| name | event-prospecting |
| description | Event prospecting skill. Takes a conference / event speakers URL,
extracts the people, filters their companies against the user's
ICP, then deep-researches only the speakers at ICP-fit companies.
Outputs a person-first HTML report where each card answers "why
should the AE talk to this person?" with all public links and a
one-click DM opener.
Use when the user wants to: (1) find leads at a specific
conference, (2) prep for an event, (3) research event speakers,
(4) build a target list from a sponsor/exhibitor page,
(5) scrape conference speakers and rank by ICP fit.
Triggers: "find leads at {event}", "research speakers at",
"prospect this conference", "stripe sessions leads",
"ai engineer summit prospects", "event prospecting",
"scrape conference speakers", "who should I meet at".
|
| license | MIT |
| compatibility | Requires browse CLI (`npm install -g browse`) and BROWSERBASE_API_KEY env var. The same `browse` binary covers both API commands and JS-rendered page fallback. |
| allowed-tools | Bash Agent AskUserQuestion |
| metadata | {"author":"browserbase","version":"0.1.0"} |
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 0: Setup Output Directory
Derive the output directory from the URL the user gave you. Do NOT hardcode any event name.
EVENT_SLUG=$(node -e 'const h = new URL(process.argv[1]).hostname.replace(/^www\./,""); console.log(h.split(".")[0])' "$EVENT_URL")
TIMESTAMP=$(date +%Y-%m-%d-%H%M)
OUTPUT_DIR=/Users/jay/Desktop/${EVENT_SLUG}_prospects_${TIMESTAMP}
mkdir -p "$OUTPUT_DIR/companies" "$OUTPUT_DIR/people"
Use the full literal home path — never ~ or $HOME. Pass {OUTPUT_DIR} as the full literal path to all subagent prompts.
Step 1: Load User Profile
The profile defines the ICP that ICP triage and deep research score against. Load from {SKILL_DIR}/profiles/{user_slug}.json (interchangeable across all GTM skills — same shape as company-research). example.json is a template, not a real profile — never use it.
DO NOT look outside {SKILL_DIR}/profiles/ for profiles — never reach into other skills' directories. If a profile is needed elsewhere, the user copies it explicitly.
Resolution order:
- If the user invoked with
--user-company <slug>, use that slug.
- Else, list
profiles/*.json excluding example.json. If exactly one profile exists, use it (and tell the user which one). If multiple exist, ask the user (plain chat) which one.
- If zero profiles exist, fail loudly and instruct the user to create one (copy
profiles/example.json to profiles/<your_slug>.json and fill it in, or run the company-research skill which builds one automatically).
PROFILES=$(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/<your_slug>.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 <slug> 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.
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|${slug}`;
});
fs.writeFileSync("{OUTPUT_DIR}/_seed_with_urls.txt", lines.join("\n") + "\n");
'
split -l 10 {OUTPUT_DIR}/_seed_with_urls.txt {OUTPUT_DIR}/_batch_triage_
ls {OUTPUT_DIR}/_batch_triage_* | wc -l
Then in a single message, dispatch one Agent call per batch (up to 6 in parallel; subsequent waves after the first returns). Each Agent gets the prompt from references/workflow.md → "ICP Triage" with these substitutions before sending:
{SKILL_DIR} → full literal skill path (e.g. /Users/jay/skills/skills/event-prospecting){OUTPUT_DIR} → full literal output path{USER_COMPANY}, {USER_PRODUCT}, {ICP_DESCRIPTION} → from the loaded profile{EVENT_NAME} → recon.json .title{COMPANY_LIST} → contents of the batch file (e.g. cat {OUTPUT_DIR}/_batch_triage_aa){TOTAL} → number of lines in this batch (substitute into # browse call N/{TOTAL})
Agent dispatch (skeleton, repeat per batch in one message):
Agent(
description: "ICP triage batch aa",
prompt: <ICP Triage prompt from workflow.md with all placeholders substituted>,
subagent_type: "general-purpose"
)
Agent(
description: "ICP triage batch ab",
prompt: <same prompt template, COMPANY_LIST swapped to batch ab>,
subagent_type: "general-purpose"
)
... up to 6 per message
After all subagents return, verify every company in seed_companies.txt has a corresponding companies/{slug}.md:
ls {OUTPUT_DIR}/companies/*.md | wc -l
Clean up the batch files: rm {OUTPUT_DIR}/_batch_triage_*.
Step 6: Filter by ICP Threshold
Read each companies/*.md frontmatter, keep those with icp_fit_score >= 6 (or whatever --icp-threshold is). Write the surviving company slugs to {OUTPUT_DIR}/icp_fits.txt:
THRESHOLD=6
for f in {OUTPUT_DIR}/companies/*.md; do
score=$(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)
while read slug; do
website=$(awk '/^website:/{print $2; exit}' {OUTPUT_DIR}/companies/${slug}.md)
echo "${slug}|${website}"
done < {OUTPUT_DIR}/icp_fits.txt > {OUTPUT_DIR}/_deep_targets.txt
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: <Deep Research prompt from workflow.md with all placeholders substituted; COMPANY_LIST = cat _batch_deep_aa>,
subagent_type: "general-purpose"
)
Agent(
description: "Deep research batch ab",
prompt: <same template, COMPANY_LIST = cat _batch_deep_ab>,
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
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=$(wc -l < {OUTPUT_DIR}/people.jsonl)
ICP_FITS=$(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 want = new Set(Object.values(slug2name).map(s => 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=2
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, ~$((TOTAL * LANES)) calls" }
]
}
])
Save the chosen scope as ENRICH_SCOPE=icp_fits or ENRICH_SCOPE=all. If the user picks "All speakers" and TOTAL × LANES > 600, print a warning and ask once more — that's a 10+ minute run with hundreds of tool calls.
Step 8b — Filter and batch
if [ "$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 -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: <Person Enrichment prompt from workflow.md with all placeholders substituted; PEOPLE_BATCH = cat _batch_people_aa>,
subagent_type: "general-purpose"
)
Agent(
description: "Person enrichment batch ab",
prompt: <same template, PEOPLE_BATCH = cat _batch_people_ab>,
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
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
