# Research > Researches a company, person, buying committee, signal, domain, stack, financial context, or competitor mention. Produces a focused answer with sources and confidence notes. - **Type:** Skill - **Install:** `agentstack add skill-swan-gtm-gtm-skills-research` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [swan-gtm](https://agentstack.voostack.com/s/swan-gtm) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [swan-gtm](https://github.com/swan-gtm) - **Source:** https://github.com/swan-gtm/gtm-skills/tree/main/skills/ido-goldberg/research ## Install ```sh agentstack add skill-swan-gtm-gtm-skills-research ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About ## Instructions Research is target-driven and doesn't require org config to function — there's no state-hint paragraph and no `Setup.md`. The agent just routes to the right sub-page and runs. ### Step 1 — Parse the ask, pick the sub-page Route based on what the user is asking about. Load the sub-page via `swan-get-skill`. | Ask shape | Sub-page | |-----------|----------| | Generic company / one company / "what does X do" / "/research acme" | `Company` | | Named person — "/research tom from acme", "tell me about Jane Doe" | `Person` | | Mapping the people inside one account — buying committee, who to thread to | `AccountTeam` | | How does the target sell / their GTM / pricing posture | `GTMmotion` | | What's recent / news / signals / last-30-days | `News` | | Domain-level / web traffic / SEO / digital footprint | `Domain` | | Financial health / funding / revenue / runway / ownership | `Financial` | | Tech stack only — what tools do they run | `TechStack` | | Where the target talks about competitors / displacement signals | `CompetitorMentions` | If the ask is ambiguous (e.g. just a company name with no further context), default to `Company`. If the ask explicitly chains multiple facets ("research acme — company plus the buying committee"), it's fine to load two sub-pages — but always pick a primary. ### Step 2 — Pick the depth Most asks want a one-paragraph answer; some want a dossier. Read the user's phrasing: - "/research acme" / "what does acme do" → brief read (3–5 lines, the essentials). - "deep brief on acme" / "full dossier" / pre-meeting prep with a VP+ → run the sub-page end-to-end. - "quick — is acme an ICP fit" → micro-answer, one paragraph. Default to brief. Scale up only when the user signals depth, or when the situation (board prep, founder intro, executive outreach) demands it. Over-researching when a sentence would do is the most common failure mode. ### Step 3 — Load the sub-page and execute The sub-page owns the procedure for its facet. Follow it. Across all sub-pages, the same cheap-before-expensive pattern applies: - check what Swan already has (`swan-search-companies`, `swan-get-memory`, prior briefs) - check the CRM if connected - reach for free preview tools (`swan-fetch-scraped-url`, `swan-fetch-businesses`, `swan-website-traffic`, `swan-fetch-business-events`) before paid enrichment - enrich (`swan-enrich-company`, `swan-enrich-contact`) only when the cheap signals left a real gap ### Step 4 — Output shape Default to a structured short summary, not raw scraped data. Every sub-page ends with a composed block — follow its shape. Always: - Cite sources inline (e.g. "via `swan-website-traffic`", "from LinkedIn post 2026-05-03", "press"). - Flag confidence on soft signals (high / medium / low based on signal density). - Surface the highest-signal finding first. Don't bury the lede in a wall of context. - Tag the company in Swan via `swan-update-company` when the research produced a durable fact worth reusing (financial state, tech stack, motion type) so the next play inherits it. --- ## What good looks like - **Right depth for the ask.** A one-paragraph answer when a paragraph is enough; a full dossier when the stakes justify it. Reading the room beats running every facet. - **Sources cited, confidence flagged.** Every claim points to a tool result, a URL, or a CRM record. Soft signals (inferred revenue, motion type, runway) carry an explicit "est" or "looks like" — never stated as fact. - **Lede first.** The user sees in 30 seconds what they came for. The strongest hook is line one of the output, not buried below firmographics. - **Knows when to stop.** If business events covered the surface area, no more web fetches. If a person has no public profile, the dossier says so and ends. What gets overlooked: - **Over-researching.** Running every sub-page when only one matters. Running the deep flow when a brief was the right call. - **Treating all signals as equal-confidence.** Headcount × industry-avg as a revenue figure stated like a fact. Inferred motion stated without hedge. - **Raw dumps.** Pasting 40 LinkedIn posts, 200 keywords, or the full enrichment payload into the chat. Synthesize, then summarize. --- ## Rules - MUST pick a single sub-page per call unless the user explicitly asks for multi-facet research. - MUST cite sources inline and flag confidence on soft signals. - MUST check the free preview tools (`swan-fetch-scraped-url`, `swan-fetch-businesses`, `swan-website-traffic`, `swan-fetch-business-events`) before chaining paid enrichments. - MUST tag the company in Swan with any durable fact (motion type, financial state, detected stack, ICP fit) the research produced. - NEVER produce a raw dump. Always synthesize into the sub-page's composed output shape. - NEVER invent quotes, beliefs, financials, or priorities not present in the source. - NEVER chain `swan-enrich-company` or `swan-enrich-contact` calls without first checking what Swan and the CRM already have. - If a tool result is truncated, read the JSON from `files/tool-outputs/_.json` in `swan-execute-code` and summarize from there. --- ## Specificity sub-pages this skill will grow Shipped today: - `Company` — general company research (the default landing for "/research acme") - `Person` — full dossier on a named person - `AccountTeam` — buying-committee mapping inside one account - `GTMmotion` — how the target sells today - `News` — last-30-days signal scan - `Domain` — digital footprint, traffic, SEO posture - `Financial` — funding history, ownership, runway, pressure - `TechStack` — detected tools by category - `CompetitorMentions` — public mentions of a named competitor Accreted over time as use cases sharpen: - `AcquisitionTargets` — research for M&A or partnership shortlists - `CustomerHealth` — research framing for an existing customer (expansion / churn risk) - `IndustryDeepDive` — a whole vertical, not one account - `Investor` — partner / fund research - `Board` — board prep on a director or chair ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [swan-gtm](https://github.com/swan-gtm) - **Source:** [swan-gtm/gtm-skills](https://github.com/swan-gtm/gtm-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-swan-gtm-gtm-skills-research - Seller: https://agentstack.voostack.com/s/swan-gtm - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.