event-prospecting

by browserbase

Take a conference URL → get a ranked list of people the AE should talk to, with a "why reach out" rationale per person. npx skills add https://github.com/browserbase/skills --skill event-prospecting Download ZIPGitHub3.6k

Event Prospecting

Take a conference URL → get a ranked list of people the AE should talk to, with a "why reach out" rationale per person.

Required: BROWSERBASE_API_KEY env var and the browse CLI installed (npm install -g browse). Use browse cloud ... for API calls and browse open / browse get markdown for JS-heavy speaker pages.

Path rules: Always use the full literal path in all Bash commands — NOT ~ or $HOME (both trigger "shell expansion syntax" approval prompts). Resolve the home directory once and use it everywhere. When constructing subagent prompts, replace {SKILL_DIR} with the full literal path (typically /Users/jay/skills/skills/event-prospecting).

Output directory: All event prospecting output goes to ~/Desktop/{event_slug}_prospects_{YYYY-MM-DD-HHMM}/. Final deliverable is index.html (people grouped by company, ranked by company ICP), with companies.html and people.html (filterable) as alternate views, plus results.csv for cold-outbound import.

CRITICAL — Tool restrictions (applies to main agent AND all subagents):

• All web searches: use browse cloud search. NEVER use WebSearch.

• All page content extraction: use node {SKILL_DIR}/scripts/extract_page.mjs "<url>". This script fetches via browse cloud fetch --output, parses title + meta tags + visible body text, and automatically falls back to browse get markdown when fetch fails or returns thin JS-rendered content. NEVER hand-roll a browse cloud fetch | sed pipeline. NEVER use WebFetch.

• All research output: subagents write one markdown file per company OR per person to {OUTPUT_DIR}/companies/{slug}.md or {OUTPUT_DIR}/people/{slug}.md using bash heredoc. NEVER use the Write tool or python3 -c. See references/example-research.md for both file formats.

• Report compilation: use node {SKILL_DIR}/scripts/compile_report.mjs {OUTPUT_DIR} --open.

• Subagents must use ONLY the Bash tool. No other tools allowed.

• HARD TOOL-CALL CAPS: ICP triage = 1 call/company; deep research = 5 calls/company; person enrichment = 4 calls/person. See references/workflow.md for enforcement detail.

CRITICAL — Anti-hallucination rules (applies to main agent AND all subagents):

• NEVER infer product_description, industry, or a person's role_reason from a site's fonts, framework, design system, or typography. These are cosmetic and say nothing about what the company sells or what the person does.

• NEVER let the user's own ICP leak into a target's description. If you don't know what the target does, write Unknown — do not pattern-match them onto the ICP.

• product_description MUST quote or paraphrase a specific phrase from extract_page.mjs output. If none of TITLE/META/OG/HEADINGS/BODY yield a recognizable product statement, write Unknown — homepage content not accessible and cap icp_fit_score at 3.

• A person's hook MUST quote or paraphrase a specific finding from a browse cloud search result (podcast title, blog headline, GitHub repo, talk abstract). If no public signal exists in the last 6 months, fall back to event-context (their talk title at this event).

CRITICAL — Minimize permission prompts:

• Subagents MUST batch ALL file writes into a SINGLE Bash call using chained heredocs. One Bash call = one permission prompt.

• Batch ALL searches and ALL fetches into single Bash calls using && chaining.

Pipeline Overview

Follow these 10 steps in order. Do not skip steps or reorder.

• Setup — output dir + clean slate

• Load profile — read profiles/{user_slug}.json

• Recon — detect event platform

• Extract people — people.jsonl

• Group by company — seed_companies.txt

• ICP triage — fast company-level scoring (1 call/company)

• Filter — companies with icp_fit_score >= --icp-threshold

• Deep research — full Plan→Research→Synthesize on ICP fits

• Enrich speakers — ask user: ICP-fit only (default) or all speakers

• Compile report — HTML + CSV, open in browser

The user invokes the skill with a URL like /event-prospecting <URL>. Parse EVENT_URL from that invocation message. Defaults: DEPTH=deep, ICP_THRESHOLD=6. The USER_SLUG (ICP profile) is auto-resolved in Step 1 from whatever profile files exist locally — there is no built-in default profile. Do NOT ask the user to confirm the URL — they already gave you it.

Step 1: Load User Profile

The profile defines the ICP that ICP triage and deep research score against. Load from {SKILL_DIR}/profiles/{user_slug}.json (interchangeable across all GTM skills — same shape as company-research). example.json is a template, not a real profile — never use it.

DO NOT look outside {SKILL_DIR}/profiles/ for profiles — never reach into other skills' directories. If a profile is needed elsewhere, the user copies it explicitly.

Resolution order:

• If the user invoked with --user-company <slug>, use that slug.

• Else, list profiles/*.json excluding example.json. If exactly one profile exists, use it (and tell the user which one). If multiple exist, ask the user (plain chat) which one.

• If zero profiles exist, fail loudly and instruct the user to create one (copy profiles/example.json to profiles/<your_slug>.json and fill it in, or run the company-research skill which builds one automatically).

PROFILES=$(ls {SKILL_DIR}/profiles/*.json 2>/dev/null | xargs -n1 basename | sed 's/\.json$//' | grep -v '^example$')
COUNT=$(echo "$PROFILES" | grep -c .)

if [ -z "$USER_SLUG" ]; then
 if [ "$COUNT" -eq 0 ]; then
 echo "No profiles found in {SKILL_DIR}/profiles/. Copy profiles/example.json to profiles/ .json and fill it in, or run the company-research skill to build one."
 exit 1
 elif [ "$COUNT" -eq 1 ]; then
 USER_SLUG=$PROFILES
 echo "Using the only profile available: ${USER_SLUG}"
 else
 echo "Multiple profiles found:"
 echo "$PROFILES" | sed 's/^/ - /'
 echo "Re-invoke with --user-company to pick one."
 exit 1
 fi
fi

test -f {SKILL_DIR}/profiles/${USER_SLUG}.json || {
 echo "Profile not found: profiles/${USER_SLUG}.json"
 exit 1
}
cat {SKILL_DIR}/profiles/${USER_SLUG}.json

The profile yields: company, product, icp_description, existing_customers. These get embedded verbatim in every subagent prompt downstream.

Step 2: Recon

Detect the event platform and extraction strategy. One command:

node {SKILL_DIR}/scripts/recon.mjs {EVENT_URL} {OUTPUT_DIR}

Writes {OUTPUT_DIR}/recon.json with platform, strategy, and (for Next.js) nextDataPaths. See references/event-platforms.md for the platform catalog and detection priority.

Expected outcomes:

• Stripe Sessions class (Next.js): platform: "next-data", 1-3 paths

• Sessionize: platform: "sessionize"

• Lu.ma / Eventbrite: platform: "luma" | "eventbrite"

• Anything else: platform: "custom", strategy: "markdown" (best-effort fallback)

Step 3: Extract People

node {SKILL_DIR}/scripts/extract_event.mjs {OUTPUT_DIR} --user-company {USER_SLUG}

Reads recon.json, dispatches to the platform-specific extractor, writes people.jsonl (one speaker per line) and seed_companies.txt (deduped companies).

The --user-company flag also drops the host-org's own employees (a Stripe-hosted event drops Stripe employees) and the user's own employees from the speaker list — those aren't prospects.

Sanity-check the output:

wc -l {OUTPUT_DIR}/people.jsonl {OUTPUT_DIR}/seed_companies.txt
head -3 {OUTPUT_DIR}/people.jsonl

If people.jsonl is empty or under ~10 lines, recon picked the wrong platform — see references/event-platforms.md and re-run with adjusted strategy.

Step 4: Group by Company

extract_event.mjs emits seed_companies.txt already (one company per line, deduped, sorted). This step is informational — verify the count looks reasonable before fanning out:

wc -l {OUTPUT_DIR}/seed_companies.txt

Expected: roughly 0.4-0.6× the speaker count (most events have ~2 speakers per company on average, some companies send 5+, many send 1).

Step 5: ICP Triage

Fast pass — one tool call per company, no deep research. Score every company in seed_companies.txt against the user's ICP and write a thin triage stub to companies/{slug}.md. Companies with icp_fit_score >= --icp-threshold (default 6) advance to Step 7's deep research; the rest stay as triage stubs.

Dispatch pattern: split seed_companies.txt into batches of ~10 and fan out N subagents in a SINGLE Agent batch (multiple Agent tool calls in one message). Each subagent runs the prompt from references/workflow.md → "ICP Triage" section. Hard cap: 1 tool call per company (just extract_page.mjs on the homepage), enforced via the # browse call N/1 comment pattern.

# Build batch files: each batch line is "name|guessed_homepage|slug".
# extract_event.mjs only emits company NAMES (no URLs), so we slugify and guess
# https://{slug-without-spaces}.com as the canonical homepage. The triage subagent
# is allowed to write product_description: "Unknown — homepage content not accessible"
# and cap score at 3 if the guessed URL 404s — that's the documented fallback in
# workflow.md (rule 3 of the ICP Triage prompt). Burning a real browse cloud search to
# discover the URL would bust the 1-call-per-company HARD CAP.
node -e '
const fs = require("fs");
const slugify = (s) => (s || "").toLowerCase().replace(/[^a-z0-9]+/g, "-").replace(/^-+|-+$/g, "");
const seed = fs.readFileSync("{OUTPUT_DIR}/seed_companies.txt", "utf-8").split("\n").filter(Boolean);
const lines = seed.map(c => {
 const slug = slugify(c);
 const guessedHost = c.toLowerCase().replace(/[^a-z0-9]/g, "");
 return `${c}|https://${guessedHost}.com|${slug}`;
});
fs.writeFileSync("{OUTPUT_DIR}/_seed_with_urls.txt", lines.join("\n") + "\n");
'

# Split into ~10-company batches
split -l 10 {OUTPUT_DIR}/_seed_with_urls.txt {OUTPUT_DIR}/_batch_triage_

# Count batches → number of subagents to dispatch (cap at 6 per message; second wave for the rest)
ls {OUTPUT_DIR}/_batch_triage_* | wc -l

Then in a single message, dispatch one Agent call per batch (up to 6 in parallel; subsequent waves after the first returns). Each Agent gets the prompt from references/workflow.md → "ICP Triage" with these substitutions before sending:

• {SKILL_DIR} → full literal skill path (e.g. /Users/jay/skills/skills/event-prospecting)

• {OUTPUT_DIR} → full literal output path

• {USER_COMPANY}, {USER_PRODUCT}, {ICP_DESCRIPTION} → from the loaded profile

• {EVENT_NAME} → recon.json .title

• {COMPANY_LIST} → contents of the batch file (e.g. cat {OUTPUT_DIR}/_batch_triage_aa)

• {TOTAL} → number of lines in this batch (substitute into # browse call N/{TOTAL})

Agent dispatch (skeleton, repeat per batch in one message):

Agent(
 description: "ICP triage batch aa",
 prompt: ,
 subagent_type: "general-purpose"
)
Agent(
 description: "ICP triage batch ab",
 prompt: ,
 subagent_type: "general-purpose"
)
... up to 6 per message

After all subagents return, verify every company in seed_companies.txt has a corresponding companies/{slug}.md:

ls {OUTPUT_DIR}/companies/*.md | wc -l
# Should equal `wc -l {OUTPUT_DIR}/seed_companies.txt`

Clean up the batch files: rm {OUTPUT_DIR}/_batch_triage_*.

Step 6: Filter by ICP Threshold

Read each companies/*.md frontmatter, keep those with icp_fit_score >= 6 (or whatever --icp-threshold is). Write the surviving company slugs to {OUTPUT_DIR}/icp_fits.txt:

THRESHOLD=6 # from --icp-threshold flag
for f in {OUTPUT_DIR}/companies/*.md; do
 score=$(awk '/^icp_fit_score:/{print $2; exit}' "$f")
 if [ -n "$score" ] && [ "$score" -ge "$THRESHOLD" ]; then
 basename "$f" .md
 fi
done > {OUTPUT_DIR}/icp_fits.txt

wc -l {OUTPUT_DIR}/icp_fits.txt

Expected: 20-40% of seed_companies.txt. If the survival rate is < 10%, the threshold may be too high or the ICP description too narrow — surface a warning to the user.

Step 7: Deep Research

Full Plan→Research→Synthesize on ICP-fit companies only. Hard cap: 5 tool calls per company (homepage extract + 2-3 sub-question searches + 1-2 supplementary fetches). Subagents OVERWRITE the existing companies/{slug}.md triage stub with the richer deep-research version (frontmatter triage_only: false).

Dispatch pattern: split icp_fits.txt into batches of ~5 (deep mode default) and fan out one Agent per batch in a SINGLE message (up to 6 Agents per message). Each Agent gets the prompt from references/workflow.md → "Deep Research" with these substitutions:

• {SKILL_DIR}, {OUTPUT_DIR}, {USER_COMPANY}, {USER_PRODUCT}, {ICP_DESCRIPTION}

• {EVENT_NAME} (from recon.json .title), {EVENT_CONTEXT} (track / topic, manually inferred from the event homepage)

• {COMPANY_LIST} → contents of the batch file (each line slug|website)

# Build {company-slug|website} pairs by reading frontmatter from each triage stub
while read slug; do
 website=$(awk '/^website:/{print $2; exit}' {OUTPUT_DIR}/companies/${slug}.md)
 echo "${slug}|${website}"
done {OUTPUT_DIR}/_deep_targets.txt

# Split into ~5-company batches (deep mode)
split -l 5 {OUTPUT_DIR}/_deep_targets.txt {OUTPUT_DIR}/_batch_deep_
ls {OUTPUT_DIR}/_batch_deep_* | wc -l

Agent dispatch (skeleton, repeat per batch in one message):

Agent(
 description: "Deep research batch aa",
 prompt: ,
 subagent_type: "general-purpose"
)
Agent(
 description: "Deep research batch ab",
 prompt: ,
 subagent_type: "general-purpose"
)
... up to 6 per message; second wave after the first returns

After all subagents return, verify the deep-research files exist and have triage_only: false:

grep -l "triage_only: false" {OUTPUT_DIR}/companies/*.md | wc -l
# Should equal wc -l icp_fits.txt

Step 8: Enrich Speakers

Per person: harvest LinkedIn URL, recent activity (podcast / blog / talk / GitHub / X), and write people/{slug}.md. Hard cap: 4 tool calls per person, three lanes:

• browse cloud search "{name} {company} linkedin" (always)

• browse cloud search "{name} podcast OR talk OR blog 2026" (deep+)

• browse cloud search "{name} github" (deeper)

• browse cloud search "{name} site:x.com OR site:twitter.com" (deeper, best-effort)

Quick mode: skip Step 8 entirely. Deep mode: lanes 1-2. Deeper mode: lanes 1-4.

Step 8a — Ask the user: scope of enrichment

Before dispatching, compute the two candidate counts and ask the user to choose. The default is ICP-fit only (faster, cheaper, what most users want); enriching every speaker is opt-in because cost scales linearly with people enriched.

TOTAL=$(wc -l s.toLowerCase()));
const ppl = fs.readFileSync("{OUTPUT_DIR}/people.jsonl","utf-8").split("\n").filter(Boolean).map(JSON.parse);
console.log(ppl.filter(p => p.company && want.has(p.company.toLowerCase())).length);
')

# Lanes per person: 2 (deep) or 4 (deeper) — match {DEPTH}
LANES=2 # or 4 for deeper
echo "ICP fits: ${ICP_FITS} speakers × ${LANES} = $((ICP_FITS * LANES)) calls"
echo "All: ${TOTAL} speakers × ${LANES} = $((TOTAL * LANES)) calls"

Then ask via AskUserQuestion — clean two-option choice with the quantified cost on each:

AskUserQuestion(questions: [
 {
 question: "Enrich which speakers?",
 header: "Enrichment scope",
 multiSelect: false,
 options: [
 { label: "ICP fits only", description: "${ICP_FITS} speakers, ~$((ICP_FITS * LANES)) calls (recommended)" },
 { label: "All speakers", description: "${TOTAL} speakers, ~$((TOTAL * LANES)) calls" }
 ]
 }
])

Save the chosen scope as ENRICH_SCOPE=icp_fits or ENRICH_SCOPE=all. If the user picks "All speakers" and TOTAL × LANES > 600, print a warning and ask once more — that's a 10+ minute run with hundreds of tool calls.

Step 8b — Filter and batch

# Build _people_to_enrich.jsonl based on ENRICH_SCOPE
if [ "$ENRICH_SCOPE" = "all" ]; then
 cp {OUTPUT_DIR}/people.jsonl {OUTPUT_DIR}/_people_to_enrich.jsonl
else
 node -e '
const fs = require("fs");
const fits = new Set(fs.readFileSync("{OUTPUT_DIR}/icp_fits.txt", "utf-8").split("\n").filter(Boolean));
const slug2name = {};
for (const slug of fits) {
 const md = fs.readFileSync(`{OUTPUT_DIR}/companies/${slug}.md`, "utf-8");
 const m = md.match(/^company_name:\s*(.+)$/m);
 if (m) slug2name[slug] = m[1].trim();
}
const wantNames = new Set(Object.values(slug2name).map(s => s.toLowerCase()));
const lines = fs.readFileSync("{OUTPUT_DIR}/people.jsonl", "utf-8").split("\n").filter(Boolean);
const keep = lines.filter(l => {
 const p = JSON.parse(l);
 return p.company && wantNames.has(p.company.toLowerCase());
});
fs.writeFileSync("{OUTPUT_DIR}/_people_to_enrich.jsonl", keep.join("\n") + "\n");
console.error(`Enriching ${keep.length} of ${lines.length} speakers`);
'
fi

# Split into ~5-person batches
split -l 5 {OUTPUT_DIR}/_people_to_enrich.jsonl {OUTPUT_DIR}/_batch_people_

Then in a single message, dispatch one Agent call per batch (up to 6 per message) with the prompt from references/workflow.md → "Person Enrichment". Each subagent's prompt should include:

• {SKILL_DIR}, {OUTPUT_DIR}, {DEPTH} (deep | deeper)

• {USER_COMPANY}, {USER_PRODUCT}, {ICP_DESCRIPTION}

• {EVENT_NAME} (from recon.json .title)

• {LANES} → 2 for deep mode, 4 for deeper mode (substituted into # browse call N/{LANES})

• {PEOPLE_BATCH} → contents of _batch_people_aa (each line a JSON record from people.jsonl)

Agent dispatch (skeleton, repeat per batch in one message):

Agent(
 description: "Person enrichment batch aa",
 prompt: ,
 subagent_type: "general-purpose"
)
Agent(
 description: "Person enrichment batch ab",
 prompt: ,
 subagent_type: "general-purpose"
)
... up to 6 per message

After all subagents return, verify the people files exist:

ls {OUTPUT_DIR}/people/*.md | wc -l
# Should equal wc -l _people_to_enrich.jsonl

Step 9: Compile Report

Generate the company-grouped HTML index, alternate views, and CSV in one command:

node {SKILL_DIR}/scripts/compile_report.mjs {OUTPUT_DIR} --open

This generates:

• {OUTPUT_DIR}/index.html — people grouped by company, ranked by company ICP score (opens in browser)

• {OUTPUT_DIR}/people.html — filterable speaker list (alternate view)

• {OUTPUT_DIR}/companies.html — ICP-ranked company table with attendees

• {OUTPUT_DIR}/results.csv — cold-outbound-ready spreadsheet

Then present a summary in chat:

## Event Prospecting Complete — {Event Name}

- **Total speakers extracted**: {count}
- **Unique companies**: {count}
- **ICP fits (score ≥ {threshold})**: {count}
- **Speakers enriched**: {count}
- **Score distribution** (companies):
 - Strong fit (8-10): {count}
 - Partial fit (5-7): {count}
 - Weak fit (1-4): {count}
- **Report opened in browser**: {OUTPUT_DIR}/index.html

Show the top 5 people cards as a markdown table sorted by company ICP score, then offer to:

• Adjust --icp-threshold and re-run Steps 6-9

• Export the CSV to a CRM