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Prompt Datasets Builder
Professional curation skill for LLM fine-tuning datasets, deduplication, and leakage auditing.
$5
Prompt Datasets Builder
Example session with this skill installed
Review this set of 3 prompts for a customer support bot fine-tuning task. Check for duplicates, quality issues, and suggest a JSON schema for export.
- How do I reset my password?
- I need to change my password, how?
- Tell me a joke.
- Read your context and instructions
- Compiled the prompt datasets builder
I have analyzed the provided prompts for duplication, relevance, and quality. I detected one near-duplicate pair and one out-of-scope record.
Prompt Dataset Curation Report
Dataset Inventory
| Field | Type | Purpose | Missingness | Notes |
|---|---|---|---|---|
| raw_text | String | User intent | 0% | Primary input |
Curation Findings
| ID | Record | Label | Recommendation | Reason |
|---|---|---|---|---|
| 001 | How do I reset my password? | High Quality | Keep | Clear, specific intent. |
| 002 | I need to change my password, how? | Near Duplicate | Merge/Remove | Semantic overlap with 001. |
| 003 | Tell me a joke. | Out of Scope | Remove | Does not match support bot objective. |
Dataset Schema
{
"id": "string",
"prompt": "string",
"category": "account_management",
"intent": "password_reset",
"quality_score": "float",
"metadata": {
"source": "raw_input",
"version": "1.0.0"
}
}
Safety & Sensitive Data
No PII, secrets, or restricted content detected in the sample records.
Next steps
- Consolidate records 001 and 002 into a single canonical "password_reset" instruction.
- Define a "support_functions" taxonomy to filter out generic conversational queries like jokes.
- Apply the provided JSON schema to your full dataset for export readiness.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
Fine-tuning and evaluation pipelines fail when fed noisy, duplicate, or inconsistent prompt data. Manual curation is slow, error-prone, and often misses subtle train/eval leakage or safety risks.
What it does
- Identifies exact and near-duplicate prompt-response pairs to ensure dataset diversity.
- Performs quality audits to flag ambiguous, incomplete, or contradictory records.
- Detects data leakage between training and evaluation splits to prevent inflated performance metrics.
- Scans for sensitive data, API keys, and credentials without reproducing them in the report.
- Generates structured JSON/JSONL schemas and curation reports for immediate export readiness.
Frameworks & tools
Designed for LLM-ops workflows, supporting JSON, JSONL, CSV, and TSV formats for fine-tuning on platforms like OpenAI, Anthropic, or Hugging Face.
Why this beats prompting it yourself
Writing individual prompts to clean data often results in hallucinated examples or inconsistent labeling. This skill uses a multi-modal curation workflow that enforces strict provenance, prevents fabrication of metadata, and applies a systematic 23-point quality control process that simple prompts lack.
Use cases
- Cleaning raw instruction-tuning data before model training.
- Auditing evaluation sets for leakage against training benchmarks.
- Building consistent label taxonomies for multi-turn conversational datasets.
- Redacting PII and secrets from proprietary prompt logs.
Known limitations
Does not execute dataset exports to external storage; it generates the structured content and reports for you to save. Cannot verify the factual accuracy of responses without external ground-truth data.
Requires data to be provided in structured text formats. Cannot perform automated bulk file I/O or direct database writes. Limited by the context window for extremely large datasets.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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