Last updated: 2026-01-17 16:36 UTC
Works with
Active patterns: 30
Total samples: 5095
Confidence threshold: 60%
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionentry-signalsExecute the skills CLI command in your project's root directory to begin installation:
Fetches entry-signals from 0xhubed/agent-trading-arena 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 entry-signals. Access via /entry-signals 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
0
total installs
0
this week
4
GitHub stars
0
upvotes
Run in your terminal
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this week
4
stars
Last updated: 2026-01-17 16:36 UTC Active patterns: 30 Total samples: 5095 Confidence threshold: 60%
These entry signals have been learned from competition data:
| Signal | Success Rate | Samples | Confidence | Seen |
|---|---|---|---|---|
| Multi-timeframe bullish alignment (... | 88% | 164 | 85% | 1x |
| Multi-timeframe bullish alignment (... | 85% | 157 | 75% | 1x |
| Multi-timeframe bullish alignment (... | 85% | 164 | 80% | 1x |
| Multi-timeframe bullish alignment (... | 85% | 164 | 85% | 1x |
| SMA crossover + bullish MACD + neut... | 82% | 184 | 80% | 1x |
| SMA crossover + bullish MACD + neut... | 82% | 184 | 85% | 1x |
| SMA crossover + bullish MACD + neut... | 82% | 184 | 85% | 1x |
| Scaling into existing winning posit... | 80% | 157 | 75% | 1x |
| Scaling into existing winning posit... | 78% | 164 | 80% | 1x |
| Multi-timeframe bullish alignment (... | 75% | 89 | 95% | 1x |
| Multi-timeframe bearish alignment f... | 65% | 103 | 95% | 1x |
| Relative strength divergence (one a... | 45% | 79 | 60% | 1x |
| SMA and MACD bearish signals for sh... | 35% | 50 | 60% | 1x |
| High funding rate alone as bullish ... | 35% | 248 | 75% | 1x |
| High funding rate alone as bullish ... | 35% | 248 | 80% | 1x |
| High funding rate alone as bullish ... | 35% | 247 | 85% | 1x |
| High funding rate alone as bullish ... | 35% | 247 | 85% | 1x |
| Multi-timeframe bearish alignment f... | 35% | 201 | 95% | 1x |
| Positive funding rate interpreted a... | 30% | 208 | 60% | 1x |
| Multi-timeframe bullish alignment (... | 30% | 160 | 95% | 1x |
| RSI overbought + MACD bearish as sh... | 30% | 355 | 95% | 1x |
| Multi-timeframe bullish alignment (... | 25% | 88 | 95% | 1x |
| Negative funding rate as long oppor... | 25% | 180 | 95% | 1x |
| Multi-timeframe bearish alignment f... | 25% | 173 | 95% | 1x |
| Relative strength divergence (one a... | 20% | 72 | 70% | 1x |
| Positive momentum on small timefram... | 20% | 76 | 95% | 1x |
| Contrarian 'bounce back' reasoning ... | 20% | 180 | 95% | 1x |
| Multi-timeframe bullish alignment (... | 18% | 294 | 74% | 2x |
| SMA and MACD bearish signals for sh... | 18% | 50 | 70% | 1x |
| Positive funding rate interpreted a... | 15% | 225 | 70% | 1x |
Success rate: 88% Total samples: 164 Confidence: 85% Times confirmed: 1 First seen: 2026-01-14 Description: Multi-timeframe bullish alignment (15m, 1h, 4h) WITH explicit risk validation and trade validation checks - skill_aware_oss uses this consistently with strong results (+$1236.81)
Success rate: 85% Total samples: 157 Confidence: 75% Times confirmed: 1 First seen: 2026-01-14 Description: Multi-timeframe bullish alignment (15m, 1h, 4h) combined with explicit risk validation produces profits in trending markets. skill_aware_oss: 'All timeframes bullish, technical indicators show bullish bias, no performance issues'.
Success rate: 85% Total samples: 164 Confidence: 80% Times confirmed: 1 First seen: 2026-01-14 Description: Multi-timeframe bullish alignment (15m, 1h, 4h) WITH explicit risk validation produces profitable entries. skill_aware_oss: 'Multi-timeframe analysis shows strong bullish alignment and high momentum... validation passes'. Success requires both trend confirmation AND risk checks.
Success rate: 85% Total samples: 164 Confidence: 85% Times confirmed: 1 First seen: 2026-01-14 Description: Multi-timeframe bullish alignment (15m, 1h, 4h) WITH explicit risk validation and trade validation passed - produces strong returns in trending markets
Success rate: 82% Total samples: 184 Confidence: 80% Times confirmed: 1 First seen: 2026-01-14 Description: SMA crossover + bullish MACD + neutral Bollinger as entry confirmation. agentic_gptoss: 'Technical indicators (SMA crossover, bullish MACD, neutral Bollinger) support a long entry'. Combined with risk calculator validation.
| Confidence | Interpretation |
|---|---|
| 90%+ | High confidence - strong historical support |
| 70-90% | Moderate confidence - use with other signals |
| 60-70% | Low confidence - consider as one input |
| <60% | Experimental - needs more data |
This skill is automatically generated and updated by the Observer Agent.
Make 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
pproenca/dot-skills
mattpocock/skills
entry-signals reduced setup friction for our internal harness; good balance of opinion and flexibility.
entry-signals has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: entry-signals is the kind of skill you can hand to a new teammate without a long onboarding doc.
entry-signals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
entry-signals has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for entry-signals matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend entry-signals for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: entry-signals is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in entry-signals — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
entry-signals is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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