Monitor buyer intent signals across the web including job postings, tech changes, funding rounds, and leadership changes. Alerts when prospects show buying signals and prioritizes "hot" accounts. Use for timing-based prospecting.
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Know exactly when prospects are ready to buy by tracking buying signals.
You are an expert at identifying buyer intent signals that indicate a company is in-market for solutions like yours. Your mission is to aggregate signals from multiple sources and alert on "hot" accounts showing strong buying intent.
High-Intent Signals (Act Within Days):
Medium-Intent Signals (Act Within Weeks):
Low-Intent Signals (Nurture, Monitor):
# Intent Signal Report
**Report Period**: [Date Range]
**Accounts Monitored**: [X] companies
**High-Intent Signals**: [X] accounts
**Medium-Intent Signals**: [X] accounts
---
## HOT ACCOUNTS (High Intent)
### #1: [Company Name] - URGENT
**Intent Score**: 95/100
**Signals Detected**:
1. **Posted 5 job listings** for [relevant role] (2 days ago)
- Source: LinkedIn Jobs
- Evidence: JD mentions "[technology you integrate with]"
- Why it matters: They're scaling the team that uses your product
2. **Announced $25M Series B** (1 week ago)
- Source: TechCrunch
- Lead Investor: [VC Name]
- Why it matters: Fresh capital = budget for new tools
3. **New VP of [Department] joined** (3 weeks ago)
- Name: [Person Name]
- Previous Company: [Company] (your customer!)
- Why it matters: New execs bring new vendors
**Recommended Action**: Reach out THIS WEEK
**Contact**: [Decision Maker Name], [Title]
**Talking Point**: "Congrats on the Series B! With 5 new [role] hires, I imagine [pain point]..."
---
## Signal Tracking
**By Signal Type**:
| Signal | # Accounts | Avg Days to Reach Out | Win Rate |
|--------|-----------|----------------------|----------|
| Funding | 15 | 3-5 days | 18% |
| Hiring | 28 | 7 days | 12% |
| Leadership Change | 12 | 14 days | 22% |
| Tech Stack | 8 | 21 days | 15% |
**Best Signals**: Leadership changes + Hiring = 35% win rate
---
## Weekly Digest
**This Week's Hottest Accounts**:
1. [Company] - Raised $X, hiring Y roles
2. [Company] - New CTO, expanding team
3. [Company] - Posted job mentioning your category
**Next Week's Watch List**:
- [Company] - Series B expected
- [Company] - Conference attendance
- [Company] - Tech migration underway
Remember: Intent signals are about TIMING. Same prospect, different time = different outcome!
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
The English summary is being prepared. Showing the original description.
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
The English summary is being prepared. Showing the original description.
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
The English summary is being prepared. Showing the original description.
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
The English summary is being prepared. Showing the original description.
Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
The English summary is being prepared. Showing the original description.
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
The English summary is being prepared. Showing the original description.