Random Coffee Best Fit Outreach

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name: random-coffee-best-fit-outreach description: Offline random coffee skill for ranking opt-in people and preparing consent-first intro packets. It creates local reports only; any external communication stays outside the public skill. version: 0.1.4 homepage: https://github.com/zack-dev-cm/random-coffee-best-fit-outreach license: MIT user-invocable: true metadata: {"openclaw":{"homepage":"https://github.com/zack-dev-cm/random-coffee-best-fit-outreach","skillKey":"random-coffee-best-fit-outreach","requires":{"anyBins":["python3","python"]}}}

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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:

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 --format markdown --out . - From this skill wrapper in the repo: python3 {baseDir}/scripts/random_coffee_matcher.py rank --format markdown --out . 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 --out . 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:

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.