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Skillsllmfit

llmfit

Agents & models

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Updated 10-29 04:36

About this skill

llmfit operator

When to use this skill

Use this skill for source-grounded, node-local model-fit decisions: hardware inventory, fit verdicts, score/ranking output, hardware profiles, plan and storage estimates, and read-only REST queries. It keeps formula-based estimates separate from measured inference.

Route generic service or inference performance tuning to performance-optimization. This checkout has no separate lmstudio-cli or typesafe-ai skill; treat those names only as llmfit provider/API values and use the pinned reference when needed.

Instructions

  1. Run the offline gate first. From the repository root run python3 .agent-skills/llmfit/scripts/preflight.py --json; from an installed skill root run python3 "$SKILLS_ROOT/llmfit/scripts/preflight.py" --json. Add --repo PATH --expect-commit FULL_SHA only when checking a supplied source checkout. The helper never starts llmfit or invokes network/provider work.
  2. Establish binary and scope. Run llmfit --version and llmfit --help; if unavailable, stop at an install/source-build handoff. Do not run an installer, package manager, cargo build, container pull, or model download without explicit approval.
  3. Capture read-only hardware evidence. Run llmfit system --json; use llmfit doctor only for a diagnostic request. Load references/upstream.md for the pinned platform probe and capability matrix.
  4. Run a static fit query. Prefer llmfit recommend --json --limit N (optionally a supported --use-case) or llmfit fit --json -n N; use llmfit info "MODEL" for one row. Keep tok/s, prefill, and TTFT labeled as estimates unless output explicitly reports measured confidence.
  5. Apply the source thresholds. memory_required / memory_available gives Perfect at ≤0.60, Good at ≤0.85, Marginal at ≤0.98, and Too Tight above 0.98. recommended_ram_gb does not alter the verdict; offload paths cap at Good, and Too Tight is not a runnable claim. Consult the reference for estimation caveats.
  6. Compare capacity and plans consistently. Use global capacity overrides before fit, or a validated hardware profile for speed-relevant simulation. For a named model use llmfit plan "MODEL" --context 8192 --json, keeping quantization/context fixed when comparing plan and fit. Storage output is capacity arithmetic, not free-disk evidence.
  7. Keep API and side effects distinct. A loopback serve plus GET routes is read-only after an approved launch; /api/v1/plan is still an estimate. Downloads, TUI/provider pulls, llmfit bench, credentials/network, and --share PR creation require separate approval; use the pinned dry-run command for share payload preview.
  8. Return an evidence-backed conclusion. Include exact commands, host/profile/override inputs, model/quant/context, fit level/run mode, estimate basis/confidence, and whether a measured run occurred. Never report estimated throughput as a benchmark.

Examples

# Offline inventory before any upstream command
python3 .agent-skills/llmfit/scripts/preflight.py --json

# Read-only host inventory and top coding candidates
llmfit system --json
llmfit recommend --json --use-case coding --limit 5

# Capacity simulation and one reproducible model plan
llmfit --memory=24G --ram=64G --cpu-cores=16 fit --json -n 10
llmfit plan "Qwen/Qwen3-4B-MLX-4bit" --context 8192 --json

Best practices

  • Preserve raw JSON alongside summaries as the audit trail for detected hardware and estimate provenance.
  • Prefer validated profiles over capacity flags when memory bandwidth or fp16 throughput matters.
  • Treat Perfect/Good/Marginal as fit pressure, not quality or speed guarantees; report run mode and confidence.
  • Keep doctor for bug reports and API access loopback-only unless an approved design says otherwise.

References