Random Coffee Best Fit Outreach#

Goal#

Run a consent-first random coffee workflow from local participant data:

• normalize opt-in people into a small participant CSV
• rank best-fit 1:1 intro candidates by mutual utility
• draft first-touch and double opt-in intro text
• render an offline review packet for the operator
• keep external communication outside this public skill

Use This Skill When#

• the user asks for random coffee, best-fit introductions, warm networking, or founder/operator matching
• the source data is already opt-in, consented, or intentionally provided by the operator
• an older chat-first matching project exists and should be adapted into a public-safe intro workflow
• Codex should produce a repeatable intro packet, not ad hoc social copy

Inputs#

Use a CSV with these canonical columns:

csv

person_id,display_name,role,organization,location,timezone,languages,domains,skills,offers,needs,preferred_channel,availability,consent_notes,do_not_match,notes

Read references/intake-schema.md when the user gives messy notes, a contact map, or community notes.

Workflow#

1. Restate the cohort goal, target audience, consent boundary, and verification command.
2. Normalize participant data into the CSV schema. Use placeholder or consented data only.
3. Rank matches:
   • In a cloned repo: python3 -m random_coffee_matcher rank <people.csv> --format markdown --out <report.md>.
   • From this skill wrapper in the repo: python3 {baseDir}/scripts/random_coffee_matcher.py rank <people.csv> --format markdown --out <report.md>.
4. Review the top matches. Prefer pairs with clear mutual utility, language overlap, manageable timezone gaps, and complete consent notes.
5. Generate a reviewed packet for any selected pair:
   • python3 -m random_coffee_matcher packet <people.csv> <person-a-id> <person-b-id> --out <packet.md>.
6. Hand the packet to the operator. Any external communication happens outside this public skill.
7. Log the operator-recorded outcome: skipped, blocked, opted in, declined, scheduled, or closed.

External Communication Boundary#

Read references/outreach-surface-runbook.md before using the packet outside the repo.

Rules:

• Use only operator-provided or consented participant data.
• Keep the generated packet local until the operator approves it.
• Do not include private notes, long copied profile text, or private conversations in public artifacts.
• Do not reveal names, handles, links, or detailed context until both sides opt in.
• If any platform, privacy, or account-control issue appears, stop this workflow and ask for human handling outside the skill.

Outreach Rules#

• First touch asks whether the person wants to be considered. It should not reveal another person's identity.
• Double opt-in asks each side before sharing names, handles, links, or detailed context.
• Keep drafts short, concrete, and easy to decline.
• Avoid fake urgency, pressure, claims of personal familiarity, or unverifiable praise.
• If either person declines or does not reply after the agreed follow-up limit, close the case.

Verification#

For the open-source repo, run:

bash

python3 -m pytest -q
python3 -m random_coffee_matcher rank examples/participants.csv --format text
python3 scripts/check_clawhub_skill_surface.py

Before publishing, run the local public-surface audit available in the surrounding Codex workspace when present.