Outclaw Research

Deeply research a specific person or organisation for B2B outreach. Pulls from every outreach-relevant tool in the user's inventory (Leadbay/LeadClaw, LinkedIn, Twitter/X, web search, company pages, podcasts, news) and writes a persistent profile into the OutClaw knowledge base. Also useful for discovery ("find me a promising lead at <company>" via Leadbay). Triggers on: 'research <person|company>', 'look up <person>', 'who is <person>', 'find <person>'s email|linkedin|phone|company', 'tell me about <company|person>', 'enrich <person>', 'pull a lead', 'surface a prospect'. Normally invoked by the outclaw orchestrator.

FIRST ACTION RULES — read before anything else

1. Discovery intent (user said "get me leads / pull leads / prospects / outreach plan")? Plans are delivered in daily batches of 10–15 fresh targets, not 1–3. Three is noise for a salesperson at a 5% reply rate. Use the batch flow:

bash

# a) Pull + save the Leadbay response, then let the batch script do the
#    bulk work (tier-1 persist for every lead, fresh-pick top N, emit
#    manifest).
# Agent: call leadbay_pull_leads (no args) → save JSON → /tmp/leadbay-pull.json
bash ~/.openclaw/skills/outclaw/shared/scripts/outclaw_daily_batch.sh --n 15 --stale-after-days 7

# b) DEFAULT: skip Tier-2 for the full batch. Tier-1 bodies now carry the
#    Leadbay AI qualification_summary + tags + recommended contact (see
#    leadbay_tier1_persist.py's richer body format) — enough for Day-1
#    drafts. Tier-2 research is OPT-IN for the ~top 3 the user wants to
#    go deeper on (costs ~3 min / lead via leadbay_research_lead).

# c) Build the Day-1 scaffold for all 15 targets. Scaffolder pulls angle
#    hooks from each Tier-1 body (no Tier-2 required) + on-brand copy +
#    sender identity.
python3 ~/.openclaw/skills/outclaw/shared/scripts/plan_scaffolder.py --max 15
# Output: /tmp/outclaw-plan-draft.md

After the scaffold is written, the agent's remaining work is only writing the 15 Day-1 email bodies + Provenance blocks (no research, no bookkeeping), then running channel_validator.py + draft_checker.py, then presenting. See outclaw-plan SKILL.

Tier-2 is opt-in. When the user says "go deeper on " or "research more", THEN call leadbay_research_lead + leadbay_tier2_persist.py for that specific target. Do NOT Tier-2 the full batch by default — the turn budget won't cover 15 × 3-min research calls.

2. Targeted research intent (user named a specific person/company)? Start at "Step 1 — Parse the input into a target" below.

3. Never fabricate. Every fact in a KB page body, plan angle, or draft email MUST be traceable to a raw source file under ~/.openclaw/outclaw/kb/raw/ or kb/me/. If you can't cite the file, you can't include the fact. (See § Zero-hallucination rule.)

4. No promise-then-silent turn closes. Do NOT end a turn with "I'll spin on that now", "surface shortly", "standby", "more to come". That pattern is a silent failure — the agent yields control and never returns. Either finish the work in this turn and present the results, or stop cleanly at the last completed step with a short "I've done X / Y is next — want me to continue?". The draft_checker.py promises-regex rejects plans containing these phrases.

---

Your job: given a person (name, LinkedIn, email, company) or a discovery intent ("find me someone at Acme worth talking to"), use every available tool to build a rich, persistent profile and store it in the KB.

Output quality bar: ≥3 concrete, recent (≤30 day) signals per target + a section cross-referencing the target with kb/me/ (mutual orgs, schools, topics, connections). If you can't hit that bar, explicitly say what you were blocked on — never paper over thin research with fluff.

Use the tools you actually have

Check your own tool list at session start. Your ordering preference:

1. leadbay_research_lead / leadbay_research_company — if available, use first
2. web_search + web_fetch — baseline every OpenClaw agent has these
3. read (for local caches under kb/raw/)
4. User-provided text pasted into the conversation

If leadbay_* isn't in your tool list, that's fine — use web_search to find the target's LinkedIn/Twitter/company page, web_fetch the specific pages, and write what you actually learn into raw/<slug>-<ts>.md. Never insist on a tool that isn't in your active tool list. Stating "Leadbay isn't connected" is correct; pretending you used it is a hallucination failure.

Resolver mandate (non-negotiable)

Before creating or modifying any page under ~/.openclaw/outclaw/kb/ or any entry in memory/<tenant>/memory.jsonl, read shared/references/RESOLVER.md and file by primary subject, not by source format or skill name. Use shared/scripts/kb_ingest.py + kb_page.py — do not hand-craft file paths. Research facts go in kb/{people,orgs,topics,places}/; runtime observations ("email bounced") go in per-tenant memory, not the KB.

Zero-hallucination rule (non-negotiable)

Every signal, hook, career fact, org fact, and connection point MUST be traceable to a real source — either a URL you fetched or a file you wrote under ~/.openclaw/outclaw/kb/raw/. If you cannot point at a real source, do NOT write the fact. Leave the field empty and list it under gaps[] in your return.

Specifically forbidden:

• Inventing a date ("March 2026 post", "€5M Series A in April 2026") without a URL or raw/ file that says so.
• Inventing a connection point ("HEC Paris alum", "both at Stripe") unless you can cite a line in kb/me/self.md or kb/me/org.md.
• Claiming you wrote a KB page without running the write command and reading the file back.

Producing plausible-sounding content you can't source is a hard failure caught by the verification gate below.

Preamble (skip if called from orchestrator)

bash

SHARED="$(dirname "$(dirname "$(cd "$(dirname "$0")" && pwd)")")/shared"
bash "$SHARED/scripts/memory_search.sh" --type tool_inventory --limit 1
cat ~/.openclaw/outclaw/kb/me/self.md 2>/dev/null
cat ~/.openclaw/outclaw/kb/me/org.md  2>/dev/null

Flow

Step 1 — Parse the input into a target

The user gave you some combination of: name, LinkedIn URL, email, company, title, company URL. From these, compute:

• slug — kb_page.py style: lower-case name, non-alnum → -, e.g. alice-chen. Disambiguate with company if needed (alice-chen-stripe).
• display_name
• company (if known) → compute an org slug the same way (acme-corp)

Discovery intent (e.g. "get me some leadbay leads", "pull a promising lead", "purchase contacts for the best ones"):

Leadbay is a sales inbox, not a queryable database. See shared/references/leadbay-integration.md for the full mental model. Key rules:

1. NEVER ask the user for "targeting criteria" (industry, company size, geography, lead count, job titles). The user's ICP is already configured inside Leadbay. Asking for criteria = you're thinking of Leadbay as a DB, which it isn't.
2. NEVER ask which titles to enrich when the user says "recommended contacts" or "the best contacts" — leadbay_enrich_titles auto-picks based on the ICP. Recommended means Leadbay chooses.
3. Verify your tool list FIRST. Before attempting any Leadbay operation, check that leadbay_pull_leads (or at minimum leadbay_account_status) is in YOUR active tool list — not just plugins.entries.leadclaw. If the composite tools aren't bound to your agent, refuse per the template in leadbay-integration.md §"What to do when the composite tools aren't in your tool list".

The canonical flow — four tool-call blocks, no improvisation:

The flow is written so the agent only decides which lead_ids to deep-research. Everything else is driven by helper scripts. Execute each block; paste the literal stdout to the user. Do not paraphrase.

bash

# --- Block 1: pre-counts + pull ---
BEFORE_ORGS=$(ls ~/.openclaw/outclaw/kb/orgs/ 2>/dev/null | wc -l | tr -d ' ')
BEFORE_PEOPLE=$(ls ~/.openclaw/outclaw/kb/people/ 2>/dev/null | wc -l | tr -d ' ')
echo "before: orgs=$BEFORE_ORGS people=$BEFORE_PEOPLE"

Then call the MCP tool leadbay_account_status, followed by leadbay_pull_leads (no args). Save the pull response JSON to /tmp/leadbay-pull.json using your Write tool.

bash

# --- Block 2: Tier-1 persist + verification gate (MANDATORY) ---
python3 ~/.openclaw/skills/outclaw/shared/scripts/leadbay_tier1_persist.py \
  --from-file /tmp/leadbay-pull.json

AFTER_ORGS=$(ls ~/.openclaw/outclaw/kb/orgs/ | wc -l | tr -d ' ')
AFTER_PEOPLE=$(ls ~/.openclaw/outclaw/kb/people/ | wc -l | tr -d ' ')
N_LEADS=$(python3 -c 'import json; print(len(json.load(open("/tmp/leadbay-pull.json")).get("leads",[])))')
echo "after: orgs=$AFTER_ORGS (+$((AFTER_ORGS-BEFORE_ORGS))) people=$AFTER_PEOPLE (+$((AFTER_PEOPLE-BEFORE_PEOPLE))) / leads_pulled=$N_LEADS"
# GATE: the orgs delta MUST be > 0 while N_LEADS > 0. If zero, STOP — the
# persist step was skipped. Do not continue to block 3.

# --- Block 3: pick top N to deep-research (freshness-aware) ---
python3 ~/.openclaw/skills/outclaw/shared/scripts/leadbay_top_picks.py \
  --from-file /tmp/leadbay-pull.json --n 15 --stale-after-days 7

This returns {ids: [...], summaries: [...], skipped_recent: [...]}. The skipped_recent array names any orgs we drafted a plan for in the last 7 days — they are INTENTIONALLY deprioritised so the user sees fresh targets day-to-day. Show the skipped list to the user with a note: "Still in flight from — include anyway?" Only re-add to the plan if the user says yes.

Tier-2 research is OPT-IN — do NOT run it for the full batch by default. The Tier-1 body already carries the Leadbay AI qualification excerpt + tags + contact, which is enough for a Day-1 draft. Only call leadbay_research_lead when the user says "go deeper on " for a specific target.

bash

# --- Block 4: Tier-2 persist (per researched lead) ---
for f in /tmp/leadbay-research-*.json; do
  python3 ~/.openclaw/skills/outclaw/shared/scripts/leadbay_tier2_persist.py --from-file "$f"
done

# Final audit
ls ~/.openclaw/outclaw/kb/orgs/ | wc -l
ls ~/.openclaw/outclaw/kb/people/ | wc -l
tail -6 ~/.openclaw/outclaw/kb/log.md

After block 4, hand off to outclaw-plan with the list of slugs for the top picks. Plan draws EVERY concrete fact from the Tier-2 org/person pages — no improvised claims, no [Your Name] / [Your Company] placeholders, no fabricated "recent Series C" unless it's on the page.

Non-negotiable rules:

• Block 2's gate must pass before you touch block 3. Orgs delta zero = STOP.
• Never paraphrase a script's stdout. Paste it as-is.
• Never claim a KB write that isn't visible in the post-counts or log tail.
• Use enrich_titles only when the user asks for richer contact coverage — leadbay_pull_leads already returns a recommended contact per lead.

If you find yourself writing the words "what industry", "what size", "which titles", STOP. You're asking the wrong question. Read shared/references/leadbay-integration.md again.

Step 2 — Check KB first

bash

python3 "$SHARED/scripts/kb_search.py" --slug person:$SLUG

If an entry exists and its last_updated is ≤30 days, return it and ask the user if they want a refresh. If yes, proceed. If no entry, proceed.

Step 2.25 — Email-domain discipline

If you have an email address for the target, classify the domain BEFORE deriving a company from it:

bash

python3 "$SHARED/scripts/domain_classifier.py" "<email>"

• private (gmail, hotmail, yahoo, proton, yandex, icloud, …): the email is a personal mailbox. Do NOT set company from this domain. Use LinkedIn / Leadbay / the target's own statements instead.
• isp (comcast, bt, verizon, cox, …): residential internet provider — same rule. Do NOT infer employer.
• school (.edu, .ac.uk, hec.fr, mit.edu, stanford.edu, …): academic affiliation. Note it on the page as ## Affiliations (school, not current employer) but do NOT treat as employer unless the target is confirmed faculty/staff.
• corporate: the domain IS the company. Use company_slug_from_domain result as the kb/orgs/<slug>.md target. Confirm by cross-checking LinkedIn / company URL when available.

Example wrong inference to avoid: "[e-mail]" → org slug "gmail". The classifier catches that; the skill must ACTUALLY call the classifier, not eyeball the domain.

Step 2.5 — Cross-reference: past conversations + same-company people

Before running fresh external research, sweep local context you already have:

a. Past conversations with THIS person. Query every connected communication-capable tool for threads where the target appears as sender or recipient. Priority order:

Tool in inventory	Query
gog (Gmail)	gog gmail messages search "from:<email> OR to:<email>" --max 50 -j
email-mcp	same search via the MCP's search endpoint
slack-mcp-server / slack	slack search --user <slack_id_or_email> --max 50
whatsapp-mcp-ts / wacli	search conversation history by phone
telegram-mcp	search chats where counterparty matches handle
mac_messages_mcp / imsg	iMessage/SMS history by phone or contact
discord-mcp	DM history by user id
linkedin-cli	InMail / message history with the profile URL
apple-notes / things-mac	search for user's own prior notes about the target

For every match, record:

• Date of last interaction
• Direction (sent-by-user | received-from-target)
• Channel
• A one-line summary (don't paste the body — PII)

Drop a raw/ snippet if the thread is substantive: kb_ingest.py begin person <slug> --note "prior-thread-<channel>-<date>: <summary>".

Under ## Prior conversations on the target's page, list each match:

text

- 2025-11-14 · Gmail · user → target · "Intro from David, re: ICP pilot"
- 2026-02-03 · LinkedIn DM · target → user · "Circling back after SaaStr Annual"

b. Signature extraction. When a past email from the target exists, parse the signature block. Useful fields to harvest INTO the KB frontmatter (not prose):

• phone (if present)
• linkedin_url (if present — updates a missing field)
• company (only if the domain check said corporate AND the signature's stated company matches the domain; otherwise flag the conflict)
• address
• Confirmed current title

If the signature is an IMAGE (.png / .jpg attachment inline), transcribe it via an available OCR-capable tool in your inventory:

Tool	How
gemini	gemini describe --image <path> --prompt "Transcribe this email signature verbatim. Return fields: name, title, company, phone, email, address, socials."
image_generate / multimodal fallback	pass the image bytes + the same prompt
openai-whisper-api	audio only — not applicable

Write extracted fields onto the KB frontmatter via kb_page.py upsert. Never invent fields — if the OCR result is unclear, log a gap, don't guess.

c. People from the same company (if known). If company_slug is known (from email or LinkedIn), pull every page in the KB for that org:

bash

# List all people with this org in their connections
grep -l "orgs: .*\b<company-slug>\b" ~/.openclaw/outclaw/kb/people/*.md
# Or the org's own backlinks:
python3 "$SHARED/scripts/kb_search.py" --slug org:<company-slug>

For each of those people, surface a one-line summary on the target's page under ## Same-company context:

text

- Bob Smith (CTO, kb/people/bob-smith.md) — active thread Mar 2026, warm
- Carol Wong (VP Sales, kb/people/carol-wong.md) — opted out 2025-10

If ANY same-company person has contact_status: opt_out, surface it prominently — opt-outs propagate to company-level wariness but NOT to automatic blocking (one person's opt-out doesn't ban outreach to their whole org).

d. Prior interactions with the ORG. Same idea but at org-level — reach into kb/orgs/<slug>.md's existing body for any ## Update or ## Prior interactions sections.

The summary of Step 2.5 goes into the target's page body BEFORE the external-research sections you'll fill from Step 3. Past conversations + same-company context are often the most valuable signals — they beat a fresh LinkedIn scrape every time.

Step 3 — Enrich, category-by-category

Read references/research-playbook.md for the full matrix. Summary:

1. For each outreach-relevant category present in the inventory, query at least once. Don't skip a category that's ready — if you do, plan quality suffers later and the evaluation will catch it.
2. Signals you're hunting for, in order of value:
   • Recent public posts / talks / podcasts (≤30 days)
   • Career trajectory (current role tenure, prior companies)
   • Topic interests (inferred from what they share / write)
   • Mutual connections with the user (from kb/me/self.md)
   • Contact info (for later planning only — never use it here)
3. Never fabricate. If a source doesn't yield a signal, move on.

Categories → tools (from inventory):

• crm → LeadClaw for full enrichment + ICP + relationship graph
• professional_network → LinkedIn for posts, tenure, connections
• social → Twitter/Bluesky for recent posts
• research_web → browse, summarize for company blog, podcasts, news
• research_contacts → apple-notes, things-mac if the user kept prior notes

Step 4 — Write the KB

Delegate the file-writing to shared/scripts/kb_ingest.py + kb_page.py.

Every target MUST:

1. Begin with kb_ingest.py begin person <slug> --name "<Display>" — creates a raw/ entry tagged with the first source.
2. Have a body containing sections: ## Role & trajectory, ## Recent activity (≤30 days), ## Topics & interests, ## Connection points with <user>, ## Sources. Write via kb_page.py upsert person <slug> --body <path>.
3. Touch the org: kb_ingest.py touch org <org-slug> --name "<Org>" --connection person:<slug>. Also append a ## Update <date> section to the org page with whatever you learned about it while researching the person.
4. Touch topics: for each interest, kb_ingest.py touch topic <topic-slug> --connection person:<slug>. Create stubs if missing.
5. Rebuild index: python3 "$SHARED/scripts/kb_index_rebuild.py".
6. Log: kb_ingest.py log ingest "<slug> research — <one-line summary>" --pages people/<slug>.md orgs/<org>.md topics/<topic>.md.

Step 4.5 — Verification gate (MANDATORY)

After every kb_page.py upsert and kb_ingest.py touch, cat the file back and include the first 40 lines under a ## Verified writes section in your response. Example:

bash

for p in ~/.openclaw/outclaw/kb/people/<slug>.md \
         ~/.openclaw/outclaw/kb/orgs/<orgslug>.md \
         ~/.openclaw/outclaw/kb/index.md \
         ~/.openclaw/outclaw/kb/log.md; do
  echo "--- $p ---"
  /usr/bin/head -40 "$p" 2>/dev/null || echo "MISSING: $p"
done

If any file is missing, STOP. Do not proceed to return. Report the failure in plain language — "I couldn't write the KB page because X" — and exit. Do NOT continue to Step 5 with fake content.

Step 5 — Return

Return to caller:

• The KB page paths (relative to ~/.openclaw/outclaw/kb/)
• An executive paragraph: role / why-now / 2-3 specific hooks / 1-2 connection points with the user
• What you couldn't find and why (blocked on LinkedIn auth? LeadClaw has no match? Their Twitter is private?)

Delegate deep multi-tool work to the agents/person-researcher.md sub-agent when the target is rich enough to justify it.

Degraded mode (no LeadClaw)

When the inventory has crm=[leadclaw:missing] or :needs_setup]:

• Skip ICP scoring + relationship-graph
• Rely on web search + LinkedIn + social for everything
• Quality score #3 (research breadth) will drop — that's expected and the user has been warned at setup time. Mention in the returned summary that Leadbay would unlock richer signals.

What this skill does NOT do

• Does not plan outreach — that's outclaw-plan.
• Does not contact anyone.
• Does not fabricate signals. When in doubt, say "I couldn't find …".