alvarovillalbaa/message-outreach
skills/message-outreach/SKILL.md
Convert raw person- or account-level data into a single, skimmable research brief for the sales team with Account, Persona, and ongoing research. Use when the user has prospect/account data and needs an actionable brief with ICP/ICA scoring.
development
Updated Apr 12, 2026
$ install --global
skillsauthnpx skillsauth add alvarovillalbaa/agent-suite message-outreachInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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95%
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Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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OWASP Dep-Check
50%
Last scanned: Apr 12, 2026, 3:12 AM8.5s2 files scanned
SKILL.md
- name:
- message-outreach
- description:
- Convert raw person- or account-level data into a single, skimmable research brief for the sales team with Account, Persona, and ongoing research. Use when the user has prospect/account data and needs an actionable brief with ICP/ICA scoring.
- version:
- 1.0.0
- license:
- MIT
- compatibility:
- Instruction-only. Optional file/JSON inputs.
Message & Outreach Research Brief Skill
Convert any pile of raw person‑ or account‑level data into a single, skimmable brief the sales team can act on immediately.
Output template
Previous research
(Short background paragraph orienting a newcomer in <100 words.)
Account
- 1‑sentence elevator pitch of the company.
- Fit vs. Ideal Customer Account scorecard (0‑10 each): Geography · Tech Stack Maturity · AI Adoption · Growth Stage · Decision Agility.
- Red‑flag signals (if any) and quick verdict: Qualify / Nurture / Disqualify.
Persona
- Role, seniority tier (A/B/C), economic power, and buying‑center position.
- 3 bullets on pains/goals (e.g. tied to productivity or AI adoption).
- Engagement cues: public posts, events, mutual connections.
Ongoing research
- Timestamped discoveries (YYYY‑MM‑DD) — newest first.
- Link or note source in‑line.
End with one forward‑looking question in italics.
Guidelines
Data ingestion: Accept JSON, CSV, or raw text. Normalize dates to ISO (YYYY‑MM‑DD) and currencies (e.g. €). Validate required fields: account name, contact name, contact role. If missing, annotate as N/A and log under “Follow‑Up Research.” Reject unsupported formats with a clear error message.
Evidence: Cite every fact inline (e.g. [LinkedIn:2025-07-20], [SalesNotes:RN-42]). For conflicting data, list variants with dates and choose the most recent with justification. Mark inferences (inferred) with brief logic.
Scoring (0–10): Geography, Tech Maturity, AI Adoption, Growth Stage, Decision Agility. Aggregate Fit = round(mean). Document bands (e.g. ARR, procurement cycle) per user context.
Output: Section order fixed; each section ≤200 words; bullets only; no extra sections.
Tone: Professional, concise, active voice. Metrics over adjectives. Every pain → specific next step; highlight one “killer insight” in bold.
When to use
- User provides prospect/account data and wants a research brief.
- User needs ICP/ICA-style scoring and qualification verdict.
- User wants engagement cues and next-step recommendations.
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