* feat: add prospecting skill + truelist integration New skill: skills/prospecting/ - SKILL.md (251 lines, well under 500 limit): branch picker for SaaS / B2B / Local SMB, shared 5-phase framework (ICP -> discovery -> qualify -> score -> output), compliance guardrails, tool selection quick-picks, output formats - references/saas-prospecting.md: tech stack signals, funding/hiring triggers, SaaS-specific sources and qualification - references/b2b-prospecting.md: industry/firmographic signals, trigger events, decision-maker mapping, B2B-specific sources - references/local-prospecting.md: 4-tier website status classification, browser-assisted research workflow (generalized from the local-client- prospector pattern), proximity scoring - references/data-sources.md: deep dives on Apollo, Clay, ZoomInfo, Clearbit, Hunter, Snov, Truelist, LinkedIn Sales Nav, BuiltWith, Crunchbase, RB2B, with sequencing recommendations across the three branches - references/compliance.md: CAN-SPAM, GDPR, CASL, platform ToS (LinkedIn, Google Maps, Apollo/ZI/Clearbit), anti-patterns, audit checklist - evals/evals.json: 6 evals (2 SaaS, 2 B2B, 1 Local SMB, 1 deliverability) New integration: - tools/integrations/truelist.md: email deliverability validation (Deliverable / Risky / Undeliverable / Unknown classification) Registry + marketplace wiring: - tools/REGISTRY.md: truelist row + new Email Verification category section - .claude-plugin/marketplace.json: bumped to 2.1.0, prospecting added to plugin description - VERSIONS.md: prospecting 1.0.0 + 2.1.0 changelog entry - README.md: skill table re-synced, prospecting added to ASCII flow under Sales & GTM column All 41 skills pass validation. sync-skills.js is idempotent. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * feat(prospecting): add GitHub stargazers/forks/watchers as discovery channel Net-new in this commit: - tools/clis/github-prospects.js: zero-dep Node CLI with commands stargazers / forks / watchers / user / rate-limit. Pagination via Link header, optional --enrich for full profile data, --with-email / --with-company / --with-blog filters, --format csv|json output, --dry-run preview. Uses GITHUB_TOKEN for 5000/hr rate limit (vs 60/hr unauthenticated). - tools/integrations/github.md: integration guide covering auth, rate limits, endpoints, workflows for SaaS prospecting, compliance notes (public API, not scraping), CLI reference. Skill updates: - skills/prospecting/SKILL.md: added GitHub to the tool selection quick picks and to the tool integrations table. - skills/prospecting/references/saas-prospecting.md: added GitHub to Tier 3 buying signals plus a dedicated "GitHub prospecting pattern (when audience is developers)" subsection with end-to-end workflow. - skills/prospecting/references/data-sources.md: added GitHub deep-dive section between RB2B and Free fallbacks. Registry: - tools/REGISTRY.md: github row in Tool Index, new Developer Intent / GitHub category section. All 41 skills still pass validation. sync-skills.js still no-op. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * refactor(prospecting): apply review suggestions CLI hardening + optimization: - github-prospects.js: encodeURIComponent on username path interpolation (defense in depth; GitHub usernames are restricted enough that this is safe in practice, but good hygiene). - github-prospects.js: refactored enrichUsers to filter inline and support --target N early termination. Previously, --with-email on a 1000-star repo would enrich all 1000 users before filtering down to the ~50 that match. Now you can pass --target 25 to stop as soon as 25 matches are found, saving API quota on restrictive filters. - github.md: documented the new --target flag. Reverse cross-references (so prospecting is discoverable from sibling skills): - cold-email: added prospecting as the natural upstream skill - customer-research: added "Translating customer research into an ICP for outbound" hand-off to prospecting - competitor-profiling: distinguished from prospecting ("this skill does deep research on specific accounts; prospecting builds the initial list") All 41 skills still pass validation. sync-skills.js still no-op. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * fix(truelist): align integration doc with actual OpenAPI spec Source of truth: Truelist-Labs/truelist-openapi (OpenAPI 3.1). The earlier integration doc had inferred (and wrong) endpoint paths, request shapes, and status enum values. Corrected against the published spec: Base URL: https://api.truelist.io Endpoints (real): - POST /api/v1/verify_inline?email=... (sync single, email is query param) - POST /api/v1/verify (async bulk, body: {emails: [...]}) - GET /me (account info) Real email_state enum: - ok, email_invalid, risky, unknown, accept_all (not the inferred "Deliverable / Risky / Undeliverable / Unknown") Real email_sub_state enum: - email_ok, is_disposable, is_role, unknown_error, failed_smtp_check Also corrected: - Truelist has an official MCP server (Truelist-Labs/truelist-mcp) — was marked as MCP unavailable - Truelist has 7 official SDKs (Node, Python, Ruby, PHP, Go, Java, .NET) + framework integrations (Django, Laravel, Next.js, Rails, React, Svelte, Vue, WordPress) — was marked as SDK unavailable - Native integrations with Mailchimp, Klaviyo, HubSpot, Zapier, Make, n8n, Clay, Salesforce, ActiveCampaign, Brevo, ConvertKit, Drip, BigCommerce, Go High Level — was unlisted - Rate limits: 10 req/s per endpoint (was unspecified) Files updated: - tools/integrations/truelist.md: full rewrite against spec - tools/REGISTRY.md: MCP and SDK columns now show ✓ for truelist; classifier note in the Email Verification section reflects real enum values - skills/prospecting/evals/evals.json: eval #6 expected_output and assertions use real email_state values and mention the MCP server - skills/prospecting/references/data-sources.md: Truelist deep-dive uses real endpoint paths, real enum values, and lists the MCP/SDK ecosystem All 41 skills still pass validation. sync-skills.js still no-op. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * feat(prospecting): add Firecrawl + Browserbase for single-target site research Both tools are programmatic scrapers, but their use in prospecting is strictly bounded: extract content from individual public business sites (the prospect's own website URL), never from the platforms hosting them (Google Maps, LinkedIn, Yelp, Apollo, etc.). This matches the line drawn by the original local-client-prospector reference skill and our own compliance section. New integration docs: - tools/integrations/firecrawl.md: REST + MCP + SDKs (Node/Python/Go/Rust); scrape / map / crawl / extract / search endpoints; explicit "when NOT to use" section listing the prohibited platforms. - tools/integrations/browserbase.md: real Chromium via Playwright/Puppeteer or Stagehand (AI-friendly natural-language extraction); session recordings; useful when rendering or interaction is required. Prospecting skill updates: - SKILL.md: added Firecrawl + Browserbase to tool selection quick picks and tool integrations table. - references/data-sources.md: new "Firecrawl / Browserbase (single-target site research)" section between RB2B and Free fallbacks. Includes the compliance line inline so the framing isn't lost. - references/local-prospecting.md: optional "programmatic verification" paragraph in the browser research workflow — once you have a candidate's URL from manual Maps discovery, you can hit it programmatically. - references/compliance.md: anti-pattern #1 now explicitly clarifies that Firecrawl/Browserbase are fine for the prospect's own website but not for the platforms hosting prospects. Registry: - tools/REGISTRY.md: firecrawl + browserbase rows in Tool Index, new "Site Scraping (single-target only)" category section with the compliance framing in the agent recommendation. All 41 skills still pass validation. sync-skills.js still no-op. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
14 KiB
name, description, metadata
| name | description | metadata | ||
|---|---|---|---|---|
| competitor-profiling | When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement. |
|
Competitor Profiling
You are an expert competitive intelligence analyst. Your goal is to take a list of competitor URLs and produce comprehensive, structured competitor profile documents by combining live site scraping with SEO and market data.
Initial Assessment
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered.
Before profiling, confirm:
- Competitor URLs — the list of competitor website URLs to profile
- Your product — what you do (if not in product marketing context)
- Depth level — quick scan (key facts only) or deep profile (full research)
- Focus areas — any specific dimensions to prioritize (e.g., pricing, positioning, SEO strength, content strategy)
If the user provides URLs and context is available, proceed without asking.
Core Principles
1. Facts Over Opinions
Every claim in a profile should be traceable to a source — scraped page content, review data, or SEO metrics. Label inferences clearly.
2. Structured and Comparable
All profiles follow the same template so they can be compared side by side. Consistency matters more than completeness on any single profile.
3. Current Data
Profiles are snapshots. Always include the date generated. Flag anything that looks stale (e.g., "pricing page last updated 2023").
4. Honest Assessment
Don't exaggerate competitor weaknesses or downplay their strengths. Accurate profiles are useful profiles.
Saving Raw Data
Before synthesizing the profile, persist all raw scrape, SEO, and review data to disk so it can be re-read, audited, or re-used later without re-running expensive API calls.
Directory layout (relative to project root):
competitor-profiles/
├── raw/
│ └── <competitor-slug>/
│ └── <YYYY-MM-DD>/
│ ├── scrapes/ # one .md file per scraped page (homepage.md, pricing.md, ...)
│ ├── seo/ # one .json file per DataForSEO call (backlinks-summary.json, ranked-keywords.json, ...)
│ └── reviews/ # one .md or .json file per review source (g2.md, capterra.md, ...)
├── <competitor-slug>.md # final synthesized profile
└── _summary.md # cross-competitor summary
Rules:
<competitor-slug>is lowercase, hyphenated (e.g.responsehub,safe-base)<YYYY-MM-DD>is the date the data was pulled — supports re-running and diffing snapshots over time- Save each Firecrawl scrape as raw markdown to
scrapes/<page-name>.md - Save each DataForSEO response as raw JSON to
seo/<endpoint-name>.json - Save each review source to
reviews/<source>.md(cleaned text) or.json(raw) - Always create the date folder fresh on a new run; never overwrite a prior date's data
The synthesized profile (<competitor-slug>.md) should reference the raw data folder it was built from in its ## Raw Data Sources section.
Research Process
Phase 1: Site Scraping (Firecrawl)
For each competitor URL, scrape key pages to extract positioning, features, pricing, and messaging.
Step 1: Map the site
Use Firecrawl Map to discover the competitor's site structure and identify key pages:
firecrawl_map → competitor URL
From the map, identify and prioritize these page types:
- Homepage
- Pricing page
- Features / product pages
- About / company page
- Blog (top-level, for content strategy signals)
- Customers / case studies page
- Integrations page
- Changelog / what's new (if exists)
Step 2: Scrape key pages
Use Firecrawl Scrape on each identified page:
firecrawl_scrape → each key page URL
Save each result to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/scrapes/<page-name>.md before extracting fields.
Extract from each page:
| Page | What to Extract |
|---|---|
| Homepage | Headline, subheadline, value proposition, primary CTA, social proof claims, target audience signals |
| Pricing | Tiers, prices, feature breakdown per tier, billing options, free tier/trial details, enterprise pricing signals |
| Features | Feature categories, key capabilities, how they describe each feature, screenshots/demo signals |
| About | Founding story, team size, funding, mission statement, headquarters |
| Customers | Named customers, logos, industries served, case study themes |
| Integrations | Integration count, key integrations, categories |
| Changelog | Release velocity, recent focus areas, product direction signals |
Step 3: Scrape competitor reviews (optional but high-value)
Use Firecrawl Scrape or Firecrawl Search to find:
- G2 reviews page for the competitor
- Capterra reviews page
- Product Hunt launch page
- TrustRadius profile
Save each scraped review page to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/reviews/<source>.md. Then extract: overall rating, review count, common praise themes, common complaint themes, and 3-5 representative quotes.
Phase 2: SEO & Market Data (DataForSEO)
Use DataForSEO MCP tools to gather quantitative competitive intelligence. Save each raw response as JSON to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/seo/<endpoint-name>.json before parsing it into the profile. For the full list of MCP tools used in this skill (Firecrawl + DataForSEO) and example calls, see references/tool-reference.md.
Domain Authority & Backlinks
Use backlinks_summary to get:
- Domain rank / authority score
- Total backlinks
- Referring domains count
- Spam score
Use backlinks_referring_domains for:
- Top referring domains (quality signals)
- Link acquisition patterns
Keyword & Traffic Intelligence
Use dataforseo_labs_google_ranked_keywords to get:
- Total organic keywords ranking
- Keywords in top 3, top 10, top 100
- Estimated organic traffic
Use dataforseo_labs_google_domain_rank_overview for:
- Domain-level organic metrics
- Estimated traffic value
- Top keywords by traffic
Use dataforseo_labs_google_keywords_for_site to discover:
- What keywords they target
- Content gaps vs. your site
Competitive Positioning Data
Use dataforseo_labs_google_competitors_domain to find:
- Their closest organic competitors (may reveal competitors you haven't considered)
- Market overlap data
Use dataforseo_labs_google_relevant_pages to find:
- Their highest-traffic pages
- Content that drives the most organic value
Phase 3: Synthesis
Combine scraped content with SEO data to build the profile. Cross-reference claims (e.g., if they claim "10,000 customers" on site, check if their traffic/backlink profile supports that scale).
Output Format
Profile Document Structure
Generate one markdown file per competitor, saved to a competitor-profiles/ directory in the project root.
Filename: competitor-profiles/[competitor-name].md
For the full profile and summary templates: See references/templates.md
Each profile follows this structure:
# [Competitor Name] — Competitor Profile
**URL**: [website]
**Generated**: [date]
**Depth**: [quick scan / deep profile]
---
## At a Glance
| Metric | Value |
|--------|-------|
| Tagline | [from homepage] |
| Founded | [year] |
| Headquarters | [location] |
| Team size | [estimate] |
| Funding | [if known] |
| Domain rank | [from DataForSEO] |
| Est. organic traffic | [monthly] |
| Referring domains | [count] |
| Organic keywords | [count] |
---
## Positioning & Messaging
**Primary value proposition**: [headline + subheadline from homepage]
**Target audience**: [who they're speaking to, based on copy analysis]
**Positioning angle**: [how they position — e.g., "simplicity-first," "enterprise-grade," "all-in-one"]
**Key messaging themes**:
- [theme 1 — with source page]
- [theme 2]
- [theme 3]
---
## Product & Features
### Core capabilities
- [capability 1] — [brief description from their site]
- [capability 2]
- ...
### Notable differentiators
- [what they emphasize as unique]
### Integrations
- [count] integrations
- Key: [list top 5-10]
### Product direction signals
- [based on changelog / recent feature releases]
---
## Pricing
| Tier | Price | Key Inclusions |
|------|-------|---------------|
| [Free/Starter] | [price] | [what's included] |
| [Pro/Growth] | [price] | [what's included] |
| [Enterprise] | [price] | [what's included] |
**Billing**: [monthly/annual, discount for annual]
**Free trial**: [yes/no, duration]
**Notable**: [any pricing quirks — per-seat, usage-based, hidden costs]
---
## Customers & Social Proof
**Named customers**: [list notable logos]
**Industries**: [primary industries served]
**Case study themes**: [what outcomes they highlight]
**Review ratings**:
- G2: [rating] ([count] reviews)
- Capterra: [rating] ([count] reviews)
---
## SEO & Content Strategy
**Organic strength**:
- Estimated monthly organic traffic: [number]
- Organic keywords (top 10): [count]
- Organic traffic value: $[estimated]
**Top organic pages** (by estimated traffic):
1. [page URL] — [keyword] — [est. traffic]
2. [page URL] — [keyword] — [est. traffic]
3. [page URL] — [keyword] — [est. traffic]
**Content strategy signals**:
- Blog post frequency: [estimate]
- Primary content types: [guides, comparisons, templates, etc.]
- Content focus areas: [topics they invest in]
**Backlink profile**:
- Referring domains: [count]
- Top referring sites: [list 5]
- Link acquisition pattern: [growing/stable/declining]
---
## Strengths & Weaknesses
### Strengths
- [strength 1 — with evidence source]
- [strength 2]
- [strength 3]
### Weaknesses
- [weakness 1 — with evidence source]
- [weakness 2]
- [weakness 3]
---
## Competitive Implications for [Your Product]
**Where they're strong vs. us**: [areas where this competitor has an advantage]
**Where we're strong vs. them**: [areas where you have an advantage]
**Opportunities**: [gaps in their offering or positioning we can exploit]
**Threats**: [areas where they're improving or gaining ground]
---
## Raw Data Sources
- Homepage scraped: [date]
- Pricing page scraped: [date]
- SEO data pulled: [date]
- Review data pulled: [date, sources]
Summary Document
After profiling all competitors, generate a competitor-profiles/_summary.md that includes:
- Competitor landscape overview — one paragraph summarizing the competitive field
- Comparison table — key metrics side by side for all profiled competitors
- Positioning map — where each competitor sits (e.g., simple↔complex, cheap↔premium)
- Key takeaways — 3-5 strategic observations from the research
- Gaps and opportunities — where the market is underserved
Quick Scan vs. Deep Profile
Quick Scan (faster, lower cost)
- Scrape: homepage + pricing page only
- SEO: domain rank overview + ranked keywords summary
- Skip: reviews, technology stack, backlink details
- Output: abbreviated profile (At a Glance + Positioning + Pricing + SEO summary)
Deep Profile (comprehensive)
- Scrape: all key pages + review sites
- SEO: full backlink analysis + keyword intelligence + competitor discovery
- Include: technology stack, content strategy analysis, review mining
- Output: full profile template
Default to quick scan unless the user requests deep profiling or specifies a small number of competitors (3 or fewer).
Handling Multiple Competitors
When profiling more than one competitor:
- Parallelize scraping — scrape all competitors' homepages simultaneously, then pricing pages, etc.
- Use consistent metrics — pull the same DataForSEO metrics for every competitor so profiles are comparable
- Build the summary last — after all individual profiles are complete
- Prioritize by relevance — if the user has 10+ competitors, suggest profiling the top 5 first based on domain overlap or market similarity
Updating Profiles
Profiles are snapshots. When updating:
- Check pricing pages first (most volatile)
- Re-pull SEO metrics (traffic and rankings shift monthly)
- Scan changelog for product changes
- Update the "Generated" date
- Note what changed since last profile in a
## Change Logsection at the bottom
Task-Specific Questions
Only ask if not answered by context or input:
- What competitor URLs should I profile?
- Quick scan or deep profile?
- Any specific dimensions to focus on (pricing, SEO, positioning)?
- Should I compare findings against your product?
Related Skills
- competitors: For creating comparison/alternative pages from these profiles
- prospecting: For broader list-building qualification (this skill does deep research on specific accounts; prospecting builds the initial list)
- customer-research: For mining reviews and community sentiment in depth
- content-strategy: For using competitor content gaps to plan your own content
- seo-audit: For auditing your own site relative to competitors
- sales-enablement: For turning profiles into battle cards and sales collateral
- ads: For analyzing competitor ad strategies
- pricing: For deeper pricing analysis informed by competitor profiles