Hypersampling
Parallel multi-sampling for synthesizing the best answer. Triggers: hypersampling, multi-sample, ensemble.
Install
$ agentstack add skill-kjx-talesofai-claude-skill-hypersampling-claude-skill-hypersampling ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-kjx-talesofai-claude-skill-hypersampling-claude-skill-hypersampling) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Hypersampling
Overview
Hypersampling generates K independent answers to a question in parallel, then synthesizes the best outcome by combining insights from all samples.
Workflow
1. Parse Parameters
Extract from the user's request:
- Question: The problem/question to answer
- K: Number of parallel samples (default: 3 if not specified)
2. Spawn Parallel Tasks
Launch K parallel tasks using the Task tool. Each task:
- Receives the identical question
- Is a general-purpose subagent with no special instructions
- Independently generates an answer
Example for K=3:
Hypersample answer 1/3
Answer this question thoroughly: {question}
general-purpose
Hypersample answer 2/3
Answer this question thoroughly: {question}
general-purpose
Hypersample answer 3/3
Answer this question thoroughly: {question}
general-purpose
3. Synthesize Results
Once all K tasks complete:
- Collect all answers
- Analyze for:
- Common consensus points (high confidence)
- Unique insights from each sample
- Contradictions or alternative approaches
- Best parts of each answer
- Synthesize into a final answer that:
- Prioritizes consensus (most reliable)
- Incorporates valuable unique insights
- Acknowledges uncertainty where answers diverge
- Presents the strongest overall solution
4. Report to User
Present:
- The synthesized best answer
- Brief note on how many samples agreed on key points
- Optional: mention if significant alternative approaches were considered
Example Usage
User: "Hypersample: What's the best way to learn Python?"
Action:
- Launch 3 parallel tasks with "What's the best way to learn Python?"
- Receive 3 different learning path suggestions
- Synthesize: Combine the common elements (practice, projects) + best unique insights from each
- Report comprehensive learning roadmap
Notes
- Higher K (5-7) for complex/multi-faceted questions
- Lower K (2-3) for straightforward questions
- Synthesis should be selective, not exhaustive - prioritize quality over comprehensiveness
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: kjx-talesofai
- Source: kjx-talesofai/claude-skill-hypersampling
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.