cold-outreach-skill（冷外展技巧）

Purpose

Run a full B2B cold outreach workflow from ICP definition to sequence-ready output.

Primary objective:

• Identify high-fit leads.
• Enrich context for personalization.
• Produce concise, non-salesy, high-response outreach sequences.
• Return execution-ready assets for external sending/scheduling systems.

This is an orchestration skill. It coordinates upstream skills; it does not replace them.

Required Installed Skills

• apollo-api (inspected latest: 1.0.5)
• linkedin-api (inspected latest: 1.0.2)
• yc-cold-outreach (inspected latest: 1.0.1)
• cold-email (MachFive Cold Email, inspected latest: 1.0.5)

Install/update with ClawHub:

npx -y clawhub@latest install apollo-api
npx -y clawhub@latest install linkedin-api
npx -y clawhub@latest install yc-cold-outreach
npx -y clawhub@latest install cold-email
npx -y clawhub@latest update --all

Verify availability:

npx -y clawhub@latest list

If any required skill is missing, stop and report exact install commands.

Required Credentials

• MATON_API_KEY for apollo-api and linkedin-api (Maton gateway)
• MACHFIVE_API_KEY for cold-email

Preflight checks:

echo "$MATON_API_KEY" | wc -c
echo "$MACHFIVE_API_KEY" | wc -c

If either key is missing or empty, stop before lead processing.

Job Context Template

Collect these inputs before execution:

• offer: what is being sold (example: design service)
• icp_title: target role (example: CMO)
• icp_industry: target industry (example: SaaS)
• icp_location: target location (example: Berlin)
• lead_count_target (example: 50)
• campaign_goal: reply, meeting, referral, audit request, etc.
• proof_points: case studies, metrics, social proof
• tone_constraints: plain-English, short, non-salesy
• machfive_campaign (campaign ID or campaign name to resolve)
• execution_mode: draft-only or generation-ready

Do not start writing copy until these are explicit.

Tool Responsibilities

Apollo API (apollo-api)

Use for lead discovery and basic enrichment.

Operationally relevant behavior from inspected skill:

• Search people: POST /apollo/v1/mixed_people/api_search
• Search filters include:

- q_person_title - person_locations - q_organization_name - q_keywords

• Enrich person by email or LinkedIn URL:

- POST /apollo/v1/people/match

• Supports pagination via page and per_page.
• Uses Maton gateway and optional Maton-Connection header.

Primary output of this stage:

• initial lead list with role/company/email/linkedin_url (when available)

LinkedIn API (linkedin-api)

Use for LinkedIn-side context where accessible through provided endpoints.

Operationally relevant behavior from inspected skill:

• Authenticated profile/user info endpoints (for connected account context).
• Content/posting APIs (ugcPosts) and organization post/stat APIs.
• Requires MATON_API_KEY and LinkedIn protocol headers.

Important boundary:

• The inspected skill is not a generic scraper for arbitrary third-party personal profiles and recent personal posts.
• If a workflow requires deep per-lead personal-post enrichment, mark that as additional-tool-required.

YC Cold Outreach (yc-cold-outreach)

Use as writing strategy/critique framework, not as a transport API.

Core principles to enforce:

• single goal per email
• human tone
• deep personalization (not just token replacement)
• brevity/mobile readability
• credibility and proof
• reader-centric language
• clear CTA

MachFive Cold Email (cold-email)

Use for sequence generation from prepared lead records.

Operationally relevant behavior from inspected skill:

• Campaign required (campaign_id mandatory for generate endpoints).
• Single lead sync generation (/generate) can take minutes; use long timeout.
• Batch async generation (/generate-batch) returns list_id; poll list status; export when complete.
• Lead email is required.
• Supports structured sequence output with subject/body per step.

Canonical Workflow

Stage 1: Build lead universe (Apollo)

1. Query Apollo for ICP-constrained leads (example: CMO + SaaS + Berlin).
2. Page until lead_count_target or quality threshold is reached.
3. Normalize each lead record to required fields.
4. Drop records without email if generation-ready mode is requested (MachFive requires email).

Recommended normalized lead schema:

{
  "lead_id": "apollo-or-derived-id",
  "name": "Anna Example",
  "title": "Chief Marketing Officer",
  "company": "Startup GmbH",
  "location": "Berlin",
  "email": "anna@startup.com",
  "linkedin_url": "https://linkedin.com/in/...",
  "source": "apollo-api"
}

Stage 2: Enrich personalization context

1. Attempt LinkedIn/API enrichment within supported endpoints.
2. If direct personal-post signal is unavailable, keep the context slot explicit as not_available.
3. Optionally enrich from Apollo fields (company, role, keywords, domain context) to avoid fake personalization.

Personalization object per lead:

{
  "icebreaker": "not_available_or_verified_fact",
  "pain_hypothesis": "Likely CRO bottleneck in paid landing pages",
  "proof_hook": "Helped X improve conversion by Y%",
  "confidence": 0.0
}

Hard rule:

• Never invent a post, interest, or quote.

Stage 3: Message strategy (YC framework)

For each lead, create a strategy brief before generating copy:

• Problem: what specific pain this role likely has
• Solution: what your offer solves
• Proof: one concrete metric/client signal
• CTA: one low-friction next step

Apply YC constraints:

• one ask
• short/mobile-first
• human language
• personalization grounded in verifiable context

Stage 4: Sequence generation (MachFive)

1. Resolve campaign ID first (GET /api/v1/campaigns) if not provided.
2. Submit leads with required email field.
3. Prefer batch for many leads; poll until completion.
4. Export JSON result and map sequences back to lead IDs.

Required generation payload hygiene:

• include name, title, company, email
• include linkedin_url and company_website when available
• set email_count intentionally (usually 3)
• use approved CTA set aligned with campaign goal

Stage 5: QA and decision gate

Before declaring output ready, validate each sequence:

• personalization factuality check
• YC rubric check (human, concise, one CTA)
• token insertion sanity (name/company/title correct)
• prohibited claims check (no fabricated proof)

Any failed sequence must be flagged needs_revision.

Stage 6: Scheduling and send handoff

This meta-skill outputs send-ready recommendations, not direct send automation.

If user asks for timing optimization (for example Tuesday 10:00), return it as a scheduling recommendation field and handoff plan.

Example handoff object:

{
  "lead_id": "...",
  "sequence_status": "approved",
  "suggested_send_time_local": "Tuesday 10:00",
  "timezone": "Europe/Berlin",
  "send_system": "external",
  "notes": "Timing is recommendation-only; execution tool must schedule/send."
}

Causal Chain (Scenario Mapping)

For the scenario "sell design services to startup marketing leaders":

1. Apollo returns target leads (example target: 50 CMOs in Berlin SaaS).
2. LinkedIn/API enrichment attempts to add usable context per lead.
3. YC framework converts lead context into a concise Problem → Solution → Proof → CTA angle.
4. MachFive generates multi-step sequences with validated variables.
5. Agent outputs:

- approved sequences - quality score per lead - scheduling recommendation (example: Tuesday 10:00 local)

Output Contract

Always return these sections:

• LeadSummary

- requested vs qualified lead count - rejection reasons (missing email, poor fit, duplicate)

• EnrichmentSummary

- fields successfully enriched - unavailable fields and why

• SequencePackage

- one object per lead with subjects/bodies by step - QA status (approved or needs_revision)

• ExecutionPlan

- send-time recommendation - required external sender/scheduler - blockers (missing campaign, missing API key, missing email)

Guardrails

• Never fabricate personalization facts.
• Never claim a lead posted something unless sourced and verifiable.
• Do not proceed to MachFive generation without campaign ID resolution.
• Do not mark sequence approved when CTA is unclear or multiple asks exist.
• Keep language non-manipulative and compliant with outreach policies.

Failure Handling

• Missing MATON_API_KEY: stop Apollo/LinkedIn stages.
• Missing MACHFIVE_API_KEY: stop generation stage and return draft-only strategy.
• Missing campaign ID: list campaigns and request explicit selection.
• Batch timeout/partial output: continue via list status + export recovery flow.
• Insufficient lead quality: return reduced high-quality set instead of forcing volume.

Known Limits from Inspected Upstream Skills

• linkedin-api inspected capability set is not equivalent to unrestricted scraping of arbitrary personal lead activity.
• cold-email generates sequences but does not itself guarantee outbound send scheduling/execution.
• apollo-api provides search/enrichment primitives; email deliverability validation beyond provider fields may require extra tooling.

Treat these as explicit constraints in planning and reporting.