Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add llm-d-incubation/llm-d-skills --skill llm-d-autoconfiggit clone --depth 1 https://github.com/llm-d-incubation/llm-d-skillsWrote 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/skills/llm-d-incubation/llm-d-skills/llm-d-autoconfig)<a href="https://agentmods.dev/skills/llm-d-incubation/llm-d-skills/llm-d-autoconfig"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/llm-d-autoconfig/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/skills/llm-d-incubation/llm-d-skills/llm-d-autoconfig"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/llm-d-autoconfig.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 | $0.00075 | $0.03548 |
| Opus 5.5 | $0.00030 | $0.01419 |
| Sonnet 5.5 | $0.00015 | $0.00710 |
| Haiku 4.5 | $0.00007 | $0.00355 |
Grade A, and why
llm-d-autoconfig scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| 1 | Cluster discovery (read-only kubectl/curl) | [references/phase-1-cluster-discovery.md](references/phase-1-cluster-discovery.md) | How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
llm-d-autoconfig
You help the user configure (and optionally deploy) an EndpointPickerConfig for their llm-d inference deployment.
This skill wraps a deterministic recommender script (autoconfig). Your job is to gather workload information conversationally, hand it to the script in a structured form, narrate the output with its evidence tiers, and — if the user wants — apply the result to their cluster.
Relationship to the single-purpose skills
This repo also ships focused skills — deploy-llm-d, run-llm-d-benchmark, compare-llm-d-configurations, configure-wva-autoscaling-llm-d, teardown-llm-d. This skill owns config generation and the clone-free deterministic bundle; the single-purpose skills own standalone operations on a config the user already has.
- Stay in this skill when the user is deciding what config to run for a workload/SLA, then deploying/benchmarking the bundle it renders. Phases 6–7 run the bundle-native, URL-only (no repo clone) deploy + deterministic benchmark — that path is autoconfig's and is not duplicated elsewhere.
- Hand off to a single-purpose skill when the user's ask is a single, well-scoped operation on an existing/hand-written setup rather than an autoconfig bundle: general guide-driven deploy →
deploy-llm-d;llmdbenchmark-CLI benchmarking (guide or custom workload profiles) →run-llm-d-benchmark; config comparison →compare-llm-d-configurations; WVA/HPA autoscaling →configure-wva-autoscaling-llm-d; teardown →teardown-llm-d. Phases 6 and 7 link to these for the general flows. - Troubleshooting is shared, not duplicated: generic Kubernetes/deploy failures live in
deploy-llm-d's troubleshooting guide;references/pitfalls.mdpoints there and keeps only the autoconfig-specific entries (EPP config, gateway-mode routing, PD/NIXL, modelserver install, benchmark harness).
How this skill is organized
This SKILL.md is the entry point — read it first, then load references/phase-N-*.md for the phase you're working in. Each phase reference is self-contained; you do NOT need to keep prior-phase references in context once a phase is done.
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- feature_docs.yaml 32 KB
- references/phase-1-cluster-discovery.md 17 KB
- references/phase-2-5-doc-driven-synthesis.md 13 KB
- references/phase-2-discovery-questionnaire.md 33 KB
- references/phase-3-recap.md 5.1 KB
- references/phase-4-call-script.md 15 KB
- references/phase-5-present-recommendation.md 5.9 KB
- references/phase-6-deploy.md 54 KB
- references/phase-7-benchmark.md 14 KB
- references/pitfalls.md 12 KB
- scripts/autoconfig_poc.py 103 KB runs code
- scripts/benchmark.py 28 KB runs code
- scripts/doc_cache.py 14 KB runs code
- scripts/parse_bench_results.py 13 KB runs code
- scripts/verify_doc_map.py 7.7 KB runs code
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 · 110 lines · 75 tokens per session scan A 2bef7b7a55fc
llm-d-autoconfig is a skill published in the GitHub repository llm-d-incubation/llm-d-skills (7 stars, last pushed 8d ago), licensed Apache-2.0. It adds 75 tokens to every session and 3,548 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
llm-d-autoconfig compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| llm-d-autoconfigllm-d-incubation/llm-d-skills | Skill | 7 | 8d ago | Apache-2.0 | A | 75 | Claude Code, Codex |
| tensorrt-llmNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 18 | Claude Code, Codex |
| agent-platform-tuninggoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 64 | Claude Code, Codex |
| google-cloud-solution-guided-gke-ai-migrationgoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 157 | Claude Code, Codex |
| apigee-agent-gateway-governancegoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 222 | Claude Code, Codex |
| modalK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 65 | Claude Code, Codex |
| gke-inferencegoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 119 | Claude Code, Codex |