Agent skill

discover-contacts

Research and enrich professional contacts and marketing prospects. Uses /dogpile to find current company, role, recent news, and company intelligence. Ingests CSV contact lists and outputs enriched profiles to /memory.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/discover-contacts

Metadata

Additional technical details for this skill

author
Graham
version
0.1.0
short description
Contact and prospect research via /dogpile enrichment

SKILL.md

discover-contacts

Research and enrich professional contacts and marketing prospects. Takes a contact list (CSV, YAML, or individual names) and uses /dogpile to build comprehensive profiles with current company info, role, recent news, and company intelligence.

Why This Exists

Contact lists go stale fast. People change jobs, companies get acquired, divisions restructure. A CSV from a DARPA conference 6 months ago is already outdated. This skill uses deep multi-source research to answer: "Who is this person NOW, and what is their company doing?"

Architecture

┌──────────────────────────────────────────────────────┐
│              discover-contacts                        │
│  - Ingest contact list (CSV/YAML/name)               │
│  - Batch or single-contact research                  │
│  - Rate-limited concurrent /dogpile calls            │
│  - Enrichment pipeline                               │
└──────────────────────────────────────────────────────┘
         │                              │
    ┌────┴──────┐                 ┌─────┴─────────┐
    │ Person    │                 │ Company        │
    │ Research  │                 │ Research       │
    ├───────────┤                 ├────────────────┤
    │ - LinkedIn│                 │ - Website      │
    │ - Papers  │                 │ - News         │
    │ - GitHub  │                 │ - Funding      │
    │ - News    │                 │ - Contracts    │
    │ - Patents │                 │ - Key hires    │
    └───────────┘                 └────────────────┘
         │                              │
         └──────────┬───────────────────┘
                    │
    ┌───────────────┴────────────────────┐
    │         Enriched Profile           │
    │  - Current role + company          │
    │  - Company summary + sector        │
    │  - Recent news (last 6 months)     │
    │  - Publications / patents          │
    │  - Social links                    │
    │  - Confidence + staleness score    │
    │  → Stored to /memory               │
    │  → Written to enriched CSV/YAML    │
    └────────────────────────────────────┘

Commands

bash
# Research a single contact
./run.sh research "John Rushby" --org "SRI International"

# Research a company
./run.sh company "Galois, Inc."

# Enrich a CSV contact list (batch)
./run.sh enrich /mnt/storage12tb/media/personas/references/darpa_arcos_contacts.csv

# Enrich with concurrency limit
./run.sh enrich contacts.csv --concurrency 3 --delay 5

# Research a specific contact from the CSV
./run.sh research --csv contacts.csv --row 5

# Check enrichment freshness
./run.sh freshness contacts.csv

# Export enriched profiles
./run.sh export contacts.csv --format yaml --output enriched_contacts.yaml

Input Formats

CSV (primary)

csv
first_name,last_name,organization,email
John,Rushby,SRI International,[email protected]

YAML

yaml
contacts:
  - name: John Rushby
    org: SRI International
    email: [email protected]

Single contact (CLI)

bash
./run.sh research "John Rushby" --org "SRI International"

Enrichment Pipeline

For each contact, the skill runs a structured research pipeline:

1. Person Research

/dogpile "{first_name} {last_name} {organization} current role"

Extracts:

  • Current role and company (may have changed from CSV)
  • LinkedIn profile (via Brave search)
  • Recent publications (ArXiv, Google Scholar)
  • GitHub activity (if technical)
  • Recent news mentions
  • Conference talks (YouTube)

2. Company Research

/dogpile "{organization} recent news funding contracts"

Extracts:

  • Company summary — what they do, sector, size
  • Recent news — last 6 months of significant events
  • Government contracts — via /ops-sam-gov if relevant
  • DARPA programs — via /ops-darpa if relevant
  • Key hires/departures — leadership changes
  • Funding/acquisitions — financial events

3. Profile Assembly

Merges person + company research into enriched profile:

yaml
contact:
  name: John Rushby
  current_role: Senior Computer Scientist
  current_org: SRI International
  previous_org: null  # or previous if changed
  email: [email protected]
  email_status: likely_valid  # or stale, bounced
  linkedin: null
  github: null
  research:
    publications: 3  # recent papers found
    patents: 0
    talks: 1
  company:
    name: SRI International
    sector: Defense/Research
    size: 2000+
    recent_news:
      - "SRI awarded $X contract for..."
    darpa_programs:
      - ARCOS
      - PROOFS
    sam_gov_active: true
  enriched_at: "2026-02-12T12:00:00Z"
  confidence: 0.85  # how confident in current info
  staleness_days: 0

Rate Limiting

Batch enrichment is rate-limited to avoid burning through API quotas:

Setting Default Description
--concurrency 2 Parallel /dogpile calls
--delay 10 Seconds between batches
--budget 20 Max /dogpile calls per run
--skip-enriched true Skip contacts enriched within 30 days

Storage

/mnt/storage12tb/media/personas/references/
├── darpa_arcos_contacts.csv          # Original CSV
├── darpa_arcos_enriched.yaml         # Enriched profiles
├── company_profiles/
│   ├── sri_international.yaml
│   ├── galois_inc.yaml
│   └── ...
└── enrichment_log.json               # Audit trail

Memory + Taxonomy Integration

The skill integrates with the shared memory and taxonomy systems via memory_integration.py for cross-session contact intelligence:

  • Pre-hook (recall_prior_research): Before researching a contact, recalls prior enrichment data for that person. Avoids redundant /dogpile calls and surfaces previously gathered intelligence.
  • Post-hook (learn_contact): After enrichment, stores the contact profile (name, company, role, sources, enrichment data) to memory with taxonomy bridge tags for cross-skill recall.
  • Bridge keywords: Precision, Resilience, Fragility, Corruption, Loyalty, Stealth (tuned to contact research domain).
  • Tags: ["discover_contacts", person_name] + bridges

Gracefully degrades if common.memory_client or taxonomy/taxonomy.py are unavailable.

Enriched profiles are also stored to /memory for cross-skill access:

bash
# After enrichment, profiles available via:
/memory recall "John Rushby SRI International"
/memory recall "DARPA ARCOS contacts"
/memory recall "defense contractors formal verification"

File Structure

discover-contacts/
  SKILL.md                   # This file
  run.sh                     # Shell entry point
  sanity.sh                  # Sanity checks
  config.py                  # Paths, constants, skill references
  memory_integration.py      # Memory + Taxonomy hooks

Leveraged Skills

Skill Purpose
/dogpile Multi-source deep research per contact
/memory Store enriched profiles for recall
/ops-sam-gov Government contract lookup
/ops-darpa DARPA program participation
/brave-search Free web search for current info
/perplexity Deep research for high-value contacts

Persona Generation

Enriched contacts can seed /create-persona for fictional personas:

bash
# Research a contact, then create an inspired-by persona
./run.sh research "Natasha Neogi" --org NASA
/create-persona --inspired-by /mnt/storage12tb/media/personas/references/company_profiles/nasa.yaml

This bridges the gap between real-world contacts and the persona system.

Privacy & Ethics

  • Contact data is stored locally only (12TB drive)
  • No data is sent to external services beyond search queries
  • Research uses only publicly available information
  • Email validation does NOT send emails
  • Enrichment log tracks all research for audit

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