Agent Skills Pipeline v1.0
8-stage pipeline: Scout → Filter → Reader → Extractor → Score → Generator → Reviewer → Publisher - Scout: GitHub search with token auth + rate limit retry - Filter: Deterministic rules (language, stars, age, keywords) - Reader: Incremental context loading (README → docs → examples → code) - Extractor: LLM workflow extraction with JSON retry - Score: Rule-based evaluation (no LLM) - Generator: Standardized Hermes Skill format - Reviewer: Independent LLM review (separate from generator) - Publisher: Branch + PR to Gitea First run: 5 repos discovered, 0 extracted (correct — all frameworks, no workflows)
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#!/usr/bin/env python3
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"""Agent Skills Pipeline Runner — full 8-stage pipeline."""
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import yaml
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import json
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import sys
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import os
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import datetime
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from pipeline.scout import scout
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from pipeline.filter import filter_repos
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from pipeline.reader import read_repo
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from pipeline.extractor import extract_workflow
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from pipeline.scorer import score_workflow
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from pipeline.generator import generate_skill
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from pipeline.reviewer import review_skill
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from pipeline.publisher import publish_skill
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def load_config():
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config_path = os.path.join(os.path.dirname(__file__), "config", "settings.yaml")
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with open(config_path) as f:
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return yaml.safe_load(f)
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def main():
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config = load_config()
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runs_dir = os.path.join(os.path.dirname(__file__), "runs")
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os.makedirs(runs_dir, exist_ok=True)
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run_id = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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run_log = {"run_id": run_id, "started_at": datetime.datetime.now().isoformat(), "stages": {}}
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print("=" * 60)
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print("Agent Skills Pipeline — Run", run_id)
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print("=" * 60)
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# --- Stage 1: Scout ---
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print("\n[1/8] Scout — Discovering repos from GitHub...")
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state = {}
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scout_result = scout(config, state)
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if scout_result.get("status") in ("COOLDOWN", "RATE_LIMITED"):
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print(f" ⏸ {scout_result.get('message', scout_result.get('status'))}")
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return
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repos = scout_result.get("repos", [])
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print(f" Found {scout_result.get('count', 0)} repos")
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run_log["stages"]["scout"] = {"count": scout_result.get("count", 0)}
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if not repos:
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print(" No repos found. Exiting.")
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return
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# --- Stage 2: Filter ---
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print("\n[2/8] Filter — Applying deterministic rules...")
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filter_result = filter_repos(repos, config)
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kept = filter_result.get("kept", [])
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print(f" Kept: {filter_result.get('kept_count', 0)} | Rejected: {filter_result.get('rejected_count', 0)}")
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run_log["stages"]["filter"] = {"kept": filter_result.get("kept_count", 0), "rejected": filter_result.get("rejected_count", 0)}
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for rej in filter_result.get("rejected", [])[:3]:
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print(f" ✗ {rej['repo'].get('name', '?')}: {', '.join(rej['reasons'])}")
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if not kept:
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print(" All repos filtered out. Exiting.")
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return
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# --- Stages 3-8: Process each kept repo ---
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print(f"\n[3-8/8] Processing {len(kept)} repos through pipeline...")
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results = {"extracted": 0, "scored": 0, "generated": 0, "reviewed": 0, "published": 0}
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for i, repo in enumerate(kept[:5]): # Cap at 5 per run
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repo_name = repo.get("full_name", repo.get("name", "?"))
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repo_url = repo.get("url", "")
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print(f"\n ── Repo {i+1}/{min(len(kept), 5)}: {repo_name} ──")
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# Stage 3: Reader
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print(f" [3/8] Reader — Loading context...")
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reader_output = read_repo(repo.get("clone_url", repo_url), config)
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if reader_output.get("status") == "INSUFFICIENT":
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print(f" ⏸ Insufficient context: {reader_output.get('decision_reason', '')}")
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continue
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print(f" Loaded {len(reader_output.get('context_loaded', []))} files")
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# Stage 4: Extractor
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print(f" [4/8] Extractor — Looking for reusable workflow...")
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extract_output = extract_workflow(reader_output, config)
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if extract_output.get("status") != "EXTRACTED":
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print(f" ✗ No workflow: {extract_output.get('reason', extract_output.get('status', ''))}")
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continue
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workflow = extract_output.get("workflow", {})
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print(f" ✓ Extracted: {workflow.get('skill_name', '?')} (confidence: {workflow.get('confidence', 0)})")
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results["extracted"] += 1
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# Stage 5: Scorer
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print(f" [5/8] Scorer — Evaluating...")
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extract_output["reader_output"] = reader_output
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score_output = score_workflow(extract_output, config)
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if score_output.get("decision") != "PASS":
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print(f" ✗ Score {score_output.get('score', 0)} < {score_output.get('min_score', 0.85)}")
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continue
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print(f" ✓ Score: {score_output.get('score', 0)} (passed)")
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results["scored"] += 1
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# Stage 6: Generator
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print(f" [6/8] Generator — Building Skill package...")
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gen_output = generate_skill(score_output, config)
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if gen_output.get("status") != "GENERATED":
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print(f" ✗ Generation blocked: {gen_output.get('reason', '')}")
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continue
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print(f" ✓ Generated: {gen_output.get('skill_name', '?')} ({len(gen_output.get('files', {}))} files)")
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results["generated"] += 1
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# Stage 7: Reviewer
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print(f" [7/8] Reviewer — LLM review...")
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review_output = review_skill(gen_output, config)
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if review_output.get("status") != "APPROVED":
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print(f" ✗ Review: {review_output.get('status', '?')} — {review_output.get('reason', '')[:100]}")
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continue
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print(f" ✓ Approved (confidence: {review_output.get('confidence', 0)})")
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results["reviewed"] += 1
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# Stage 8: Publisher
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print(f" [8/8] Publisher — Creating PR...")
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publish_output = publish_skill(review_output, config)
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if publish_output.get("status") == "PUBLISHED":
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print(f" ✓ Published! PR: {publish_output.get('pr_url', '')}")
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results["published"] += 1
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else:
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print(f" ! {publish_output.get('status', '?')}: {publish_output.get('message', publish_output.get('error', ''))[:100]}")
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# --- Summary ---
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print("\n" + "=" * 60)
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print("PIPELINE COMPLETE")
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print("=" * 60)
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print(f" Scout: {scout_result.get('count', 0)} discovered")
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print(f" Filter: {filter_result.get('kept_count', 0)} kept / {filter_result.get('rejected_count', 0)} rejected")
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print(f" Extracted: {results['extracted']}")
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print(f" Scored: {results['scored']}")
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print(f" Generated: {results['generated']}")
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print(f" Reviewed: {results['reviewed']}")
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print(f" Published: {results['published']}")
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# Save run log
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run_log["results"] = results
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run_log["ended_at"] = datetime.datetime.now().isoformat()
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log_path = os.path.join(runs_dir, f"{run_id}.json")
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with open(log_path, 'w') as f:
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json.dump(run_log, f, indent=2)
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print(f"\n Run log: {log_path}")
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if __name__ == "__main__":
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main()
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