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Researches individual competitors via web search to gather pricing, features, positioning, and reviews. Activates when the user wants to research a competitor, gather intel on a company, or asks 'what does [company] offer?' Covers multi-source intelligence gathering across official sites, review platforms, and community discussions.
Synthesizes competitive research into strategic insights, SWOT analyses, and positioning recommendations. Activates when the user wants a competitive analysis, market positioning review, competitor comparison matrix, or asks 'how do we stack up against the competition?' Identifies market gaps and strategic opportunities.
Extracts structured terms from legal contracts across 7 categories (Payment, Duration, IP, Confidentiality, Liability, Termination, Warranty). Activates when the user has a contract to analyze, wants to extract terms, review an agreement, or asks 'what does this contract say?' Supports PDF, DOCX, MD, and TXT formats with auto contract-type detection.
Evaluates contracts for harmful, unusual, or one-sided clauses using Red/Yellow/Green risk classification. Activates when the user wants to find contract red flags, assess legal risks, check for unfair terms, or asks 'is this contract safe to sign?' Provides plain-English explanations and concrete mitigation suggestions for freelancers and agency founders.
Creates and manages activity records in the CRM Communications database with AI-generated summaries. Activates when the user wants to log an email or meeting to CRM, record a client interaction, or asks 'save this to the client record.' Handles idempotent operations, deduplication, and AI summarization.
Matches email addresses and meeting attendees to CRM contacts using progressive matching. Activates when the user needs to identify which client an email belongs to, resolve a contact, or look up a client from an email address. Uses a 5-step algorithm from exact email match to fuzzy name matching with confidence scoring.
Orchestrates the full sync pipeline from Gmail and Calendar data to CRM Communications records in Notion. Activates when the user wants to sync emails or meetings to CRM, log client activities, or asks 'update my CRM with recent communications.' Handles single-item and batch sync with deduplication and error recovery.
| name | Competitive Research |
| description | Researches individual competitors via web search to gather pricing, features, positioning, and reviews. Activates when the user wants to research a competitor, gather intel on a company, or asks 'what does [company] offer?' Covers multi-source intelligence gathering across official sites, review platforms, and community discussions. |
| version | 1.0.0 |
Gather competitive intelligence on companies via systematic web search. This skill provides a surface-scan research strategy that retrieves current pricing, features, positioning, customer reviews, and recent news for any competitor in 4-6 targeted searches.
Execute 4-6 targeted searches per competitor to gather intelligence across five dimensions. This surface scan approach balances comprehensiveness with speed — enough queries to capture the key facts without exhaustive crawling.
Research dimensions (in order):
For company name input (e.g., "Notion"), construct the domain from the company name or find it in results. For URL input (e.g., "https://notion.so"), extract the company name and domain from the URL.
When the user provides multiple competitors (e.g., /founder-os:compete:matrix Notion Linear Asana), apply this skill independently to each company and merge outputs into a comparison structure. Process companies sequentially — complete all 5 dimensions for one company before starting the next to avoid cross-contamination of findings.
Use site: operators to target high-quality sources directly rather than relying on general search to surface them:
site:g2.com {{company_name}}, site:capterra.com {{company_name}}{{company_name}} pricing 2026, site:{{domain}} pricingsite:{{domain}} (homepage), site:{{domain}} about{{company_name}} funding OR launch OR "product update" 2026site: when casting a wider net (e.g., features often spread across documentation, blog posts, and landing pages)."product update", "contact sales")."Mercury" bank startup not just Mercury).Consult skills/compete/competitive-research/references/query-patterns.md for full templated queries for each dimension, including site-specific variants and matrix query patterns for multi-competitor research.
Extract pricing data and normalize to a comparable format:
Normalized format: $X/user/month (billed annually) or $X/month flat or Contact Sales.
When pricing is ambiguous or contradictory across sources, use the official product website as ground truth and flag discrepancies.
Categorize extracted features into three buckets:
Source features from: product pages, feature comparison pages, G2 review "pros" sections, ProductHunt listings, and changelog or blog posts. Do not infer features that are not explicitly documented — mark uncertain claims with "(unverified)".
Extract and record for each platform found:
Normalize all scores to a /5.0 scale for cross-competitor comparison. G2 and Capterra are natively /5.0. ProductHunt uses upvotes — record upvote count separately and do not convert to a score. Compute a composite score by averaging all available /5.0 platform scores, weighted equally.
When review counts are low (<50 reviews on a platform), flag the score as low-confidence.
From the competitor's homepage and about page, extract:
Note the tone of the messaging (technical, aspirational, pragmatic, community-driven) — this signals where they position on the market sophistication spectrum.
Surface and record:
Limit to the last 90 days. Record each item with: date, headline (paraphrased if necessary), source, and a one-line significance note (e.g., "Signals move into enterprise segment" or "Reduces pricing moat vs. lower-tier competitors").
Evaluate sources by reliability:
Prefer Tier 1-2 sources for pricing and feature claims. Accept Tier 3 for sentiment and complaint pattern analysis — acknowledge the source tier when citing. Discard Tier 4 sources unless no other source is available, in which case flag explicitly.
When the same fact appears across multiple sources:
Apply recency filters strictly:
After completing research for one competitor, structure findings as a data object. This object is consumed by the market-analysis skill or report generation commands:
competitor_data:
company: [name]
domain: [url]
research_date: [YYYY-MM-DD]
pricing:
model: [per_seat | flat | usage | freemium | enterprise_only | hybrid]
plans:
- name: [plan name]
price_monthly: [number or "Contact Sales"]
billing: [monthly | annual | annual_only]
notes: [key limitations or inclusions]
free_tier: [description or null]
enterprise_pricing: [true | false]
annual_discount_pct: [number or null]
features:
core:
- [feature name]
differentiating:
- feature: [name]
why_it_matters: [one-line explanation]
gaps:
- [missing or weak area]
reviews:
composite_score: [X.X / 5.0]
platforms:
- name: [G2 | Capterra | ProductHunt | Trustpilot]
score: [X.X / 5.0 or upvote count]
review_count: [number]
low_confidence: [true | false]
praise_themes:
- [theme]
complaint_themes:
- [theme]
positioning:
tagline: [verbatim H1 text]
value_prop: [subheadline text]
target_audience: [description]
messaging_angle: [core problem or transformation]
proof_elements: [logos, user counts, awards]
tone: [technical | aspirational | pragmatic | community-driven]
news:
recent_items:
- date: [YYYY-MM-DD]
headline: [text]
source: [publication or URL]
significance: [one-line note]
When running /founder-os:compete:matrix, produce one competitor_data object per company and pass the full array to the matrix formatter.
Apply these fallbacks when data is unavailable:
price_monthly: "Not publicly listed" and note the last search attempted.composite_score: null and note "no review platform data found".status: "limited_data" in the output object.Never fabricate pricing, feature claims, or review scores. When uncertain, use the phrase "unverified" and recommend the user check the source directly.