event-prospecting

# 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. 0. **Setup** — output dir + clean slate 1. **Load profile** — read `profiles/{user_slug}.json` 2. **Recon** — detect event platform 3. **Extract people** — `people.jsonl` 4. **Group by company** — `seed_companies.txt` 5. **ICP triage** — fast company-level scoring (1 call/company) 6. **Filter** — companies with `icp_fit_score >= --icp-threshold` 7. **Deep research** — full Plan→Research→Synthesize on ICP fits 8. **Enrich speakers** — ask user: ICP-fit only (default) or all speakers 9. **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. ```bash # EVENT_URL came from the invocation message (whatever the user typed after `/event-prospecting`) 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 `$HOME`. Pass `{OUTPUT_DIR}` as the full literal path to all subagent prompts. ## Step 1: Load User Profile The profile defines the ICP that ICP triage and deep research score against. Load from `{SKILL_DIR}/profiles/{user_slug}.json` (interchangeable across all GTM skills — same shape as company-research). `example.json` is a template, not a real profile — never use it. **DO NOT look outside `{SKILL_DIR}/profiles/`** for profiles — never reach into other skills' directories. If a profile is needed elsewhere, the user copies it explicitly. **Resolution order**: 1. If the user invoked with `--user-company <slug>`, use that slug. 2. Else, list `profiles/*.json` excluding `example.json`. If exactly one profile exists, use it (and tell the user which one). If multiple exist, ask the user (plain chat) which one. 3. If zero profiles exist, **fail loudly** and instruct the user to create one (copy `profiles/example.json` to `profiles/<your_slug>.json` and fill it in, or run the company-research skill which builds one automatically). ```bash PROFILES=$(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: ```bash 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 ```bash 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: ```bash 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: ```bash 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. ```bash # 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: <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`: ```bash 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`: ```bash 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}`