{"adoption": {"forks": 265, "observed_at": "2026-08-28T04:07:37.789840+00:00", "stars": 3010}, "canonical_url": "https://ross.abutalabs.com/products/autoprompt", "card": {"archived": false, "artifact_type": "framework", "description": "A framework for prompt tuning using Intent-based Prompt Calibration ", "domain": ["large-language-models", "artificial-intelligence", "machine-learning", "developer-tools"], "enriched": true, "function": ["prompt-engineering", "machine-learning", "data-generation", "llm-training"], "health_score": 58, "homepage": null, "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "active", "member_repos": ["Eladlev/AutoPrompt"], "name": "Eladlev/AutoPrompt", "platform": ["python", "cross-platform"], "pushed_at": "2025-12-02T17:23:20+00:00", "repo": "Eladlev/AutoPrompt", "stars": 3010, "tags": ["prompt-optimization", "prompt-tuning", "synthetic-data", "llm-evaluation", "intent-based-prompt-calibration"], "topics": ["prompt-engineering", "prompt-tuning", "synthetic-dataset-generation"], "urls": [], "use_cases": ["optimize and refine LLM prompts automatically", "generate synthetic datasets of challenging edge cases for prompt evaluation", "benchmark production-grade prompts with minimal annotation effort", "migrate prompts between LLM models", "build robust moderation or classification prompts", "create ranker prompts for content generation tasks"], "what_it_is": "AutoPrompt is a Python framework for optimizing LLM prompts using Intent-based Prompt Calibration. It iteratively generates challenging edge-case datasets, annotates them, and refines prompts to produce robust, production-grade prompts with measured accuracy.", "when_to_avoid": ["you need a simple one-off prompt tweak without iterative optimization", "your project has no budget for LLM API calls during calibration", "you require fine-tuning of model weights rather than prompts"], "when_to_choose": ["you need reliable, robust prompts for real-world LLM applications", "manual prompt engineering is too time-consuming or error-prone", "you want to build evaluation benchmarks with minimal labeled data", "you need synthetic data generation combined with prompt optimization"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/autoprompt", "repo": "Eladlev/AutoPrompt", "role": "main", "score": 42}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:07:37.789840+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T07:30:12.028451+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "0a4ce76c522f178a7c29b888e0bfc7c07a0f9b1ea29623f6a92c32561fecbe6f", "fetched_at": "2026-08-28T04:07:37.789840+00:00", "kind": "readme", "missing": false, "url": "https://github.com/Eladlev/AutoPrompt"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 55, "longevity": 71, "rhythm": 8}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 1005, "days_push": 274, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 42, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}