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verbalized-sampling
Generate diverse outputs by prompting for a probability distribution instead of a single response. Implements Verbalized Sampling (VS) from Zhang et al. 2025 — a training-free technique that counteracts LLM mode collapse caused by typicality bias in alignment data. Use when the t
Übersicht
Generate diverse outputs by prompting for a probability distribution instead of a single response. Implements Verbalized Sampling (VS) from Zhang et al. 2025 — a training-free technique that counteracts LLM mode collapse caused by typicality bias in alignment data. Use when the task needs genuine diversity: creative writing, brainstorming/ideation, synthetic data generation, persona/dialogue simulation, adversarial examples, open-ended QA with multiple valid answers, or any situation where "generate 5 ideas" keeps returning the same cluster. Do NOT use for: single correct answer tasks, factual lookup, strict format compliance.
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Verbalized Sampling
Universal VS Template
[Task description with rich context]
Generate {k} responses. Return in JSON format with key "{output_key}" (list of dicts). Each dict:
• text: [output specification]
• probability: estimated probability (0.0–1.0) of this response given the input
{Distribution constraint}
Output ONLY the JSON object.
Distribution constraints — pick one:
Sample from the full distribution.— balanced, moderate diversitySample from the tails of the distribution, with each probability below 0.10.— high diversitySample from the tails of the distribution, with each probability below 0.01.— maximum diversity
Variant Selection
| Variant | When to use | Trade-off |
|---|---|---|
| VS-Standard | Straightforward tasks, speed priority | Best balance |
| VS-CoT | Complex tasks needing quality + diversity | Slight diversity cost, higher quality |
| VS-Multi | Maximum diversity, token cost acceptable | Best diversity, 2× token cost |
VS-CoT: add "reasoning": "step-by-step thought process" as the first field in each dict.
VS-Multi: Turn 1 generates k/2 responses. Turn 2: "Generate k alternative responses to the original prompt — do not repeat ideas from Turn 1."
Context-First Phase (run before VS)
VS outputs are only as good as the problem framing going in. Before constructing the VS prompt:
Step 1 — Decompose into subproblems: Break the task into 3–5 distinct subproblems or angles. Example: "improve sales for a B2B SaaS" → (1) acquisition channels, (2) conversion from trial, (3) pricing/packaging, (4) referral/word-of-mouth, (5) partnerships.
Step 2 — Load context for each subproblem:
- What are the real constraints? (time, budget, team size, org politics, market saturation)
- What do others in this space actually do? (base rates — what approaches are common, what have failed)
- What has already been tried? (avoid re-suggesting)
Step 3 — Inject context into the VS prompt: Compress answers from Step 2 into the prompt preamble. Name the subproblems as explicit coverage requirements: "Cover at least one idea addressing each of: [subproblem 1], [subproblem 2], ..."
If you cannot answer Step 2 without asking the user, ask first before generating. Generic outputs caused by thin context are the primary failure mode for brainstorming tasks (FM-2).
Critique-and-Improve Loop (run after VS, before presenting)
After generating VS output, run a self-critique pass before presenting results. See references/critique-framework.md for the full 6-dimension framework and prompt templates.
Quick pass: For each output item, check:
- Is this naive/obvious? (would it appear in a top-10 listicle?)
- Is this actionable? (could execution start Monday without further research?)
- Does this require magical thinking? (assumes steps will "just work" with no mechanism)
- Does this ignore base rates? (approaches with known low success rates presented as good bets)
If 2+ items fail 2+ checks:
- Flag the specific failures with reasons
- Generate 2–3 improved variants that directly address the flagged weaknesses
- Present: original outputs + critique summary + improved variants
For automated quality scoring of VS outputs, see references/judges.md for LLM-as-Judge prompts.
Output Mode Selection
JSON mode (default for agent pipelines — pipe-able, machine-readable):
- Use when outputs feed into downstream processing, storage, or evaluation
- Return raw JSON as-is
Readable mode (in-chat or external sharing):
- Format using
scripts/format_vs_output.py(see below), or render inline as numbered markdown - Group by diversity tier: High (p < 0.05), Moderate (p 0.05–0.15), Low/Common (p > 0.15)
- Show probabilities inline as
(p=0.07)
To format manually in-chat:
## High diversity (p < 0.05)
1. [text] (p=0.03)
## Moderate diversity (p 0.05–0.15)
2. [text] (p=0.08)
CLI formatting: echo '<json>' | python ~/.cursor/skills/verbalized-sampling/scripts/format_vs_output.py
Probability Threshold Quick Reference
| Threshold | Use case |
|---|---|
| Full distribution | General brainstorm, want common + uncommon mix |
| p < 0.15 | Moderate novelty — avoids top-5 obvious answers |
| p < 0.10 | High diversity — noticeably non-obvious outputs |
| p < 0.05 | Aggressive — expect surprising, niche ideas |
| p < 0.01 | Maximum — edge cases, stress testing, adversarial |
Failure Modes (from empirical evals)
FM-1: Overfit Topic Collapse High-frequency training topics (weight loss, productivity, exercise) resist VS even at p<0.01. The tail of the model's distribution is still inside the well-known solution cluster. The paper's 1.6-2.1× diversity gains apply to creative and niche domains — not saturated self-help topics.
Mitigation: Add explicit exclusion constraints: "Exclude any idea covered in mainstream [domain] journalism. Prioritize ideas from adjacent fields or underrepresented subcultures."
FM-2: Context Starvation → Generic Gravity Thin prompt context ("Xero + retention") produces generic-category outputs even at tail sampling. The more proprietary and specific the context, the better VS performs.
Mitigation: Load rich context before the VS prompt — company stage, current channels, known constraints, target segment, what's already been tried.
FM-3: Semantic Clustering Despite Syntactic Diversity Tail sampling can produce a list that looks different but covers the same solution space. VS does not automatically cross problem-frame boundaries.
Mitigation: Name the problem frames explicitly: "Cover at least one idea from each of: distribution, pricing, community, product, and partnerships."
FM-4: Probability Spread Collapse If the highest and lowest probabilities in your output are within 3× of each other (e.g., all between 0.05–0.09), you're likely in an overfit topic and diversity is illusory.
Diagnosis signal: Good VS output has a spread of at least 5-10× between highest and lowest probability. If spread is tight, switch to FM-1/FM-2 mitigations.
Meta-Prompt: Generate a VS Prompt
I need to generate diverse {output_type} for {use_case}.
Create a Verbalized Sampling prompt that:
1. Clearly describes the task with specific context about {use_case}
2. Requests k={number} outputs in JSON format
3. Requires each output to include "text" and "probability" fields
4. Specifies a distribution constraint appropriate for the diversity level needed:
- 0.10–0.15 for moderate diversity
- 0.05–0.10 for high diversity
- 0.01–0.05 for maximum diversity
5. Ends with "Output ONLY the JSON"
6. Includes explicit problem-frame coverage if topic is likely overfit
Domain Templates
See references/templates.md for ready-to-paste prompts across: creative writing, brainstorming/ideation, dialogue simulation, synthetic data, adversarial examples, open-ended QA.
When NOT to Use VS
- Single correct answer exists
- Factual lookup or retrieval
- Task requires strict format compliance
- You need one best answer, not a distribution
- Overfit topic with no rich context available (add context first, then re-evaluate)
Dateimetadaten
name: verbalized-sampling description: > Generate diverse outputs by prompting for a probability distribution instead of a single response. Implements Verbalized Sampling (VS) from Zhang et al. 2025 — a training-free technique that counteracts LLM mode collapse caused by typicality bias in alignment data. Use when the task needs genuine diversity: creative writing, brainstorming/ideation, synthetic data generation, persona/dialogue simulation, adversarial examples, open-ended QA with multiple valid answers, or any situation where "generate 5 ideas" keeps returning the same cluster. Do NOT use for: single correct answer tasks, factual lookup, strict format compliance.
Originaltext anzeigen
---
name: verbalized-sampling
description: >
Generate diverse outputs by prompting for a probability distribution instead of a single response.
Implements Verbalized Sampling (VS) from Zhang et al. 2025 — a training-free technique that counteracts
LLM mode collapse caused by typicality bias in alignment data.
Use when the task needs genuine diversity: creative writing, brainstorming/ideation, synthetic data
generation, persona/dialogue simulation, adversarial examples, open-ended QA with multiple valid
answers, or any situation where "generate 5 ideas" keeps returning the same cluster.
Do NOT use for: single correct answer tasks, factual lookup, strict format compliance.
---
# Verbalized Sampling
## Universal VS Template
```
[Task description with rich context]
Generate {k} responses. Return in JSON format with key "{output_key}" (list of dicts). Each dict:
• text: [output specification]
• probability: estimated probability (0.0–1.0) of this response given the input
{Distribution constraint}
Output ONLY the JSON object.
```
**Distribution constraints — pick one:**
- `Sample from the full distribution.` — balanced, moderate diversity
- `Sample from the tails of the distribution, with each probability below 0.10.` — high diversity
- `Sample from the tails of the distribution, with each probability below 0.01.` — maximum diversity
## Variant Selection
| Variant | When to use | Trade-off |
|---|---|---|
| **VS-Standard** | Straightforward tasks, speed priority | Best balance |
| **VS-CoT** | Complex tasks needing quality + diversity | Slight diversity cost, higher quality |
| **VS-Multi** | Maximum diversity, token cost acceptable | Best diversity, 2× token cost |
**VS-CoT**: add `"reasoning": "step-by-step thought process"` as the first field in each dict.
**VS-Multi**: Turn 1 generates k/2 responses. Turn 2: "Generate k alternative responses to the original prompt — do not repeat ideas from Turn 1."
## Context-First Phase (run before VS)
VS outputs are only as good as the problem framing going in. Before constructing the VS prompt:
**Step 1 — Decompose into subproblems:**
Break the task into 3–5 distinct subproblems or angles. Example: "improve sales for a B2B SaaS" → (1) acquisition channels, (2) conversion from trial, (3) pricing/packaging, (4) referral/word-of-mouth, (5) partnerships.
**Step 2 — Load context for each subproblem:**
- What are the real constraints? (time, budget, team size, org politics, market saturation)
- What do others in this space actually do? (base rates — what approaches are common, what have failed)
- What has already been tried? (avoid re-suggesting)
**Step 3 — Inject context into the VS prompt:**
Compress answers from Step 2 into the prompt preamble. Name the subproblems as explicit coverage requirements: "Cover at least one idea addressing each of: [subproblem 1], [subproblem 2], ..."
If you cannot answer Step 2 without asking the user, **ask first** before generating. Generic outputs caused by thin context are the primary failure mode for brainstorming tasks (FM-2).
## Critique-and-Improve Loop (run after VS, before presenting)
After generating VS output, run a self-critique pass before presenting results. See `references/critique-framework.md` for the full 6-dimension framework and prompt templates.
**Quick pass:**
For each output item, check:
1. Is this naive/obvious? (would it appear in a top-10 listicle?)
2. Is this actionable? (could execution start Monday without further research?)
3. Does this require magical thinking? (assumes steps will "just work" with no mechanism)
4. Does this ignore base rates? (approaches with known low success rates presented as good bets)
**If 2+ items fail 2+ checks:**
- Flag the specific failures with reasons
- Generate 2–3 improved variants that directly address the flagged weaknesses
- Present: original outputs + critique summary + improved variants
For automated quality scoring of VS outputs, see `references/judges.md` for LLM-as-Judge prompts.
## Output Mode Selection
**JSON mode** (default for agent pipelines — pipe-able, machine-readable):
- Use when outputs feed into downstream processing, storage, or evaluation
- Return raw JSON as-is
**Readable mode** (in-chat or external sharing):
- Format using `scripts/format_vs_output.py` (see below), or render inline as numbered markdown
- Group by diversity tier: High (p < 0.05), Moderate (p 0.05–0.15), Low/Common (p > 0.15)
- Show probabilities inline as `(p=0.07)`
To format manually in-chat:
```markdown
## High diversity (p < 0.05)
1. [text] (p=0.03)
## Moderate diversity (p 0.05–0.15)
2. [text] (p=0.08)
```
CLI formatting: `echo '<json>' | python ~/.cursor/skills/verbalized-sampling/scripts/format_vs_output.py`
## Probability Threshold Quick Reference
| Threshold | Use case |
|---|---|
| Full distribution | General brainstorm, want common + uncommon mix |
| p < 0.15 | Moderate novelty — avoids top-5 obvious answers |
| p < 0.10 | High diversity — noticeably non-obvious outputs |
| p < 0.05 | Aggressive — expect surprising, niche ideas |
| p < 0.01 | Maximum — edge cases, stress testing, adversarial |
## Failure Modes (from empirical evals)
**FM-1: Overfit Topic Collapse**
High-frequency training topics (weight loss, productivity, exercise) resist VS even at p<0.01. The tail of the model's distribution is still inside the well-known solution cluster. The paper's 1.6-2.1× diversity gains apply to creative and niche domains — not saturated self-help topics.
*Mitigation*: Add explicit exclusion constraints: "Exclude any idea covered in mainstream [domain] journalism. Prioritize ideas from adjacent fields or underrepresented subcultures."
**FM-2: Context Starvation → Generic Gravity**
Thin prompt context ("Xero + retention") produces generic-category outputs even at tail sampling. The more proprietary and specific the context, the better VS performs.
*Mitigation*: Load rich context before the VS prompt — company stage, current channels, known constraints, target segment, what's already been tried.
**FM-3: Semantic Clustering Despite Syntactic Diversity**
Tail sampling can produce a list that looks different but covers the same solution space. VS does not automatically cross problem-frame boundaries.
*Mitigation*: Name the problem frames explicitly: "Cover at least one idea from each of: distribution, pricing, community, product, and partnerships."
**FM-4: Probability Spread Collapse**
If the highest and lowest probabilities in your output are within 3× of each other (e.g., all between 0.05–0.09), you're likely in an overfit topic and diversity is illusory.
*Diagnosis signal*: Good VS output has a spread of at least 5-10× between highest and lowest probability. If spread is tight, switch to FM-1/FM-2 mitigations.
## Meta-Prompt: Generate a VS Prompt
```
I need to generate diverse {output_type} for {use_case}.
Create a Verbalized Sampling prompt that:
1. Clearly describes the task with specific context about {use_case}
2. Requests k={number} outputs in JSON format
3. Requires each output to include "text" and "probability" fields
4. Specifies a distribution constraint appropriate for the diversity level needed:
- 0.10–0.15 for moderate diversity
- 0.05–0.10 for high diversity
- 0.01–0.05 for maximum diversity
5. Ends with "Output ONLY the JSON"
6. Includes explicit problem-frame coverage if topic is likely overfit
```
## Domain Templates
See `references/templates.md` for ready-to-paste prompts across: creative writing, brainstorming/ideation, dialogue simulation, synthetic data, adversarial examples, open-ended QA.
## When NOT to Use VS
- Single correct answer exists
- Factual lookup or retrieval
- Task requires strict format compliance
- You need one best answer, not a distribution
- Overfit topic with no rich context available (add context first, then re-evaluate)
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Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.
Mit einer kleinen Aufgabe beginnen
- 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
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Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.
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- Financial research output is not financial advice; require human review before any live investment decision
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 104 stars, 8 forks; issue activity unavailable in current metadata
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Weitere Details
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"not_relevant": 0,
"success_rate": null,
"recent_success_rate": null,
"recent_failure_rate": null,
"install_attempts": 0,
"install_success_rate": null,
"risk_blocked": 0,
"setup_required": 0,
"avg_output_quality": null,
"production_outcomes": 0,
"last_outcome_at": null,
"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"security",
"agent-skill"
],
"known_risks": [
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Stars/forks activity: 104 stars, 8 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 76,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Stars/forks activity: 104 stars, 8 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 64,
"label": "Promising"
},
"supply": {
"track": "Legal, policy, and compliance",
"scenario": "Security and compliance",
"maintenance": "2mo since push",
"risk": "Needs review"
},
"alternative_skills": [
{
"slug": "cherryhq-gh-create-pr",
"name": "gh-create-pr",
"url": "https://www.openagentskill.com/skills/cherryhq-gh-create-pr",
"stars": 52338,
"install_command": "npx skills add CherryHQ/cherry-studio --skill gh-create-pr",
"trust_score": 83,
"audit_score": 87
},
{
"slug": "kiterlin-anti-defensive-writing-3c8f161a",
"name": "anti-defensive-writing",
"url": "https://www.openagentskill.com/skills/kiterlin-anti-defensive-writing-3c8f161a",
"stars": 904,
"install_command": "npx skills add Kiterlin/anti-defensive-writing --skill anti-defensive-writing",
"trust_score": 82,
"audit_score": 83
}
],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No major risk signals from current metadata",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision."
],
"agent_contract": {
"task_input": "Use verbalized-sampling in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 74/100 Strong shortlist",
"Audit: 76/100 Needs review",
"Safety: 36/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "gnurio-verbalized-sampling (verbalized-sampling)",
"install_command": "npx skills add gnurio/nurijanian-skills --skill verbalized-sampling",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "gnurio-verbalized-sampling",
"task": "Use verbalized-sampling in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/gnurio-verbalized-sampling",
"api": "https://www.openagentskill.com/api/agent/skills/gnurio-verbalized-sampling",
"audit": "https://www.openagentskill.com/skills/gnurio-verbalized-sampling/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=gnurio-verbalized-sampling&task=Use%20verbalized-sampling%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20verbalized-sampling%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20verbalized-sampling%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/gnurio-verbalized-sampling/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/gnurio-verbalized-sampling"
}
}Für Ersteller
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- gnurio
- Quelle
- gnurio/nurijanian-skills
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
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Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
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[](https://www.openagentskill.com/skills/gnurio-verbalized-sampling/audit)
[](https://www.openagentskill.com/skills/gnurio-verbalized-sampling?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.