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The weekly judgment pass over benchlab's benchmark dataset.
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Basé sur la classification professionnelle SOC
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
Operating and troubleshooting hal0-managed user-level systemd services. Use when a hal0 platform service (hermes-gateway, hal0-agent@*, etc.) is misbehaving — missing env vars, not starting, not connecting to Telegram/Discord, etc. Encodes the structural fact that hal0 provisioners write secrets files but do NOT wire service units to load them.
Creating ROCmFPX-family quants (ROCmFP3/4/6/8, straight or agent) from BF16/F16 GGUF sources on hal0 / Strix Halo, using the Hal0ai/Hal0_ROCmFPX runner. Use when asked to quantize a model to ROCmFP4 (or FP3/FP6/FP8), build the ROCmFPX llama.cpp tree for a GPU arch, or produce an agent-coherent quant for tool-calling / JSON / coding workloads. Encodes the clone→build→quantize contract and the straight-vs-agent tensor-routing choice.
Running LLM inference benchmarks on hal0 / Strix Halo. Use when asked to benchmark a model, measure tokens/sec (prompt-processing or generation), compare the ROCm vs Vulkan backends, sweep context lengths, or refresh benchmark data. Encodes the structural fact that benchmarking is a rootful GPU op the unprivileged agent reaches ONLY through the hal0-benchctl sudo seam, and that the single iGPU is shared with the live inference slots.
Tuning LLM inference settings on hal0 / Strix Halo for throughput, latency, or memory. Use when asked to optimize/tune a model's llama.cpp flags & values (batch, ubatch, ngl, flash-attn, KV-cache quant, threads), find the fastest config, or close a gap vs other people's numbers. Combines benchmark-driven sweeps (via hal0-bench) with external research (r/LocalLLaMA, llama.cpp GitHub issues/PRs, the kyuz0 toolbox repo). Core rule: never apply a community claim without measuring it locally first.
| name | hal0-bench-autopilot |
| category | homelab-ops |
| description | The weekly judgment pass over benchlab's benchmark dataset. |
The weekly judgment pass, run by Hermes after the timer session (or invoked manually). Procedure encoded in SKILL.md:
hal0 bench status — confirm last session outcome; for failed cells read
the artifact log, classify (OOM → mark model/depth combination as
excluded in the suite override; hang → retry once; else file board task).hal0 bench publish --check → if changed, regenerate, update the "what
the data shows" prose ONLY if a headline fact changed (pre-registered
claims live in the mdx; the skill edits them with the same
accept/reject discipline as the runbook §2), open the site-repo PR.reps to make
budget — drop whole cells instead (planner already orders by value).# Run the autopilot pass
sudo -n /usr/lib/hal0/bin/hal0-benchctl gpu-quiesce start
hal0 bench plan --json > /tmp/bench-worklist.json
hal0 bench run --no-exclusive --dry-run # dry-run first
hal0 bench run --no-exclusive # actual run
hal0 bench status
hal0 bench publish
sudo -n /usr/lib/hal0/bin/hal0-benchctl gpu-quiesce end
# Check for regressions
python3 /usr/lib/hal0/bench/regression.py
A short report: