| name | competitive-intel |
| version | 1.0.0 |
| description | Competitive intelligence on one or more companies — paid ads, organic post activity,
tech stack, tracking pixels, social footprint, and hiring signals. Compares multiple
competitors side-by-side when asked. Uses ad_search, website_intelligence, web_tech_stack,
post_keyword_search, and profile_activities on key executives. Proactively invoke on
"what ads is X running", "compare X vs Y", "what tech does X use", "competitive scan".
|
| benefits-from | ["richapi-gtm","account-research"] |
| allowed-tools | ["Bash","Read","Write","Edit","AskUserQuestion"] |
| triggers | ["what ads is X running","compare X vs Y","what tech does X use","competitive scan","competitive intel","look at their landing page"] |
competitive-intel
Quick, comparable, evidence-based snapshots of competitors.
Preamble
~/.claude/skills/richapi-gtm-skills/bin/richapi-skills-preflight
Phase 0 — define the question
Ask if unclear — the cost varies 10× by angle:
| The user really wants to know… | Use |
|---|
| "What ads are they running?" | ad_search + ad_details |
| "What's their tech stack?" | web_tech_stack (cheap) or website_intelligence (comprehensive) |
| "What are they posting about?" | post_keyword_search with fromCompany |
| "Who are their thought leaders?" | enrich_profile + profile_activities on 2-3 named execs |
| "How do we stack up vs them?" | Full compare mode — run a narrow version of all the above on each side |
Phase 1 — ad intelligence
ad_search (0.2/result) scoped to the target company. You can:
- Pass
advertiser: "<Company>" or LinkedIn Ad Library URL
- Add
dateRange to scope to last 30/90 days
- Add
country if geo-specific
After getting the list, run ad_details (2 credits each) on the top 2-3 most interesting ads only — don't burn credits on every ad.
What to extract
- Creative angles: what pains / promises / CTAs do the ads emphasize?
- Audience signals: targeting breakdown (if available) — industries, titles, seniority
- Cadence: how often they launch new creatives (churn rate signal)
- Channels: image vs video vs carousel share
Phase 2 — tech stack + GTM signals
For a fast read: web_tech_stack (2 credits) on their marketing site.
For the full picture: website_intelligence (5 credits) — one call, all of:
- Tech stack (100+ detectors)
- Pixels / analytics (Meta, LinkedIn, TikTok, etc. — reveals ad spend channels)
- Social profile links
- Emails on the site
- SSL / domain metadata
Rule: If the user wants >2 companies, website_intelligence is almost always the right call. Running tech_stack + pixels + social_links separately costs 2+1+2 = 5 anyway, and loses the aggregated view.
Interpreting pixels → GTM playbook
| Pixel seen | Implies |
|---|
| LinkedIn Insight Tag | Running LinkedIn Ads — high-intent B2B |
| Meta Pixel + Google Ads + TikTok | Multi-channel paid acquisition (DTC or SaaS-wide) |
| HubSpot / Marketo / Pardot | Running marketing automation → organized demand gen |
| Segment | Data-mature — tracks events to multiple downstream tools |
| Intercom / Drift / Zendesk | Support ops maturity |
| Cal.com / Calendly / Chili Piper | Sales booking flow (meeting-driven) |
Phase 3 — content activity
Last 30d posts from the company page: post_keyword_search with { fromCompany: "<companyUrn>", datePosted: "past-month" } (6 credits).
Ask before running — 6 credits is the biggest single line item here.
Extract themes:
- Product-led (feature launches, tutorials)
- Thought-leadership (opinions, research)
- Employer-brand (hiring, culture)
- Customer-led (case studies, testimonials)
Phase 4 — exec voice (optional)
For 1-3 named executives: enrich_profile + profile_activities (POST only).
Cost: 1 + 2 = 3 credits each. Cap at 3 execs = 9 credits.
What this tells you:
- Where they're consciously positioning themselves
- What they openly dislike about competitors (pay attention — they often name names)
- What customers they're engaging with publicly
Phase 5 — compare-mode synthesis
When comparing 2+ companies, output a side-by-side grid:
Acme Corp Beta Industries
Size (LinkedIn) 501-1000 201-500
Ad cadence (30d) 12 new creatives 3 new creatives
Ad angle "Save time" "Save money"
Pixels LI + Meta + HubSpot LI only
Tech maturity Enterprise Scrappy
Company posts (30d) 21 (product-led) 8 (thought-leadership)
Top exec voice CEO — thought lead CMO — product demos
Inferred strategy Demand gen at scale Founder-led, narrower ICP
Then a 2-3 sentence what-this-means interpretation for the user.
Cost ceiling
Sensible defaults per company:
- Light scan (ads + tech only): ~10 credits
- Standard compare: website_intelligence (5) + 5 ad_details (10) + company posts (6) = ~21 credits per company
- Deep dive: add exec voice (9) + fuller ad sweep = ~35 credits per company
State the bill before running. For compare mode with 3+ companies, always confirm.
Anti-patterns
- Don't run
ad_details on every ad — top 3-5 only.
- Don't interpret tech stack without seeing the site — pixels can be stale.
- Don't compare companies on metrics that vary by company-size (total posts, total employees) — normalize by headcount.
- Don't make claims about strategy you can't point at (ad, post, hire) as evidence. Always cite.
Follow-ups
Offer to schedule this
"Want this as a weekly intel report? Every Monday I can re-pull ads + posts + tech signals for these competitors and email you the diff vs last week. → scheduled-workflow."
Weekly competitor diffs catch campaign launches and positioning shifts within a week — any slower and the intel is a history lesson.
