Research

Research: explore method, data, evaluation, and cookbook starting points.

Help the customer understand what to train, on what data, and how to measure it before they spend GPU. This skill is the interview-driven front door (like Tinker research). Execution lives in configure; failures live in debug.

What research finds (and what it does not)

Research answers	Configure answers (later)
Implied method (SFT, DPO, RFT, embedding) from supervision signal	Exact method confirmation + hyperparameters
Cookbook entry — case study, example, or recipe path	Workflow surface (firectl vs SDK vs Training API)
Dataset plan — local, bundled, HF candidates, labeling schema	Upload, column mapping, job create
Eval plan — metric class, baseline, cookbook eval hook	Run baseline eval, wire evaluators
Suggested path (managed, serverless, dedicated) — coarse	Cost, model choice, monitor, deploy

Research does not pick a final base model, estimate cost, or create jobs. If the user already named model + JSONL + method, skip research and go to configure.

Order: research → configure → (debug if needed).

Research methodology

Follow references/methodology.md. In short:

1. Inspect the cookbook first — read references/cookbook-catalog.md and open the closest README before asking questions the catalog can answer.
2. Interview one question at a time — references/interview-questions.md; propose options grounded in what you read; let the user revise.
3. Eval before train — every readiness package includes how success will be measured and whether a baseline run is required.
4. Propose, then approve — present one recommendation; user can ask for alternatives or stay in research for labeling/data help.
5. Hand off only when ready — configure starts after explicit handoff choice.

What this skill does

1. Show the Research skill banner every turn.
2. Scan the full cookbook catalog (not only case studies).
3. Run the interview — one AskQuestion per turn until the completion gate in references/interview-questions.md passes.
4. Optional public dataset search after the user approves external search.
5. Write a readiness package to the run manifest and hand off to configure via AskQuestion.

What this skill does not do

• Create training jobs, upload datasets, or run mutating firectl.
• Choose hyperparameters or final model (configure owns that).
• Debug failed runs (use debug).

Workflow

0. Attribution and privacy

export FIREWORKS_SESSION_ID="$(python3 -c 'import uuid; print(uuid.uuid4())')"
export FIREWORKS_CLIENT_SOURCE="fireworks-training-skill/2.2.0"

Record entry_skill: research when writing a run manifest.

Before the first structured question, show the privacy notice in ../fireworks-training/references/telemetry-notice.md.

Check firectl skill-journey record --help once. When available, record each registered question and option ID through ../fireworks-training/references/telemetry.md. Store the event UUID and timestamp in the private run manifest before sending. If the command is unavailable or the user opts out, continue with local run state only.

Record milestones for recommendation, handoff, and terminal research_only. Never send raw customer prose, datasets, credentials, or paths.

If firectl whoami fails, tell the user to set a scoped API key with hidden terminal input. Never ask for it in chat.

1. First turn

For a vague first message, show the privacy notice, then offer Research, Configure, and Debug in the welcome-entry AskQuestion. Ask one question, then stop. Record the registered welcome option after the answer.

Continue directly when the user clearly asks to explore a task, method, dataset, evaluation, or example. The privacy notice still precedes the first research interview question.

2. Skill banner

First line of every response (see references/output-template.md):

**Research** — exploring method, data, eval, and cookbook starting points.

3. Inspect, then interview

Read references/cookbook-catalog.md. Open the best candidate README or example README. Run references/interview-questions.md — one AskQuestion per turn. STOP and wait. Do not recommend until the completion gate passes.

4. Present the readiness package

Use references/output-template.md:

• Best cookbook entry (tier + path) and why.
• Dataset plan (local / bundled / HF / labeling).
• Eval plan (metric, baseline, notebook eval hook or gap).
• Runner-up only when genuinely close.

5. Hand off to configure

Fire the Handoff AskQuestion. On plan_configure, write the handoff block from references/cookbook-catalog.md into fireworks-training-runs/<run-id>/run.md, then continue in configure.

Never tell the user to paste a canned configure prompt.

Progressive references

Need	Reference
Full cookbook index	references/cookbook-catalog.md
Interview scripts and completion gate	references/interview-questions.md
Methodology (inspect → interview → eval)	references/methodology.md
Turn shapes and readiness package	references/output-template.md
Case study slugs (subset)	references/case-studies.md
Telemetry and privacy	../fireworks-training/references/telemetry.md

Cross-skill routing

Signal	Route to
User wants to train, deploy, or resume with known plan	configure
User reports failure, stuck job, or error	debug
User still exploring data, eval, or cookbook	stay in research