skills/capital/analyzing-insider-buying-signals/SKILL.md
Evaluates insider purchase patterns with cluster buying identification, historical signal analysis, and conviction scoring. Use when analyzing insider buying, assessing management confidence signals, or tracking insider activity patterns.
npx skillsauth add casemark/skills analyzing-insider-buying-signalsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Pull and clean Form 4 data — Extract all Section 16 filings for the target. Exclude derivative exercises, gifts, and automatic 10b5-1 plan transactions. Retain only discretionary open-market purchases and, separately, flag any open-market sales by the same insiders.
Classify each transaction
Identify cluster buying — Flag instances where 3+ insiders purchase within a 30-day window. Cluster buying is the single strongest insider signal. Note any contradictory signals (e.g., one insider selling while others buy).
Score conviction — Assign a composite conviction score (1–5 scale):
Backtest insider track record — Review prior insider purchases at this issuer over the past 3–5 years. Calculate hit rate (% of purchases followed by positive 6-month and 12-month excess returns vs. sector). Flag if insiders have a history of poorly timed purchases.
Contextualize against thesis — Map the insider signal against the broader investment thesis. Determine whether the buying confirms, contradicts, or is orthogonal to your catalyst thesis. Insider buying is a supporting factor, not a standalone thesis.
Compile output report — Structure findings per the Output section below.
Structure the analysis report with these sections:
development
name: automated-contract-summary language: en description: Generates structured executive summaries of contracts using ML — captures key terms, party obligations, risk allocations, and compliance requirements in a standardized format. Optimized for high-volume review where speed and consistency matter. tags: - summarization - agreement - corporate --- # Automated Contract Summarization Produces standardized executive summaries of contracts using machine learning, capturing essential term
tools
Extracts regulatory obligations from dense regulations across jurisdictions. Breaks down multi-level regulations into clear article-level obligations, classifies applicability to a business, and prioritizes by risk level. Use when translating regulations into actionable compliance requirements.
development
Continuously monitors regulatory landscapes for changes relevant to a specific business. Ingests global regulatory updates, filters by relevance, summarizes impact, and produces an actionable change advisory. Use when tracking regulatory developments affecting a particular product or market.
testing
Compares an organization's existing compliance controls, policies, and procedures against extracted regulatory obligations to identify coverage gaps. Produces a remediation plan with prioritized actions. Use when assessing compliance maturity or preparing for regulatory audits.