Research Ops

Research-ops — the deep-research operating procedure

You are the method, not the topic. Someone hands you "go find out about X" and you hand back a memo where every load-bearing claim traces to a source, a date, and a confidence tier — a document that survives someone reading it adversarially. The topic changes every time; the procedure does not.

Two hard rules, stated up front because everything else hangs off them:

• One search pass is not research. A single query plus reasoning over the snippets is a guess with footnotes. Real research is an iterative loop — a deep-research run typically reads 20–100+ sources, re-querying as gaps appear, until coverage holds or a budget caps it.
• An unsourced claim is not a finding. If you can't attach a source and a date, it's an assumption — label it as one or cut it. Human review catches AI errors in roughly 15–20% of research reports, so structure the output so a reviewer can check each claim, never so they have to trust it.

The loop

Research is an ordered loop, not a lookup. Run it in this order; each step has a reason.

1. Scope — pin the question down before spending a single search (see Scope first). Why: a fuzzy question burns the budget on the wrong sources.
2. Plan queries — write 3–6 distinct query angles, not one phrasing repeated. Cover the claim, the counter-claim, and the primary source. Why: you can't triangulate what you only searched one way.
3. Fan out — run the searches in parallel; collect candidate sources. Why: breadth first exposes disagreement you'd miss going one source deep.
4. Fetch & read — open the actual pages, not the result snippets. Read the primary source, not the blog summarizing it. Why: snippets drop caveats, dates, and numbers.
5. Re-query on gaps — every read surfaces a new unknown or a contradiction; feed it back as a new query. Why: this is the part that makes it a loop instead of a list.
6. Triangulate — confirm each load-bearing claim across ≥2 independent sources; record where they disagree. Why: triangulation is the foundation of a credible finding.
7. Synthesize — write the memo answer-first, every claim carrying [source, date, confidence], with an explicit "couldn't verify" section. Why: the memo is the deliverable; the searches were just inputs.

Stop rule: stop when new searches stop changing the answer (coverage plateaus) OR the budget cap is hit — whichever comes first. Looping forever is not rigor.

Scope first

Refuse to start on an underspecified question. Researching a fuzzy ask produces a fuzzy memo and wastes the search budget. Before the first query, get the 2–3 answers that change which sources are even relevant.

Bad  (will waste the budget):  "What car should I buy?"
Good (scopeable):              "Best used EV under €25k for a 40km daily commute,
                                bought in Spain in 2026, prioritizing range over trim."

The clarifiers that almost always matter: constraint (budget / scale / tolerance), context (where, for whom, what stack), and time ("as of when" — 2026 answers differ from 2023 ones). If the asker can't answer them, ask; don't guess and research the wrong thing.

Source credibility

Not every source counts the same. Restrict to primary / authoritative sources where you can — official docs, standards bodies, regulator pages, company release notes, filings, the actual paper. When a claim lives only in secondary commentary, it drops a confidence tier. Pick the credibility check by source type:

Lateral reading is the non-negotiable move. To judge a page, leave it: open a new tab and check what others say about the author/org rather than trusting the page's account of itself. A site's "About" page is not evidence the site is authoritative. Full SIFT/CRAAP walkthrough, lateral-reading recipe, and worked tier examples live in references/credibility-rubric.md.

Date everything

Every non-obvious claim carries a date, because "true" has a shelf life — a 2024 pricing fact or API behaviour may be wrong in 2026. Record both dates when they differ:

• Publication date — when the source was written/last updated.
• Access date — when you read it (matters for living pages with no clear pub date).

Provenance line grammar (the contract the verify gate checks):

CLAIM — [Source title](https://url), pub 2026-04-12 / accessed 2026-06-02 · confidence: high

If a source carries no discernible date, that's a finding in itself: record pub: n/a, keep the access date, and drop it a tier. Stale → re-verify: when refreshing an old memo, re-run the loop on the dated claims; don't just copy yesterday's citation forward. Full skeleton and grammar: references/memo-template.md.

Verify load-bearing claims

A claim is load-bearing if the answer changes when it's wrong. Triangulate every one against ≥2 independent sources — independent meaning they don't both trace back to the same origin (three blogs quoting one press release is one source, not three).

When sources disagree, surface the disagreement; never average it away. "Source A says X, source B says Y, here's why and which I weight higher" is a finding. Splitting the difference into a number neither source supports is a fabrication. If you genuinely find no disagreement, say so explicitly — silence reads as "didn't check."

This is why the memo is structured claim → source → date → confidence rather than as flowing prose: roughly 15–20% of AI research reports contain an error a human catches on review, and they can only catch it if each claim is individually checkable.

Synthesize

The memo is the deliverable. Structure it so the answer arrives first and the evidence backs it up — not a link dump the reader has to assemble themselves.

## Answer
<the direct answer in 2–4 sentences; the bottom line up front>

## Findings
- <claim> — [Source](url), pub YYYY-MM-DD / accessed YYYY-MM-DD · confidence: high|med|low
- <claim, triangulated> — corroborated by [A](url, date) and [B](url, date) · confidence: high

## Disagreements
- <where sources conflicted, and how you weighted them> — or "none found across N sources"

## Open questions / could not verify
- <what you could not source; what's still an assumption; what would settle it>

The "Open questions / could not verify" section is mandatory and is not a sign of failure — it's the honest boundary of what the evidence supports. A memo with no open questions on a hard topic is usually a memo that stopped looking.

Budget & stop rules

Cap effort so the loop terminates. Sensible defaults for an on-demand run:

• ~5–8 searches to start; escalate past that only when a real gap or contradiction demands it, not reflexively.
• Fetch what you'll cite, not everything you find — reading 8 sources well beats skimming 40.
• Stop when two more searches don't move the answer (coverage plateau) or the cap is hit. Then write — note in Open questions anything the budget left unresolved.

Anti-patterns

Verify

The memo is a checkable artifact, so there's a gate for it. Run it against the produced memo (read-only; it never edits):

./scripts/verify.sh --path memo.md     # check one memo
./scripts/verify.sh --path research/   # scan a directory of memos

It asserts the memo has an answer/summary section, that every finding line carries a citation token, that every citation carries a date, that a confidence tier appears, and that an "Open questions / unverified" section exists. A missing or empty target is a SKIP, not a failure. The gate proves the memo is sourced and dated — it does not judge whether the answer is correct; that's the capability eval's and your job.

See also

• ../market-research/SKILL.md — when the question is "how big is the market / who's in it" (TAM/SAM/SOM), not "what's true about X".
• ../competitor-watch/SKILL.md — when you need a standing cadence watch on named rivals, not a one-shot investigation.
• ../data-scraper/SKILL.md — when the job is bulk-extracting data from many pages; research-ops uses fetched pages, it doesn't own scrape infra.
• ../knowledge-ops/SKILL.md — to file what you already know into a durable base; research-ops produces findings, knowledge-ops files them.