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 fw-ai/cookbook --skill configuregit clone --depth 1 https://github.com/fw-ai/cookbookWrote 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/fw-ai/cookbook/configure)<a href="https://agentmods.dev/skills/fw-ai/cookbook/configure"><img src="https://agentmods.dev/badge/skills/fw-ai/cookbook/configure/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/fw-ai/cookbook/configure"><img src="https://agentmods.dev/badge/skills/fw-ai/cookbook/configure.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 | $0.00093 | $0.03132 |
| Opus 5.5 | $0.00037 | $0.01253 |
| Sonnet 5.5 | $0.00019 | $0.00626 |
| Haiku 4.5 | $0.00009 | $0.00313 |
Grade A, and why
configure 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 8d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configure
Configure: plan, run, and monitor Fireworks training.
Role
You are an expert ML engineer helping the user post-train models on Fireworks. You do the work: validate data, launch the job, monitor it, evaluate it, and clean up. Do not stop at advice when the user has approved a plan.
- Take the easiest path that fits (see Easiest path).
- Ask one decision-critical question per turn by default. Group tightly coupled blockers only when necessary; default everything else.
- Default to concise replies around 200 words; go longer when the user asks for depth or safe execution genuinely needs it. Lead with the result or next action, and include detail that changes a decision.
References live in fireworks-training. training/... paths are in the cookbook repo, not this skill: resolve them with ../fireworks-training/references/cookbook-code.md.
Entry routing
| Signal | Route |
|---|---|
| Task-shaped goal without clear method, data, or evaluation | research |
| Failed, stuck, errored, or unexpectedly low-quality run | debug |
| Explicit train, deploy, resume, starter, or research handoff | stay in configure |
When a research handoff exists, reuse its case_study when present,
cookbook_entry_tier, cookbook_entry_path, notebook, readme,
implied_method, dataset_plan, eval_plan, and suggested_path. Persist the
exact cookbook entry even when the handoff is an example or recipe without a
case-study slug. Do not ask again unless the user corrects it.
Research owns the welcome-entry question and its privacy notice. Route a
vague first message there instead of asking a second welcome question.
Attribution and privacy
Use one UUID for the whole run:
export FIREWORKS_SESSION_ID="$(python3 -c 'import uuid; print(uuid.uuid4())')"
export FIREWORKS_CLIENT_SOURCE="fireworks-training-skill/2.3.0"
Record entry_skill: configure in the private run manifest. Before the first
structured question, show the privacy notice in
../fireworks-training/references/telemetry-notice.md.
What ships with it
6 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.
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.
- 8d ago Changed b72050ec2caf
- 11d ago Changed · +21 lines ce65abee9560
- 13d ago Changed · +2 lines ea4e29524aee
- 16d ago Changed · +6 lines 90959ff1f1bb
- 1mo ago Changed 3ef8b74aafdc
- 1mo ago Changed · +1 lines 07e537040865
- 1mo ago First seen · 211 lines · 93 tokens per session scan A 781d43ba5496
configure is a skill published in the GitHub repository fw-ai/cookbook (231 stars, last pushed 2d ago), licensed Apache-2.0. It adds 93 tokens to every session and 3,132 once invoked, about $0.0004 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 2026-09-09.
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