Tracks Smart Money market concentration across all Hyperliquid assets and flags assets accelerating up the ranks before they become crowded top-3 plays. By the time an asset hits the top of the SM leaderboard, the easy money is gone. This catches the trajectory.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionemerging-moversExecute the skills CLI command in your project's root directory to begin installation:
Fetches emerging-movers from senpi-ai/senpi-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate emerging-movers. Access via /emerging-movers in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Tracks Smart Money market concentration across all Hyperliquid assets and flags assets accelerating up the ranks before they become crowded top-3 plays. By the time an asset hits the top of the SM leaderboard, the easy money is gone. This catches the trajectory.
One API call per scan. Near-zero LLM tokens. Runs every 60 seconds.
Senpi's leaderboard_get_markets returns all assets ranked by percentage of total Smart Money profit in the last 4-hour rolling window. This isn't trader count — it's where the money is actually flowing.
#1 ETH SHORT 31.4% 286 traders
#2 BTC SHORT 25.1% 436 traders
#3 HYPE SHORT 24.2% 330 traders
...
#36 ASTER SHORT 0.2% 18 traders ← 60s later: #13, 0.82%, 65 traders
The script tracks this leaderboard over time and detects acceleration.
| Signal | Condition | Priority |
|---|---|---|
| IMMEDIATE_MOVER | 10+ rank jump from #25+ in ONE scan | Highest — act now |
| NEW_ENTRY_DEEP | Appears in top 20 from nowhere | Very high |
| CONTRIB_EXPLOSION | 3x+ contribution increase in one scan | Very high |
| DEEP_CLIMBER | 5+ rank jump from #25+ | High |
| Signal | Condition |
|---|---|
| NEW_ENTRY | First appearance in top 50 |
| RANK_UP | Jumped 2+ positions in one scan |
| CLIMBING | 3+ positions up over several scans |
| ACCEL | Contribution % increasing scan-over-scan |
| STREAK | Consistently climbing every check |
| VELOCITY | Sustained positive contribution growth |
These prevent false IMMEDIATE signals that looked great on rank jump alone but failed on execution:
| Filter | Rule | Rationale |
|---|---|---|
| Erratic rank | >5 rank reversals in history → erratic: true, downgraded |
Bouncing ranks are noise |
| Velocity gate | contribVelocity < 0.03 → lowVelocity: true, excluded from IMMEDIATE |
No momentum behind the move |
| Trader count floor | <10 traders → SKIP IMMEDIATE | Single whale risk |
| Max leverage check | max leverage < 10x → SKIP | Not worth the limited position sizing |
See references/quality-filters.md for implementation details and real-world examples.
┌────────────────────────────────────┐
│ Cron: every 60 seconds │
├────────────────────────────────────┤
│ scripts/emerging-movers.py │
│ • Loads scan history from JSON │
│ • Fetches leaderboard (1 API call) │
│ • Parses top 50 markets │
│ • Compares with previous scans │
│ • Detects signals + v3.1 filters │
│ • Saves updated history │
│ • Outputs JSON with alerts │
├────────────────────────────────────┤
│ Agent reads output: │
│ • IMMEDIATE alerts → evaluate now │
│ • Deep climbers → queue for review │
│ • No alerts → silent │
└────────────────────────────────────┘
| File | Purpose |
|---|---|
scripts/emerging-movers.py |
Scanner script |
emerging-movers-history.json |
Auto-managed scan history (last 60 scans) |
max-leverage.json |
Optional: asset max leverage reference |
See references/output-schema.md for the complete JSON schema.
Key top-level fields: alerts[], topMovers[], immediateMovers[], deepClimbers[], scanCount, timestamp.
Per-alert fields: asset, direction, rank, prevRank, contribution, traderCount, reasons[], contribVelocity, isImmediate, isDeepClimber, erratic, lowVelocity.
*/1 * * * * python3 scripts/emerging-movers.py
isImmediate: true + erratic: false + lowVelocity: false → Evaluate immediately for entry via ScannerisDeepClimber: true → Queue for next scanner runerratic: true or lowVelocity: true → Log but do not actMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
emerging-movers has been reliable in day-to-day use. Documentation quality is above average for community skills.
emerging-movers reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: emerging-movers is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend emerging-movers for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
emerging-movers reduced setup friction for our internal harness; good balance of opinion and flexibility.
emerging-movers fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend emerging-movers for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: emerging-movers is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for emerging-movers matched our evaluation — installs cleanly and behaves as described in the markdown.
We added emerging-movers from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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