On completion, follow gtm-meta-skill Section 7 for proactive issue feedback and the session-sharing Yes/No consent step.

Competitive Social Listening

Given a list of LinkedIn company URLs + time window, produces:

• all_engagements.csv — every reaction/comment, full engager profile
• senior_engagements.csv — filtered to VP / CXO / Director / Sales / Marketing
• dashboard.html — self-contained HTML dashboard (open in browser, shareable)
• competitive_analysis.zip — all three packaged for sharing

Use the e2e script for full runs:

python ~/.claude/skills/competitive-social-listening/scripts/competitive_social_listening.py \
  --companies "Acme:https://www.linkedin.com/company/acme/:acme.com" \
              "Rival:https://www.linkedin.com/company/rival/:rival.com" \
  --output /tmp/comp_run --months 3 --employees 10

# Test mode (1 company, 2 employees, 5 posts):
python competitive_social_listening.py --test --output /tmp/comp_test

Or follow the manual steps below if you need to debug or resume a partial run.

---

Pipeline Architecture

Input: [company LinkedIn URLs + time window]
         │
         ├─── ⚡ PARALLEL ──────────────────────────────────────┐
         │    Phase A: Employee discovery (dropleads)           │
         │    → employee LinkedIn URLs per company              │
         │                                                       │
         │    Phase B: Company page posts (harvestapi)          │
         │    → starts immediately, no need to wait for A       │
         └──────────────────────────────────────────────────────┘
         │
         ▼
    all_urls.csv (company pages + employee profiles)
         │
         ▼
    Full post scrape → posts_data.csv
    [harvestapi/linkedin-company-posts, no reactions, fast]
         │
         ▼
    Filter high-engagement posts (≥5 reactions or ≥2 comments)
         │
         ├─── ⚡ PARALLEL (max 12 concurrent) ────────────────┐
         │    10-post batches → async apify_run_actor         │
         │    viral posts (>300 reactions) → separate batch   │
         │    → datasetIds collected as_completed()           │
         └────────────────────────────────────────────────────┘
         │
         ▼
    Download all datasets → parse + deduplicate
         │
         ▼
    all_engagements.csv + senior_engagements.csv + dashboard.html

---

Prerequisites

deepline backend start   # always required before any deepline tool execute
deepline billing balance  # check credits before starting

Set your working directory:

export WORKDIR=/tmp/comp_$(date +%Y%m%d)
mkdir -p $WORKDIR

---

Step 1: Collect Inputs → companies.csv

Accept from user:

• List of company LinkedIn URLs
• Time window (default: 3 months)
• Max employees per company (default: 10)
• Seniority filter (default: VP, CXO, Director, Sales, Marketing)

Create seed file:

company_name,company_linkedin,domain
Acme,https://www.linkedin.com/company/acme/,acme.com
Rival,https://www.linkedin.com/company/rival/,rival.com

If domain is unknown, resolve via Apollo (optional — dropleads works without domain if you have LinkedIn URL):

deepline tools execute apollo_organization_search --payload \
  '{"q_organization_name": "Acme Corp", "per_page": 1}'
# Look for .data.organizations[0].primary_domain
# Apollo returns "organizations" NOT "accounts" — critical

---

Step 2: Employee Discovery → all_urls.csv

Provider: dropleads (not harvestapi/linkedin-company-employees — that actor returns empty)

Run for each company (fire all companies in parallel — don't wait for one before starting the next):

deepline tools execute dropleads_search_people --payload '{
  "filters.companyDomains": ["acme.com"],
  "filters.seniority": ["C-Level", "VP", "Director"],
  "filters.departments": ["Sales", "Marketing"],
  "pagination.limit": 10
}'

⚠️ Field names use dot notation: filters.companyDomains, NOT companyDomains. filters.seniority, NOT seniorities. pagination.limit, NOT limit.

⚠️ Response is raw text with a CSV path, not JSON:

/tmp/output_1772593757336.csv (8 rows)
columns: ["id", "fullName", "title", "linkedinUrl", ...]

Extract the CSV path with re.search(r'(/tmp/\S+\.csv)', output) and read it with Python's csv.DictReader.

Build $WORKDIR/all_urls.csv:

company,linkedin_url,description
Acme,https://www.linkedin.com/company/acme/,Company Page
Acme,https://www.linkedin.com/in/jane-smith,Jane Smith - VP Sales
Rival,https://www.linkedin.com/company/rival/,Company Page
...

This file is the source of truth for company → handle mapping used in Step 5.

---

Step 3: Scrape Posts (No Reactions) → posts_data.csv

One actor run for all URLs. Do not request reactions here — scraping reactions inline causes CLI timeout (actor runtime >90s).

deepline tools execute apify_run_actor --payload '{
  "actorId": "harvestapi/linkedin-company-posts",
  "input": {
    "targetUrls": [
      "https://www.linkedin.com/company/acme/",
      "https://www.linkedin.com/in/jane-smith"
    ],
    "postedLimit": "3months",
    "maxPosts": 50,
    "scrapeReactions": false,
    "scrapeComments": false
  }
}'

⚠️ postedLimit valid values: "month", "3months", "6months", "year". NOT "1month" or integers.

Wait for status: SUCCEEDED, capture defaultDatasetId. Download:

deepline tools execute apify_get_dataset_items \
  --payload '{"datasetId": "DATASET_ID", "params": {"limit": 5000}}' \
  > $WORKDIR/posts_raw.json

Parse the response (deepline CLI prepends a status header — skip to first { or [):

import re, json
with open(f"{WORKDIR}/posts_raw.json") as f:
    content = f.read()
m = re.search(r'(\{|\[)', content)
items = json.loads(content[m.start():])

Actual field names in Apify response items:

Field	Actual key	Common mistake
Post URL	linkedinUrl	postUrl ✗
Post text	content	text ✗
Author name	author["name"] (dict)	authorName ✗
Reactions	engagement["likes"] (dict)	totalReactions ✗
Comments	engagement["comments"] (dict)	totalComments ✗
Post date	postedAt	posted_at ✗

⚠️ source_url is always empty in post results — never use it for company mapping. Instead: extract the LinkedIn handle from linkedinUrl and look it up in all_urls.csv.

def extract_handle(url):
    url = url.rstrip("/")
    m = re.search(r"/(?:in|company|pub)/([^/?#]+)", url)
    if m: return m.group(1).lower()
    m = re.search(r"/posts/([^_]+)_", url)   # handle from post URLs
    if m: return m.group(1).lower()
    return None

Save as posts_data.csv. Filter high-engagement posts (≥5 reactions or ≥2 comments) → high_engagement_posts.csv.

---

Step 4: Batch Reaction Scraping

4.1 Build Batches

Key insight: harvestapi/linkedin-company-posts accepts individual post URLs as targetUrls. 10 post URLs ≈ 15s run — well within CLI timeout.

# Separate viral posts before batching
normal = [p for p in posts if p["total_reactions"] <= 300]
viral  = [p for p in posts if p["total_reactions"] > 300]

# Normal posts: 10 per batch, 100 max reactions
batches = [normal[i:i+10] for i in range(0, len(normal), 10)]

# Viral posts: 5 per batch, 50 max reactions (prevents timeout)
viral_batches = [viral[i:i+5] for i in range(0, len(viral), 5)]

Each batch config:

{
  "actorId": "harvestapi/linkedin-company-posts",
  "input": {
    "targetUrls": ["https://www.linkedin.com/posts/acme_..."],
    "scrapeReactions": true,
    "maxReactions": 100,
    "scrapeComments": true,
    "maxComments": 50
  }
}

4.2 Run Batches — Async, Max 12 Parallel

Always use apify_run_actor (async), never apify_run_actor_sync — reaction scraping exceeds the ~90s CLI timeout.

from concurrent.futures import ThreadPoolExecutor, as_completed

def run_batch(batch_config, idx):
    raw = subprocess.run(
        ["deepline", "tools", "execute", "apify_run_actor",
         "--payload", json.dumps(batch_config)],
        capture_output=True, text=True, timeout=180
    )
    output = raw.stdout + raw.stderr
    # Extract datasetId from output
    m = re.search(r'"defaultDatasetId"\s*:\s*"([A-Za-z0-9]+)"', output)
    return m.group(1) if m else None

# Run in groups of 12 — >15 concurrent causes 2-3 timeouts
with ThreadPoolExecutor(max_workers=12) as ex:
    futures = {ex.submit(run_batch, b, i): i for i, b in enumerate(batches)}
    dataset_ids = []
    for future in as_completed(futures):   # process as each finishes, don't wait for all
        ds_id = future.result()
        if ds_id:
            dataset_ids.append(ds_id)

⚠️ If a batch times out: the Apify run likely completed on the server — only the CLI wait timed out. Re-run that batch alone (not in parallel with others). It will finish in ~15s on retry.

4.3 Download All Datasets

Fire dataset downloads in parallel (all independent):

def download_dataset(ds_id):
    result = subprocess.run(
        ["deepline", "tools", "execute", "apify_get_dataset_items",
         "--payload", json.dumps({"datasetId": ds_id, "params": {"limit": 2000}})],
        capture_output=True, text=True, timeout=120
    )
    output = result.stdout + result.stderr
    m = re.search(r'(\{|\[)', output)
    if not m: return []
    data = json.loads(output[m.start():])
    if isinstance(data, dict): return data.get("data", data.get("items", []))
    return data if isinstance(data, list) else []

# Download all in parallel
with ThreadPoolExecutor(max_workers=10) as ex:
    all_items = []
    for items in ex.map(download_dataset, dataset_ids):
        all_items.extend(items)

---

Step 5: Parse Engagements → CSVs

Deduplicate on (post_url, actor_id, engagement_type). Classify seniority from position title:

SENIOR_PATTERNS = {
    "C-Suite/Exec": [r"\bceo\b", r"\bcto\b", r"\bcfo\b", r"\bcoo\b", r"\bcmo\b", r"\bcro\b",
                     r"\bcpo\b", r"\bciso\b", r"\bchief\b", r"\bfounder\b", r"\bco-founder\b",
                     r"\bpresident\b", r"\bowner\b", r"\bpartner\b", r"\bprincipal\b"],
    "VP":           [r"\bvp\b", r"\bvice president\b", r"\bsvp\b", r"\bevp\b", r"\brvp\b"],
    "Director":     [r"\bdirector\b"],
    "Sales":        [r"\bsales\b", r"\baccount executive\b", r"\bbdr\b", r"\bsdr\b", r"\brevenue\b"],
    "Marketing":    [r"\bmarketing\b", r"\bgrowth\b", r"\bdemand gen\b", r"\bcontent\b", r"\bbrand\b"],
}

Output columns (both CSVs): company, post_url, post_author, post_date, post_total_reactions, post_total_comments, post_content_preview, engagement_type, reaction_type, comment_text, engagement_date, engager_name, engager_linkedin_url, engager_position, engager_title_category, is_senior, engager_picture_url

---

Step 6: Dashboard + Package

Run build_dashboard.py → dashboard.html (Chart.js via CDN, self-contained, no server needed):

• Stat cards: senior engagements, all engagements, unique engagers, reactions, comments
• Bar: by company | Donut: title category mix | Bar: reaction types | Line: monthly trend
• Top posts table (ranked by senior engagement count)
• Top 20 engager table — ⚡ highlights multi-company engagers (highest-signal prospects)

⚠️ f-string + JS braces conflict: if building HTML template in Python, use a string placeholder (__DATA_JSON__) and .replace() instead of an f-string. Minified JS {{ inside an f-string causes SyntaxError: single '}' is not allowed.

cd $WORKDIR && zip -j competitive_analysis.zip dashboard.html all_engagements.csv senior_engagements.csv

---

Speed Architecture

These are the proven parallelism wins for this workflow. Each reduces wall-clock time without adding complexity.

1. Fan-out employee discovery

Fire dropleads for all companies simultaneously (ThreadPoolExecutor), not one-by-one. 3 companies = same time as 1.

2. Overlap employee discovery with company page post scraping

Company-page post scraping doesn't need employee URLs. Start the first actor run for company pages the moment you have them — don't wait for dropleads to finish.

3. as_completed() for reaction batches

Don't wait for an entire group to finish before starting dataset downloads. Start downloading the first completed batch immediately while others are still running.

4. Parallel dataset downloads

All apify_get_dataset_items calls are independent. Run them all concurrently (max 10 parallel — Apify rate limit).

5. Progressive CSV writing

Append engagement rows to CSVs as each dataset is parsed rather than holding all data in memory. Reduces peak memory for large runs (>50k rows).

6. Speculative file reads (Claude Code pattern)

When running in an agent loop: fire all tool calls that are independent in a single message. Don't wait for file A before reading file B if they're not dependent.

Why best-in-class agents are faster

Technique	Description	Applied here
Parallel tool dispatch	Fire all independent API calls in one message/thread	Fan-out dropleads + Apify simultaneously
PTC (Programmatic Tool Calling)	Write code to loop over tool calls, avoiding model round-trips	E2E script replaces 50+ agent turns
Speculative prefetch	Read files you'll likely need before confirmed you need them	all_urls.csv written early, reused later
Sub-agent isolation	Spawn agents for independent work streams with no shared context	ThreadPoolExecutor batches = sub-agent equivalent
Minimal context passing	Pass IDs and file paths between steps, not data blobs	Only datasetId strings passed between phases
Fail fast with known errors	Encode all known failure modes as input validation	postedLimit map, viral batch detection, header-skip parser

---

Pitfalls Reference

#	Symptom	Root cause	Fix
1	0 employees returned	Wrong dropleads field names	Use filters.companyDomains, filters.seniority, pagination.limit (dot notation)
2	dropleads JSON parse fails	Response is raw text + CSV path, not JSON	Use deepline_raw() + re.search(r'(/tmp/\S+\.csv)', output)
3	0 unique posts parsed	postUrl field doesn't exist	Use linkedinUrl; author is author["name"] (dict); engagement is engagement["likes"]
4	Posts actor 422 error	Invalid postedLimit value	Use "month", "3months", "6months", "year" only
5	Employee actor returns empty	Wrong actor	harvestapi/linkedin-company-employees is broken; use dropleads_search_people
6	Reaction batch exit code 4	apify_run_actor_sync timeout	Use async apify_run_actor, capture defaultDatasetId, download separately
7	2-3 batches timeout when >15 parallel	Apify server-side throttle	Max 12 parallel; retry timed-out batches solo
8	Viral post batch always times out	>300-reaction posts + maxReactions:100	Separate batch: maxReactions: 50, batch_size 5
9	JSONDecodeError: Expecting value on ds files	deepline CLI prepends status header	`re.search(r'({
10	Can't map posts to companies	source_url always empty in Apify output	Extract handle from linkedinUrl, match against all_urls.csv
11	SyntaxError: single '}' not allowed	JS braces in Python f-string	Use __DATA_JSON__ placeholder + .replace()
12	_csv.Error: field larger than field limit	Large JSON in deepline enrich CSV cells	Don't route Apify data through deepline enrich; use apify_get_dataset_items directly

---

Cost Estimation

Run before starting:

def estimate_cost(n_companies, employees_per_company=10, months=3):
    urls = n_companies * (1 + employees_per_company)
    posts = urls * 15                           # ~15 posts/URL avg
    high_eng = int(posts * 0.78)                # 78% pass engagement filter (validated)
    batches = (high_eng // 10) + 1

    # Apify: ~$0.002 per dataset item
    post_items = posts
    reaction_items = high_eng * 150             # reactions + comments per post
    apify_usd = (post_items + reaction_items) * 0.002

    # Dropleads: ~5 Deepline credits per employee
    dl_credits = n_companies * employees_per_company * 5

    print(f"URLs: {urls} | Posts: ~{posts} | High-eng: ~{high_eng} | Batches: {batches}")
    print(f"Apify: ~${apify_usd:.0f} | Deepline credits: ~{dl_credits}")
    return apify_usd, dl_credits

estimate_cost(3)   # 3 companies, 10 employees each
# → Apify: ~$23 | Deepline credits: ~150

Validated actuals (3-company run, Vooma/GoAugment/TryPallet):

• 25 URLs → 268 posts → 209 high-engagement → 21 batches
• 8,855 total engagements, 4,131 senior (49%)
• Apify: ~$20-30 | Deepline credits: ~75-150

Always run deepline billing balance first. Pilot one batch before committing to full run.