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analyzing-insider-buying-signals

Evaluates insider purchase patterns with cluster buying identification, historical signal analysis, and conviction scoring. Use when analyzing insider buying, assessing management confidence signals,

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Imported from lev-os/agents (skills-db/legal/analyzing-insider-buying-signals/SKILL.md). Install upstream with npx skills add lev-os/agents --skill analyzing-insider-buying-signals. Copyright stays with the author.

Analyzing Insider Buying Signals

When To Use

  • Screening for management conviction signals ahead of catalyst events (earnings, restructurings, activist campaigns)
  • Evaluating whether insider open-market purchases indicate asymmetric upside vs. routine compliance-window buying
  • Building or validating an event-driven thesis where insider activity is a supporting data point
  • Monitoring cluster buying patterns across multiple insiders at the same issuer within a compressed timeframe

Inputs To Gather

  • SEC Form 4 filings for the target issuer (all reporting persons, at least trailing 12 months; 24 months preferred)
  • Insider identity and role: CEO, CFO, directors, 10%+ beneficial owners — role materially affects signal weight
  • Transaction details: open-market purchases vs. option exercises, gift transactions, or 10b5-1 plan activity (filter out non-discretionary transactions)
  • Purchase size: absolute dollar amount and percentage of insider's existing holdings (conviction proxy)
  • Company context: market cap, recent price performance, upcoming catalysts, and current valuation multiples
  • Historical insider accuracy: prior insider purchases at this issuer and subsequent 6/12-month returns
  • Peer/sector insider activity: whether cluster buying is isolated or part of a broader sector pattern

Workflow

  1. 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.

  2. Classify each transaction

    • Role weight: C-suite and operating executives > independent directors > 10%+ holders (unless activist)
    • Size weight: purchases exceeding 10% of the insider's prior holdings or >$500K absolute are high-conviction [VERIFY — adjust thresholds for micro-cap vs. large-cap]
    • Timing context: purchases within 30 days post-earnings release, during open windows, or shortly after price dislocations carry different interpretive weight
  3. 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).

  4. Score conviction — Assign a composite conviction score (1–5 scale):

    • 5 — Exceptional: CEO/CFO + multiple directors cluster buying at sizes >20% of prior holdings, no offsetting sales, near 52-week lows
    • 4 — Strong: Multiple C-suite purchases, meaningful dollar amounts, favorable timing
    • 3 — Moderate: Single senior insider purchase of material size, or director cluster buying
    • 2 — Weak: Small purchases, single director, or purchases during routine compliance windows with no unusual context
    • 1 — Noise: Minimal dollar amounts, recent option exercises creating apparent "buys," or 10b5-1 plan artifacts
  5. 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.

  6. 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.

  7. Compile output report — Structure findings per the Output section below.

Output

Structure the analysis report with these sections:

  • Executive Summary: One-paragraph signal assessment with conviction score and key takeaway
  • Transaction Detail Table: Date, insider name, role, shares purchased, price, dollar amount, % of prior holdings, transaction type
  • Cluster Buying Analysis: Timeline visualization of purchases, identification of clusters, offsetting sales
  • Conviction Score Breakdown: Score with rationale for each sub-factor (role, size, timing, cluster, track record)
  • Historical Accuracy: Table of prior insider purchases and subsequent returns at 3/6/12-month intervals
  • Contextual Factors: Catalysts, valuation, sector activity, any contradictory signals
  • Limitations and Caveats: Data gaps, assumptions made, factors that could reduce signal reliability

Quality Checks

  • Confirm all transactions are sourced from actual Form 4 filings, not aggregated third-party summaries that may omit transaction codes [VERIFY]
  • Verify that 10b5-1 plan transactions have been correctly excluded — check for footnotes in Form 4 indicating pre-planned trades
  • Ensure conviction scoring is consistent: same criteria applied across all insiders and time periods
  • Cross-check for contemporaneous insider sales that would undercut the bullish signal
  • Validate that historical return calculations use appropriate benchmarks (sector index, not just S&P 500)
  • Flag any insiders subject to pending SEC enforcement actions or restatement risk, which would contaminate the signal
  • Note that insider buying data has inherent reporting lags (Form 4 due within 2 business days but late filings are common) [VERIFY]

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/lev-os-agents-analyzing-insider-buying-signals/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

lev-os-agents-analyzing-insider-buying-signals.ocm.jsonjson
{
  "ocm": "1",
  "id": "lev-os-agents-analyzing-insider-buying-signals",
  "kind": "skill",
  "name": "analyzing-insider-buying-signals",
  "description": "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.",
  "publisher": "lev-os",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "analysis",
      "activist-and-event-driven-inve",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/lev-os/agents",
      "path": "skills-db/legal/analyzing-insider-buying-signals/SKILL.md",
      "ref": "62e97827ea88ff5dfa3efe76d19a8fa54d3da360",
      "url": "https://github.com/lev-os/agents/blob/62e97827ea88ff5dfa3efe76d19a8fa54d3da360/skills-db/legal/analyzing-insider-buying-signals/SKILL.md",
      "key": "lev-os/agents/skills-db/legal/analyzing-insider-buying-signals/SKILL.md"
    }
  },
  "instructions": "# Analyzing Insider Buying Signals\n\n## When To Use\n\n- Screening for management conviction signals ahead of catalyst events (earnings, restructurings, activist campaigns)\n- Evaluating whether insider open-market purchases indicate asymmetric upside vs. routine compliance-window buying\n- Building or validating an event-driven thesis where insider activity is a supporting data point\n- Monitoring cluster buying patterns across multiple insiders at the same issuer within a compressed timeframe\n\n## Inputs To Gather\n\n- **SEC Form 4 filings** for the target issuer (all reporting persons, at least trai",
  "cost": {
    "context_tokens": 1324
  }
}

Fetch it by URL: GET /api/v1/registry/lev-os-agents-analyzing-insider-buying-signals/manifest?version=1.0.0

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