# Secondary Research

> [discovery-phase pack · evidence] Synthesizes published industry reports, benchmarks, regulatory filings, academic research, and analogous-domain case studies to substitute or augment direct evidence. Especially for engagements where user_access is none. Produces secondary-research.md. Reads discovery-context.md.

- Skill: `stanislavnianko/secondary-research` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add stanislavnianko/secondary-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/stanislavnianko/secondary-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: stanislavnianko (https://skillmd.com/u/stanislavnianko)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/stanislavnianko/secondary-research

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# Secondary Research

> Part of the **discovery-phase** skill pack · `evidence` group · reads `discovery-context.md` (run `profile-builder` first if missing).

Pull from published sources when primary research is constrained or unavailable. Standard tool for pre-sale discovery (no time/budget for primary), regulated domains (interview restrictions), and analogous-domain framing.

## Step 1 — Read discovery context

Read `discovery-context.md` (sections **1. Client → Domain**, **2. Product / Initiative**, **6. Constraints**) and `problem-canvas.md` if it exists.

If `discovery-context.md` is missing, ask the BA inline: "(a) client domain / sector; (b) any regulatory constraints (GDPR / HIPAA / SOC2 / none)?" — tag the output `[ASSUMED DOMAIN]`. Never block; recommend `profile-builder` for high-stakes work.

## Step 2 — Decide research scope

| Scope | Signal |
|---|---|
| **Industry baseline** | "We need to know what 'normal' looks like" |
| **Best practice / state-of-the-art** | "What have leading orgs done about this?" |
| **Regulatory / compliance** | "What's required vs forbidden?" |
| **Analogous domain** | "No data in our domain — can we borrow from `<adjacent>`?" |
| **Sizing / TAM** | "Is the opportunity even commercially worth it?" |

A good secondary-research run usually picks 2-3 of these.

## Step 3 — Delegate to web-research skills if available

If `deep-research` / `exa-search` / `market-research` are installed:
> "Use `deep-research` for `<scope>` in `<client domain>`. Surface: `<3-5 sub-questions>`. Output cited."

Without those, fall back to: industry analyst reports (Gartner, Forrester, McKinsey, BCG public excerpts), regulatory body publications (FDA, GDPR-EU, FCA, etc.), academic search (Google Scholar, arXiv), vendor whitepapers (treat with skepticism), conference proceedings, public earnings call transcripts (surprisingly rich for sizing).

## Step 4 — Capture findings with provenance

Per finding, capture:
- **Claim** — the specific assertion
- **Source** — URL, title, author, date
- **Source quality** — primary research / analyst report / vendor blog / forum / academic
- **Recency** — within 12 months / 1-3 years / older
- **Implication for our hypothesis** — supports / contradicts / orthogonal

## Step 5 — Confidence and gap statement

End the doc with two short sections:

- **What primary research would still be needed** — even after secondary, what's a desk research can't tell us
- **Confidence on each problem-canvas claim** — high / medium / low, with reasoning

## Step 6 — Analogous domain caveat

If using analogous domain (e.g., "no data on healthcare CRM, borrowing from financial-services CRM"): explicitly state where the analogy holds and where it breaks. Analogies smuggle wrong assumptions if not bounded.

## Output

`./discovery/secondary-research.md` per `./template.md`.

Append to `_log.md`: `[secondary-research | YYYY-MM-DD] scopes: <list>; sources: <count>; supports: <N>; contradicts: <N>; orthogonal: <N>`.

## Anti-patterns

- **Vendor blog as primary source.** Treat as marketing, not data. Use only as "what they want you to think".
- **Old reports.** A 2020 SaaS benchmark is mostly irrelevant in 2026. Tag recency, prefer <18 months.
- **Stacking weak sources to fake strength.** Five vendor blogs ≠ one analyst report ≠ one peer-reviewed study. Don't conflate.
- **No "what's missing" section.** Without naming the gap, downstream synthesis assumes secondary is enough — usually wrong.