GF(3) tiers based on RTT, bandwidth, loss, and pacing efficiency. Integrates with Iroh P2P and world-letter cross-predictions.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill quic-channel-grading-plurigrid-asi-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 16 KB
Bundled scripts: none
Version: 1.0.0
Path: skills/analysis/quic-channel-grading-plurigrid-asi-2/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# QUIC Channel Grading **GF(3)-classified network path quality assessment with BBRv3 congestion control.** ## Overview QUIC Channel Grading assigns quality tiers to network channels using: - **RTT measurements** (round-trip time) - **Bandwidth estimation** (bottleneck bandwidth) - **Loss rate** (packet loss percentage) - **Pacing efficiency** (burst vs smooth delivery) - **Jitter** (RTT variance) ## GF(3) Channel Tiers | Tier | Trit | Quality | RTT | BW | Loss | Use Case | |------|------|---------|-----|-----|------|----------| | **PLUS** | +1 | Excellent | <20ms | >100Mbps | <0.1% | Real-time, video | | **ERGODIC** | 0 | Standard | 20-100ms | 10-100Mbps | 0.1-1% | General, sync | | **MINUS** | -1 | Degraded | >100ms | <10Mbps | >1% | Batch, async | ### Conservation Law ``` Channel assignments across triads: Σ trits ≡ 0 (mod 3) ``` When grading 3 channels simultaneously, ensure balance: - 1 PLUS + 1 ERGODIC + 1 MINUS = 0 (balanced) - 3 ERGODIC = 0 (all neutral) ## Architecture ``` ┌─────────────────────────────────────────────────────────────────────┐ │ QUIC CHANNEL GRADING SYSTEM │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ ┌─────

What's inside
Steps it walks through
  1. Overview
  2. GF(3) Channel Tiers
  3. Conservation Law
  4. Architecture
  5. BBRv3 Congestion Control
  6. State Machine
  7. Key Improvements (v3 over v2)
  8. Pacing Rate Calculation
  9. Channel Grading Algorithm
  10. Metrics Collection
  11. Hysteresis Decay
  12. QUIC Implementation
  13. Quinn (Rust) Integration
  14. Iroh Integration
Ships with 1 file
  • metadata.json
Commands it runs
Grade a channel (probe and measure)
bb quic-channel-grade.clj probe <endpoint>
Grade all world-to-world channels
bb quic-channel-grade.clj grade-worlds
Check GF(3) conservation
bb quic-channel-grade.clj verify
Export grades to DuckDB
bb quic-channel-grade.clj export --db channels.duckdb
Visualize channel lattice
bb quic-channel-grade.clj visualize
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About this skill
What does the quic-channel-grading skill do?

QUIC channel quality grading with BBRv3 congestion control analysis. Classifies network paths into GF(3) tiers based on RTT, bandwidth, loss, and pacing efficiency. Integrates with Iroh P2P and world-letter cross-predictions.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill quic-channel-grading-plurigrid-asi-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going