Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Cold Outreach Skill
Orchestrates Apollo, LinkedIn, YC Cold Outreach, and MachFive APIs to source leads, enrich profiles, create personalized B2B outreach sequences ready for sen...
skill openclawclawhub Free
Orchestrates Apollo, LinkedIn, YC Cold Outreach, and MachFive APIs to source leads, enrich profiles, create personalized B2B outreach sequences ready for sen...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/inspected-skills.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- h4gen
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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.
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.
Stage 1: Build lead universe (Apollo)
Query Apollo for ICP-constrained leads (example: CMO + SaaS + Berlin). Page until lead_count_target or quality threshold is reached. Normalize each lead record to required fields. 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
Attempt LinkedIn/API enrichment within supported endpoints. If direct personal-post signal is unavailable, keep the context slot explicit as not_available. 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)
Resolve campaign ID first (GET /api/v1/campaigns) if not provided. Submit leads with required email field. Prefer batch for many leads; poll until completion. 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": Apollo returns target leads (example target: 50 CMOs in Berlin SaaS). LinkedIn/API enrichment attempts to add usable context per lead. YC framework converts lead context into a concise Problem โ Solution โ Proof โ CTA angle. MachFive generates multi-step sequences with validated variables. 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.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/inspected-skills.md