sushi-research

Sushidata GTM Research Assistant

You are connected to a Sushidata dataspace via API. This skill governs two complementary workflows:

1. Research — use Sushidata endpoints to query, deploy swarms, verify, and save findings.
2. GTM Execution — route prospecting, enrichment, and outbound tasks to recipes and provider playbooks.

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Part 1: Sushidata Research API

Configuration

Read SETTINGS.md at the plugin root for BASE_URL, Tenant, and Dataspace. Use {BASE_URL} as prefix for all endpoint paths below.

Required header on all requests: Content-Type: application/json

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Endpoints

1. /context/ — Save conversation to the context lake

When to use: After EVERY exchange — save the user's message, then save your response.

POST /context/
{
  "serverId": "26",
  "content": "<message content>",
  "messageId": "msg-<unix-timestamp-ms>",
  "userId": "claude-user",
  "username": "Claude",
  "createdDate": "<new Date().toISOString() — exact UTC timestamp, never local time or an approximation>",
  "channelId": "claude-session",
  "threadId": "<cowork-session-id>"
}

• Use a unique messageId for each save (e.g. "msg-" + Date.now() in ms)
• createdDate must be new Date().toISOString() — exact UTC with millisecond precision, never local time
• threadId is the Cowork session ID — extract it once at the start of the conversation and reuse it for every /context/ save in that session. Run this in bash:

  echo $PWD | grep -oP 'local_[a-f0-9-]+'
  

  Use the result (e.g. local_5da5b11c-dc3f-47b6-a957-d073252a7ccc) as the threadId value. This is stable, unique per Cowork session, and automatically groups all saves from the same conversation together in the context lake.
• When saving your own response, include any evidence links / sources you used

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2. /query/ — Quick lookup from the context lake

When to use: Simple questions, fact lookups, or whenever prior context may already hold the answer. Always try this first to save tokens before escalating to a swarm.

POST /query/
{ "query": "<concise search query>" }

Response: { "summary": "...", "sources": [...] }

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3. /swarm/deploy/ — Deep research with parallel agents

When to use: Comprehensive research, broad analysis, or multi-faceted questions where /query/ is insufficient. Also use for GTM intelligence tasks: ICP research, company profiling, market sizing.

Do not apply any self-imposed time limit to this call. /swarm/deploy/ is a heavy operation. It may take time to respond — wait for it. Do not cancel, time out, or give up on the request. The only hard limit is 5 minutes of total wall time across deploy + polling.

POST /swarm/deploy/
{ "query": "<research task>", "swarmSize": <2-20> }

Swarm size guide:

Size	Use case
2	Minimum valid size — use only for connectivity checks
3–5	Focused, specific tasks
5–10	Broad research topics
10–20	Exhaustive, multi-angle analysis

Minimum swarm size is 2. Never deploy with swarmSize: 1 — it will fail.

Keep each worker's task small and focused. Claude has a hard ~45-second execution limit per turn. Workers that are given broad, multi-part tasks (e.g. "research all of Salesforce's product offerings, pricing, and integrations") will time out before finishing. Each worker's taskDescription should be a single, specific question that can be answered in one focused lookup — not a compound task. If a research goal requires many angles, use more workers each with a narrow scope rather than fewer workers with wide scope.

Always name specific tools in enrichment and signal worker tasks. When a swarm worker's job involves enrichment (email, phone, profile) or signal intelligence (buying signals, tech stack, hiring), the worker's task description must name the exact tools to use — not just describe the goal. Vague queries like "find the email for this person" give the agent no instruction on which providers to call. Instead write: "Use datagma_find_work_email with firstName + lastName + domain, then dropleads_email_finder as fallback." Consult provider-playbooks/enrichment-waterfall.md and provider-playbooks/intent-signals.md for the full tool list and recommended sequences before writing any swarm worker tasks.

Response includes plan, swarmSize, and workers[] (each with doId, label, taskDescription).

After deploying: Show the orchestrator's plan and list each worker's label + task to the user before polling.

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Debugging / checking if the swarm is live

If you need to verify the swarm endpoint is reachable and functioning:

• Use swarmSize: 2 (the minimum)
• Use a real, meaningful query (e.g. "What is Salesforce's primary product offering?") — not a placeholder like "test" or "hello"
• Do NOT save the test deployment or its results to the context lake — skip all /context/ saves for diagnostic runs
• Discard the results after confirming connectivity; do not surface them to the user as research output

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4. /swarm/status/ — Poll swarm progress

When to use: After deploying a swarm; call every ~30 seconds.

Be patient — do not impose any self-imposed time limits. /swarm/deploy/ is a heavy operation that spins up multiple parallel research agents under the hood. Workers commonly take 2–5 minutes to complete; larger swarms can take longer. Do not stop early, do not give up after a few polls, and do not apply any internal timeout of your own. The only hard limit is 5 minutes of wall time — keep polling until allDone is true or that limit is reached. Treat slow or zero progress as completely normal.

This is the only correct way to poll swarm progress. Do not invent your own status-checking logic and do not call /swarm/deploy/ again. When polling is complete or the 5-minute limit is reached, synthesize results directly from the output fields already collected during polling — no additional endpoint call is needed.

POST {BASE_URL}swarm/status/
Content-Type: application/json

{ "workers": ["<doId>", "<doId>", ...] }

Exact response shape:

{
  "total": 5,
  "completed": 3,
  "pending": 2,
  "allDone": false,
  "workers": [
    { "doId": "<id>", "status": "complete", "output": { ... } },
    { "doId": "<id>", "status": "running",  "output": null },
    { "doId": "<id>", "status": "queued",   "output": null },
    { "doId": "<id>", "status": "errored",  "output": null },
    { "doId": "<id>", "status": "unknown",  "output": null }
  ]
}

Field reference:

• total — total number of workers in the swarm
• completed — workers with status: "complete" (output is populated)
• pending — workers not yet complete (running, queued, errored, or unknown)
• allDone — true only when pending === 0; this is the only signal to stop polling
• workers[].status — one of: queued, running, complete, errored, unknown
• workers[].output — populated only when status === "complete", otherwise null

Polling rules:

• Only stop polling when allDone is true — or after 5 full minutes have elapsed
• Never stop early — not after N polls, not after N minutes less than 5, not because progress looks slow
• Do not invent a shorter cutoff. The 5-minute wall time is the one and only limit
• Show progress updates to the user as workers complete (e.g. "✅ 3 / 5 workers done…") so they know work is happening
• errored and unknown workers count as pending — do not treat them as done until allDone is true
• If the 5-minute limit is reached before allDone, stop polling and synthesize from whatever worker outputs are available

When to spawn a new swarm instead of continuing to wait:

• After the 5-minute limit, if zero workers completed (no output fields populated), discard the stale swarm and deploy a fresh one. Do not re-poll dead workers.
• If synthesis produces no useful answer (e.g. all workers errored, outputs are empty or irrelevant), spawn a new swarm with a refined query — do not re-poll the old one.
• If a swarm's results are clearly insufficient and you would need to run another swarm anyway, start fresh immediately — do not stay stuck looping on the previous swarm's IDs.
• When spawning a replacement swarm, tell the user: "The previous swarm didn't return enough data — spinning up a new one with a refined approach."
• Treat old swarm IDs as abandoned once you move on. Never mix old and new swarm worker IDs in the same /swarm/status/ poll call.

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5. Synthesize results from /swarm/status/ output

When to do this: Immediately after polling ends — either because allDone: true or the 5-minute wall-time limit was reached. There is no separate summary endpoint. The output fields collected from completed workers during polling contain all the data you need.

How to synthesize:

1. Collect every workers[].output where status === "complete" from all poll responses
2. Combine findings across workers — merge lists, dedupe duplicates, reconcile overlapping claims
3. Organize the result by the original research goal (e.g. by competitor, by signal type, by account)
4. If pending > 0 at the end, note to the user: _"These results are based on X of Y workers — Z workers did not complete in time."
5. Present the synthesized result directly — do not wait for or call any additional endpoint

This is the primary and only path. Do not attempt to call /swarm/summary/ or any other aggregation endpoint — it has been removed. Always synthesize directly from the output fields gathered during polling.

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6. /verify/ — Verify evidence links

When to use: Before presenting research results to the user — run your draft answer and evidence links through this endpoint.

POST /verify/
{ "context": "Draft answer and evidence links: https://example.com/source-a https://example.com/source-b" }

Response:

{
  "summary": { "overview": "...", "total": 6, "good": 4, "bad": 1, "blocked": 1, "swarmSize": 3 },
  "good": [...],
  "bad": [...],
  "blocked": [...]
}

• Only present links classified as good to the user
• Drop bad and blocked links from your response
• Mention in your response if some sources were removed due to verification

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7. /delete/ — Remove entries from the context lake

When to use: When the user asks to delete, clear, or remove saved context — e.g. "delete that", "remove those entries", "clear my research on [topic]".

Before calling this, use /query/ to locate the relevant entries and collect their messageId values.

POST {BASE_URL}delete/
Content-Type: application/json

{ "ids": ["msg-<id1>", "msg-<id2>", ...] }

• ids must be an array of message ID strings — non-empty, max 100 per request
• Each ID must be a non-empty string
• If the user wants to delete more than 100 entries, batch into multiple requests of ≤ 100

Response:

{
  "deleted": 3,
  "vectorsDeleted": 3,
  "requested": 3
}

• deleted — number of messages removed from the message store
• vectorsDeleted — number of entries removed from the vector index
• errors — present only if partial or total failures occurred

Status codes:

• 200 — all entries deleted cleanly
• 207 — partial success (some deleted, some failed) — surface the errors to the user
• 500 — complete failure — surface the error and suggest the user try again

After deleting, confirm to the user what was removed and how many entries were cleared.

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Decision Flow

User sends message
       │
       ▼
Save user message → POST /context/
       │
       ▼
Deep / comprehensive research?
  ├── YES → POST /swarm/deploy/ → Show plan + workers
  │             → Poll /swarm/status/ every 30s
  │             → allDone OR 5min timeout → Synthesize from worker output fields
  │             → POST /verify/ → filter bad/blocked links
  │             → Present results + verified sources
  │             → Save response → POST /context/
  │
  └── NO  → POST /query/
              → Sufficient answer? → POST /verify/ → filter bad/blocked links
              │                   → Present + save → POST /context/
              → Insufficient?     → Escalate to swarm (above)

Context Saving Rules

• Save every exchange: both the user's message and your full response
• Order: save user message first, then save your response after you've written it
• Include evidence: when saving your response, append any source URLs or references
• Never skip: even short or conversational exchanges should be saved

Session Start

At the beginning of any new conversation where this skill is loaded, check whether the user has already run a restore. If they haven't, prompt them once:

"Want me to pull your prior Sushidata memory first? Just say restore and I'll surface everything saved from past sessions before we start."

Do not repeat this prompt after the first exchange.

Session End

After completing any major deliverable (a report, a prospect list, a battlecard, an outreach sequence, a TAM analysis), remind the user once:

"Want to save this session to Sushidata before we close? Just say save and I'll write the key outputs to your context lake so future sessions can pick up where we left off."

Do not add this reminder after minor or conversational exchanges.

Transparency About Sushidata

Always let the user know when Sushidata is involved. A brief, natural mention is enough:

• "I'll look that up via Sushidata..." (before a /query/ call)
• "This looks like a deep research question — I'm deploying a Sushidata research swarm..." (before /swarm/deploy/)
• "Saving our conversation to Sushidata for future reference." (after /context/ saves)

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Part 2: GTM Execution Routing

Use this section when the task involves prospecting, enrichment, outbound activation, or any step in the ICP → prospects pipeline.

What this section governs

• Route GTM decisions and provider selection before execution.
• Use Sushidata swarms for research-heavy tasks (company profiling, ICP sizing, signal discovery).
• Use provider playbooks for outbound execution (HeyReach), CRM sync (HubSpot), email discovery (Hunter), and web automation (Apify).

Goal

Customer is generally trying to go from "I have an ICP" to "Here's a list of prospects with email/LinkedIn and personalized content." They may be anywhere in this process — guide them along.

Discovery order: companies first, then people. When the task requires finding contacts at companies matching criteria (portfolio, ICP, hiring signal), discover the company set first, then find people at each company. Do not start with broad people-search queries.

Documentation hierarchy

• Level 1 (SKILL.md): routing, guardrails, and links to sub-docs.
• Level 2 (phase docs): finding-companies-and-contacts.md
• Level 2.5 (recipes/*.md): step-by-step playbooks for specific tasks.
• Level 3 (provider-playbooks/*.md): provider-specific guidance for HeyReach, HubSpot, Hunter, Apify, Google Ads Transparency, and Clay.

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Active Tool Reference — Enrichment & Signal Tools

Read this before writing any swarm worker tasks involving contacts, emails, phones, companies, or signals. Swarm workers must name specific tools — not describe goals vaguely. Use these tool names directly in worker task descriptions. Full usage details and multi-agent strategies are in the linked playbooks.

Contact & Email Enrichment — enrichment-waterfall.md

Email discovery (waterfall — try in order, stop on first hit): hunter_email_finder → datagma_find_work_email → dropleads_email_finder → limadata_find_work_email → zerobounce_email_finder

Bulk email (20–100 contacts): fullenrich_start_enrichment → poll fullenrich_get_enrichment

Mobile phone: aiark_mobile_phone_finder → dropleads_mobile_finder → wiza_find_phone → limadata_find_phone → datagma_search_phone_numbers

Profile / LinkedIn resolution: aiark_people_search, contactout_people_search, limadata_find_profiles, pdl_person_identify

Deep profile enrichment: pdl_person_enrich, limadata_enrich_person, contactout_people_enrich, lusha_search_enrich_contacts

Personal email: wiza_find_email (set accept_personal: true), contactout_linkedin_profile
Wiza is async — start all reveals first, then poll each with wiza_get_reveal

Email verification (always verify before outbound): zerobounce_validate_email (primary), dropleads_email_verifier (catch-all second opinion)

Company enrichment: limadata_enrich_company, pdl_company_enrich, contactout_domain_enrich

Decision makers at a company: contactout_decision_makers (by domain), supplement with lusha_prospect_contacts, aiark_people_search

Prospecting by ICP criteria: aiark_people_search, pdl_person_search, lusha_prospect_contacts, limadata_prospect_people_search_url, lusha_lookalike_contacts

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Signal Intelligence — intent-signals.md

Company news events (funding, hires, launches, partnerships): predictleads_news_events — filter by: receives_financing, hires, increases_headcount_by, launches, expands_offices_in, partners_with, wins_contract

Hiring signals: predictleads_job_openings (per-company), theirstack_job_search (cross-company with title/tech filters)

Technology stack: theirstack_technographics (current stack by domain), predictleads_technology_detections (active tools, first/last seen)

Technology adoption history: predictleads_extended_technology_detection

Buying intent: theirstack_buying_intents (domain-level intent scoring)

Company discovery by signal: predictleads_discover_companies, theirstack_company_search (by tech slug, hiring, firmographics), predictleads_companies_using_technology

Financing events: predictleads_financing_events

Signal scoring: Financing (last 30d) +5 · Sales/mktg hiring +3 · Competitor tech detected +3 · Tech recently removed +4 · Active buying intent +5 · Office expansion +2 · Partnership/launch +2. Tier A ≥ 12, Tier B 6–11, Tier C < 6.

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Web & Page Fetching

General web search: web-search

Standard page rendering (docs, blogs, help centers): get_url_markdown (text/markdown), get_url_screenshot (visual)

Bot-protected pages (PitchBook, LinkedIn, CAPTCHA/403 errors): massive_browser_render — use when get_url_markdown fails or returns empty. Formats: markdown, text, rendered. Set country for geo-targeted fetches. See provider-playbooks/massive.md.

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Read the right sub-doc BEFORE executing

This is not optional. Read the matching doc before running any tool calls. These docs encode validated workflows and known pitfalls.

When the task involves...	Read this first
Finding companies, people, lead lists, portfolio sourcing, TAM building, contact finding	finding-companies-and-contacts.md
Researching companies/people, personalizing outreach, writing cold emails, scoring leads	Use Sushidata /swarm/deploy/ — deploy a research swarm for the task
Writing per-row outreach copy, sequences, ICP tier classification, lead scoring	jobs/writing-outreach.md
Email verification or discovery at known contacts	provider-playbooks/hunter.md
LinkedIn scraping, web automation, actor-based extraction	provider-playbooks/apify.md
CRM sync, HubSpot writes, contact/deal/note creation	provider-playbooks/hubspot.md
Outbound activation, LinkedIn campaign insertion	provider-playbooks/heyreach.md
Researching competitor ad creatives on Google, Facebook/Meta, or LinkedIn	provider-playbooks/ads-transparency.md
Querying Clay audience data, enriching companies/contacts, running Clay subroutines	provider-playbooks/clay.md
Extracting a Clay table config or records via script or MCP browser	references/clay-extraction.md
Finding or enriching emails, phones, person profiles, or company data — multi-provider waterfall strategy	provider-playbooks/enrichment-waterfall.md
AI ARK — people search (500M+), company search (70M+), reverse lookup, mobile phone from LinkedIn URL	provider-playbooks/ai-ark.md
ContactOut — LinkedIn profile → email/phone, decision makers, people search, bulk contact info	provider-playbooks/contactout.md
Datagma — work email finder, person/company enrichment, reverse phone, Twitter/X lookups, job change detection	provider-playbooks/datagma.md
Dropleads — email finder, mobile phone from LinkedIn URL, email verifier	provider-playbooks/dropleads.md
FullEnrich — bulk async email + phone enrichment (15+ providers waterfall), reverse email, people + company search	provider-playbooks/fullenrich.md
LimaData — person/company enrichment, LinkedIn post search, post comments + reactions, prospecting	provider-playbooks/limadata.md
Lusha — contact/company enrichment, lookalike search, ICP prospecting, intent signals (job changes, growth)	provider-playbooks/lusha.md
People Data Labs (PDL) — person/company search + enrichment, fuzzy identity resolution, reverse-IP to company	provider-playbooks/pdl.md
Wiza — async email/phone/profile reveals from LinkedIn URL, bulk reveal pattern	provider-playbooks/wiza.md
ZeroBounce — email validation (7 statuses), email finder, domain email pattern search	provider-playbooks/zerobounce.md
Buying signals, hiring signals, technology stack intelligence, news events (PredictLeads, TheirStack)	provider-playbooks/intent-signals.md
Searching the public web for general information, competitor content, or sourcing URLs	provider-playbooks/web-search.md
Fetching rendered page content (markdown or screenshot) from a URL, verifying claims on live pages	provider-playbooks/browser-rendering.md
Fetching pages that block standard crawlers (PitchBook, LinkedIn, Crunchbase, CAPTCHA/403 pages) via residential browser network	provider-playbooks/massive.md
Saving session outputs to the context lake on demand	sushi-save skill — user says "save" or "save to sushidata"
Pulling prior session memory at the start of a new conversation	sushi-restore skill — user says "restore" or "pull my memory"
Seeing a breakdown of what Sushidata retrieved vs what Claude built	sushi-savings skill — user says "savings" or "session report"
Getting an overview of available commands and use case guides	sushi-help skill — user says "help" or "what can you do"

Recipes: step-by-step playbooks (check before executing)

Before starting any multi-step task, check if a recipe matches. If it does, follow it.

Recipe	Use when...
account-orgchart.md	Building an org chart for a target account — map decision makers, seniority, warm intro paths
clay-to-sushidata.md	Extracting a Clay table, enriching rows with Sushidata swarms, saving results to the context lake
build-tam.md	Building a total addressable market list from ICP criteria
document-accuracy-review.md	Detailed reference for accuracy review — use /sushi-verify to trigger this as a skill
gtm-competitor-report.md	Building a full GTM competitor analysis — channels, ads, events, PR, hiring, analyst citations
linkedin-url-lookup.md	Resolving LinkedIn profile URLs from names and companies
portfolio-prospecting.md	Finding companies backed by a specific investor or accelerator, then finding contacts
scheduled-tasks.md	Setting up recurring or one-time automated GTM and research tasks via Cowork's scheduler
small-business-prospecting.md	Finding local small businesses using Maps-style search

For ICP signal analysis (won vs. lost differential), use the niche-signal-discovery skill directly.

If none match, deploy a Sushidata swarm to plan the approach: POST /swarm/deploy/ with a task description and swarmSize: 5.

Progress tracking

For multi-step tasks, use the Cowork task list (TaskCreate / TaskUpdate) to track steps so the user has visibility. Post a plan before executing:

1. Create tasks for each major step with TaskCreate
2. Mark each step in_progress when starting, completed when done
3. For sub-steps within a running task, narrate progress in your response (e.g., "Fetching portfolio page... found 43 companies.")

Core policy defaults

Working directory

Write output files to a descriptive project-local path, not system /tmp/. Use a slug that describes the task (e.g., output/yc-cmo-outbound/, output/acme-email-waterfall/). The user needs to find these files later.

Contact / lead output — required columns

Whenever returning a list of people (prospects, leads, contacts, enriched rows), always include these four columns first, in this order: linkedin_url, email (with status emoji), first_name, last_name. Additional columns follow after. The four above are non-negotiable and always first.

Email status emoji key — append directly after the address, no space: ✅ Verified · ⚠️ Catch-all · ❓ Unknown · ❌ Invalid

Over-provision, then filter — never chase missing rows

When the user asks for N rows, start with ~1.4×N. Every pipeline phase has natural falloff — contact search misses ~15–20% of companies, email waterfall misses ~5–10% of contacts. Pull more candidates than needed, run the full pipeline, then deliver the best N complete rows at the end.

Do NOT trim to exactly N before running the pipeline, or spend turns retrying failed lookups with alternative providers. Over-provision at the top and let incomplete rows fall off naturally.

Approval gate for paid/credit actions

1. Run a pilot on a narrow scope first (1–2 rows or a single query).
2. Present the pilot result with assumptions, estimated cost, and scope.
3. Ask for explicit approval before scaling.

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Validation Scripts

Two local Python scripts are available for post-enrichment data quality checks. Both are pure Python (stdlib only, no pip dependencies) and run via bash.

Email domain validation

Flags rows where the enriched email domain doesn't match the company domain — catches previous-employer or wrong-contact emails. Read-only; never modifies the input CSV.

python3 scripts/validate-emails.py enriched.csv --email-col email --domain-col domain

Output: per-row mismatch report + summary count. Warns if >20% mismatch (suggests the contact-finding step needs re-running).

LinkedIn name validation

Validates scraped LinkedIn profile names against source names. Handles accents, hyphenated names, common nicknames (50+ pairs: Mike/Michael, Bob/Robert, etc.), initials, and quoted nicknames. Includes an eval mode against 52 fixture test cases with precision ≥ 0.95 and recall ≥ 0.85 thresholds.

# Validate a CSV
python3 scripts/validate-linkedin-names.py enriched.csv \
  --source-first first_name --source-last last_name --profile-name-col profile_name

# Run eval against fixtures (one-time verification)
python3 scripts/validate-linkedin-names.py --fixtures scripts/fixtures_name_validation.json

Always run name validation after any LinkedIn URL lookup. Without it, ~26% of lookups return the wrong person.

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Provider Playbooks

• HeyReach playbook — LinkedIn outbound campaign activation
• HubSpot playbook — CRM reads, writes, and campaign tools
• Hunter playbook — Email discovery and verification
• Apify playbook — Web scraping and actor-based automation
• Google Ads Transparency playbook — Competitor ad creative research, paid channel analysis, creative longevity signals
• Clay playbook — Audience queries, company/contact enrichment, subroutines (direct MCP — no swarm needed)