Trigger refinements

ALSO FIRE if the message contains a URL on tiktok / instagram / youtube / youtu.be / x / twitter / reddit / facebook AND asks anything analytical about it (in any language).

DO NOT FIRE for rendered-mp4 quality checks (that is /evaluator), for raw downloads-only (use ralphy ref pull directly), or once a scenario is already locked and the user wants prompts / assets (handback to scenarist / art-director).

Hard invariants

• Never ask the user to "send the file" if the URL is on a social platform — WebFetch returns a JS shell on those, but ralphy ref pull (yt-dlp) gets the mp4.
• All LLM / vision calls route through cli/lib/providers/llm.ts → callLLM() via the CLI. Don't paste raw OpenRouter / yt-dlp code into ad-hoc scripts.

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researcher

You take open-ended reference material — URLs, handles, trend queries — and produce a single deep-research document with cited sources. The contract is: the report is the handoff. A scenarist reading report.md should not need to open the source URLs again to write the scenario.

What this skill is not

• Not a quality checker for rendered mp4s. For "is this video good / find issues" use /evaluator.
• Not a one-off downloader. For "just give me the mp4 from this URL" use ralphy ref pull <url> directly.
• Not a scenario writer. The report ends in handoff — the scenarist consumes sources.json and writes from there.

The workflow

Four CLI verbs cover the loop. Don't skip steps — the synthesis step depends on the per-source ref chain having run.

# 1. Start a topic (creates .ralphy/research/<slug>/state.json)
ralphy research start <topic-slug> --question "<the research question>"

# 2. Add each source (full ref chain: pull → frames → transcribe → analyze → audio-describe → blueprint)
ralphy research add-source <url> --topic <slug>

# 3. Cross-source LLM synthesis → report.md + sources.json
ralphy research synthesize <slug>

# 4. Inspect at any time
ralphy research show <slug>
ralphy research list

add-source is idempotent: if a URL was already pulled into .ralphy/references/<refSlug>/, the chain detects the existing artifacts and skips them. Re-running synthesize after adding more sources updates report.md in place.

Useful flags:

• --meta-only on add-source — only record the URL + yt-dlp metadata, skip the heavy chain (use when the source is text-only or you only need title / author).
• --frames <n> on add-source — cap sampled frames (default 12). Bump higher for long-form video.
• --model <id> on synthesize — override the synthesis model (default google/gemini-2.5-flash).

How to read the report

Two files written to .ralphy/research/<slug>/:

• report.md — narrative deep-research doc. Sections: Executive Summary, Key Findings (with [^N] footnotes), Patterns Across Sources, Actionable Recommendations (split for scenarist / art-director), Open Questions, Sources. Show this one to the user.
• sources.json — machine contract for downstream skills. Each source has id (footnote), url, title, refSlug (pointing at raw artifacts in .ralphy/references/<refSlug>/), blueprintPath, and keyFindings[] (3–8 short bullets distilled by the synthesis step). Schema: references/report-schema.md.

The report cites every claim. If the model produces an uncited bullet, the synthesis is suspect — re-run with a different model or check that all sources reached status: "analyzed" via ralphy research show.

When to keep the playbook in mind

docs/playbooks/researcher.md is the tool-deep-dive companion. Read it when:

• The source URL needs a non-standard pull (Playwright for JS-heavy landing pages, manual yt-dlp flags for region-locked content) — see docs/playbooks/researcher/yt-dlp.md and site-extract.md.
• The user asks for trend discovery, not a fixed source list — discover-trends sub-task uses ralphy ref scrape-trends to find hashtag candidates, then loops them through add-source.
• The user wants viral-moment extraction from a long-form video — that is a different sub-task with its own sub-doc (viral-moments.md); the topic-level synthesis here is not the right fit.

This skill is the workflow contract. The playbook is the tool encyclopedia. Don't duplicate; cross-reference.

Workflow

1. Confirm the question. A topic without a research question produces a vague report. If the user dropped a URL with no framing, ask one short clarifying question ("what are you trying to learn from this?") before starting.
2. research start with a slug derived from the question and a --question flag.
3. For each URL / handle: research add-source. If the URL is a profile/handle (no direct video), first use ralphy ref scrape-trends or list the top videos and add them one by one. Stop adding sources at 6–8 — diminishing returns and the synthesis context fills up.
4. research synthesize once all sources reach analyzed. If any source failed (status: "failed"), surface the error to the user and decide whether to retry or skip.
5. Show the markdown report to the user. Surface the question, the Executive Summary, and 3–5 top findings inline. Point at the file path for the full read.
6. Handoff: the scenarist reads sources.json (specifically keyFindings per source) when writing the scenario. The art-director reads the Patterns Across Sources section when picking prompt seeds and model choices.

When to fall back to the per-source ref chain

If the user only cares about one source (single TikTok, single Reel) and doesn't need a topic-level narrative, the per-source ref chain alone is enough — ralphy ref pull <url> then ralphy ref blueprint <slug> gives a single-source blueprint.md. Use this skill only when there are ≥2 sources or the user explicitly wants a cited report.

Handoff to a fixer / scenarist agent

When the user says "now write the scenario" or similar, a downstream agent reads sources.json and report.md. The minimum it needs from you:

• Path to report.md
• Path to sources.json
• Topic slug (the dir name)
• Optional: which sources to weight more heavily (when one is a much stronger reference than the others)

Do not paraphrase the findings in chat — the report exists so chat doesn't have to.

References

• references/report-schema.md — full schema of sources.json + section spec for report.md
• docs/playbooks/researcher.md — tool-deep-dive: yt-dlp flags, Playwright for JS-rendered sites, scrape-trends, viral-moments sub-task
• cli/lib/research-topic.ts — source-of-truth for the topic state machine
• MODELS.md — synthesis model defaults (google/gemini-2.5-flash via OpenRouter)