Files
Corey Haines f86637eace feat: add prospecting skill + truelist integration (#308)
* 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>
2026-05-26 11:20:26 -07:00

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.
version
2.0.0

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:

  1. Competitor URLs — the list of competitor website URLs to profile
  2. Your product — what you do (if not in product marketing context)
  3. Depth level — quick scan (key facts only) or deep profile (full research)
  4. 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.

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:

  1. Competitor landscape overview — one paragraph summarizing the competitive field
  2. Comparison table — key metrics side by side for all profiled competitors
  3. Positioning map — where each competitor sits (e.g., simple↔complex, cheap↔premium)
  4. Key takeaways — 3-5 strategic observations from the research
  5. 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:

  1. Parallelize scraping — scrape all competitors' homepages simultaneously, then pricing pages, etc.
  2. Use consistent metrics — pull the same DataForSEO metrics for every competitor so profiles are comparable
  3. Build the summary last — after all individual profiles are complete
  4. 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 Log section at the bottom

Task-Specific Questions

Only ask if not answered by context or input:

  1. What competitor URLs should I profile?
  2. Quick scan or deep profile?
  3. Any specific dimensions to focus on (pricing, SEO, positioning)?
  4. Should I compare findings against your product?

  • 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