Run a research → enrich → outreach SDR campaign from inside Claude Code, fully local with your own data-provider + model keys. This is the ORCHESTRATOR: it dispatches phase sub-agents (research, enrich, draft) in fixed order, checkpoints with you between phases, and saves a validated run. Use when the user wants to prospect companies, build a lead list, research accounts, or draft personalized cold outreach (email or LinkedIn) over domains they name. Triggers: "run an outreach campaign", "prospect these companies", "research these domains", "draft cold emails to", "build me a lead list", "/intent-outreach".
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name
intent-outreach
description
Run a research → enrich → outreach SDR campaign from inside Claude Code, fully local with your own data-provider + model keys. This is the ORCHESTRATOR: it dispatches phase sub-agents (research, enrich, draft) in fixed order, checkpoints with you between phases, and saves a validated run. Use when the user wants to prospect companies, build a lead list, research accounts, or draft personalized cold outreach (email or LinkedIn) over domains they name. Triggers: "run an outreach campaign", "prospect these companies", "research these domains", "draft cold emails to", "build me a lead list", "/intent-outreach".
Problem: founders doing their own outbound have no SDR and won't pay per-seat data tools, yet cold
outreach still needs real research + enrichment to land. Solution: run a deterministic Research →
Enrich → Outreach campaign entirely on the user's machine with their own keys, drafting grounded
outreach the user approves before anything is saved.
You are the conductor. You don't call the data APIs yourself — you dispatch a phase sub-agent for
each stage (via the Task tool), aggregate what they return, and checkpoint with the user between
phases. The sub-agents keep their own context (yours stays clean) and each is responsible for one
stage's tool calls. Determinism lives in the MCP server, not here: research_domain / enrich_lead
call connectors in fixed registration order, so dispatching agents never changes which API runs or in
what order — you only control the fixed sequence of phases. Everything is local; keys are the user's
own.
Prerequisites
The bundled Intent Outreach MCP server — the phase agents drive its tools (research_domain,
enrich_lead); you drive list_connectors (Preflight) and save_run (final). It ships with the plugin.
At least one data-connector key in the environment (e.g. APOLLO_API_KEY, HUNTER_API_KEY —
both have free tiers). With none set, Preflight stops and tells the user what to add.
The phase sub-agents — outreach-researcher, outreach-enricher, outreach-drafter (ship with
this plugin; you dispatch them via Task).
A model — runs on whatever model Claude Code is using; no extra key needed for the default.
(Optional) a Report Profile under profiles/ or ~/.intent-outreach/profiles/.
Instructions
Run the phases below in strict order. You orchestrate; the sub-agents do each stage's work. Never skip
ahead to drafting.
Operating rules (non-negotiable)
Controlled delegation, fixed order. Dispatch the named phase agent for each stage, in sequence —
Research → Enrich → Score+Draft. This is delegation, not improvisation: you do not let tool choice
wander, and the agents call the deterministic MCP tools (they never pick or re-order providers).
Ground every claim. The agents use ONLY facts the connectors return; you pass that data forward
verbatim. Never invent a funding round, a mutual connection, a customer, a metric, or news. Thin data
⇒ an honest, generic message — no fabricated personalization.
Checkpoint with the user between phases (use AskUserQuestion for approve/edit/stop). The user owns
what goes out under their name.
The validator is the gate. Persist a run only through save_run, which validates before it
writes. If save_run returns a validation error, fix the run and retry — never work around it.
Phase 0 — Preflight
Call list_connectors. Show the user which data connectors are configured (have a key) and which
are skipped, with each one's tier (free / paid / enterprise / legacy).
If nothing is configured, stop and explain: set provider keys in the environment (e.g.
APOLLO_API_KEY, HUNTER_API_KEY) — Apollo + Hunter both have free tiers, so a full campaign can
run for free. Authentication is per-connector BYO: each tool reads its own API key from the
environment; keys never leave the machine except to each provider's own API.
Confirm the ICP/offer and target domains, the channel (email or linkedin), and how many
contacts per company to draft (default 1).
Phase 1 — Research (dispatch outreach-researcher, one per domain)
Dispatch the outreach-researcher agent via Taskonce per domain (they can run in parallel — this
is the fan-out win), passing the domain + ICP. Aggregate the returned leads + contacts; dedupe across
agents. Present a compact table (company, industry, size, # contacts). Checkpoint: let the user drop
companies before spending enrichment calls.
Phase 2 — Enrich (dispatch outreach-enricher per kept lead)
For each lead the user kept, dispatch the outreach-enricher agent via Task, passing the lead + its
contacts. Collect the returned enrichments (funding, verified emails/phones, web context). Show the user
the new signals.
Phase 3 — Score + Draft (dispatch outreach-drafter per lead)
For each lead, dispatch the outreach-drafter agent via Task, passing the ICP, lead, contacts,
enrichments, channel, contacts-to-draft, and any style override from a Report Profile. It returns a fit
score, angles, and draft messages (grounded only in the data you passed). Collect the drafts. Checkpoint:
the user approves, edits, or rejects each before anything is saved.
Phase 4 — Save (validated, local)
Assemble the run and call save_run with: id (a unique id you generate, e.g. from the current time),
icp, domains, provider (the model used), model, and the arrays leads, contacts,
enrichments, messages (each message: contactKey, channel, subject?, body, cta, fitScore?,
model, promptVersion: "outreach.v1", createdAt). save_run validates and appends to the local
JSONL store; report the saved path and counts. On a validation error, correct the offending field and
call it again.
Output
The skill produces a validated CampaignRun appended to the local JSONL store
(~/.intent-outreach/runs.jsonl by default), plus an at-a-glance summary for the user:
the drafted messages themselves (shown for approval).
Nothing is ever sent — save_run only persists locally; sending stays the user's deliberate act.
Error Handling
No connectors configured (Preflight) — stop and list the keys to set (free tiers first); do not
dispatch the research agents.
A phase agent reports a connector errored mid-run — it records the connector as skipped and
continues; surface which connectors ran versus skipped so the user knows the result is partial. Never
fabricate data to fill a gap.
A lead has thin data — the drafter writes an honest, generic message; never invent a funding round,
customer, metric, or mutual connection to manufacture personalization.
A dispatched agent returns nothing / fails — report the empty result honestly for that domain or
lead and continue with the rest; do not invent its output.
save_run returns a validation error — fix the offending field named in the error and call it
again. Never work around the validator; it is the gate that keeps un-validated output out of the store.
Score + draft — dispatch outreach-drafter per lead; it scores fit 0–100 and, for strong fits, drafts one grounded ≤90-word email opening on a real signal (e.g. a recent raise). Show the drafts; the user edits one and approves.
Save — call save_run; it validates and appends a CampaignRun to ~/.intent-outreach/runs.jsonl. Report the saved path + counts.
The save_run payload you assemble looks like:
{"id":"run-2026-06-16-devtools","icp":"seed-stage dev-tools founders","domains":["stripe.com","linear.app"],"provider":"anthropic","model":"claude-sonnet-4-6","leads":[{"domain":"linear.app","companyName":"Linear","source":"apollo"}],"contacts":[{"name":"A. Founder","leadDomain":"linear.app","email":"a@linear.app","source":"apollo"}],"enrichments":[{"subjectType":"lead","subjectKey":"linear.app","provider":"crunchbase","data":{}}],"messages":[{"contactKey":"a@linear.app","channel":"email","body":"...","cta":"Open to a quick call?","model":"claude-sonnet-4-6","promptVersion":"outreach.v1","createdAt":"..."}]}
Model-agnostic note
This runs on whatever model Claude Code is using. To drive a non-Anthropic model from inside Claude
Code, point ANTHROPIC_BASE_URL at an LLM gateway (LiteLLM/Bifrost) that translates to OpenAI / Grok /
Gemini. The standalone intent-outreach CLI can also target a provider directly with that provider's
key — but only providers that have passed the eval gate are marketed as supported (Claude is the
default). See 000-docs/017-AT-DECR.
Report Profiles (optional)
If the user has a Report Profile (a local file under profiles/ or ~/.intent-outreach/profiles/), use
Read to load it and pass its knobs forward — intake, filtering, tone/length, output formats, and
delivery. The profile owns the deterministic choices; the drafter agent owns the grounded drafting.
Manage profiles with the outreach-profile skill. Full knob reference and the shipped starters:
references/report-profiles.md.
Resources
Phase sub-agents (dispatched via Task) — outreach-researcher (Phase 1, one per domain),
outreach-enricher (Phase 2), outreach-drafter (Phase 3).
Companion skills — outreach-connectors (preflight connector status), outreach-research
(quick Phase-1-only research without a full campaign), outreach-profile (manage Report Profiles).
