repo-scanner

name	repo_scanner
display_name	Repo Scanner
description	Meta-specialist that auto-discovers and scaffolds new specialists from trending GitHub repos
version	1.0.0
source_repo	jeremylongshore/oss-agent-lab
license	MIT
tier	core
capabilities	["auto_scaffold","repo_analysis","specialist_generation"]
allowed_tools	["scan_repo","scaffold_specialist","evaluate_score"]
output_formats	["python_api","cli","mcp_server","agent_skill","rest_api"]

Repo Scanner Specialist

Overview

A meta-specialist that IS the OSS Agent Lab repository acting on itself. It consumes the Capability Scoring Engine's signal pipeline to evaluate whether a candidate GitHub repo is worth wrapping as a new specialist, then auto-scaffolds the directory skeleton when the score crosses the threshold.

Wraps jeremylongshore/oss-agent-lab.

Capabilities

• auto_scaffold: Materialise a new specialist directory from the _template skeleton when a repo's composite score reaches >= 80.
• repo_analysis: Analyse structural signals (Python presence, tests, README, licence) and infer likely capabilities from repo naming conventions.
• specialist_generation: Orchestrate the full scan → score → scaffold pipeline and surface a unified result with provenance metadata.

Tools

Tool	Description	Side Effects
scan_repo	Structural analysis: name suggestion, detected capabilities, has_python/tests/readme, recommendation	None
evaluate_score	Composite capability score (0-100) with action and signal breakdown	None
scaffold_specialist	Copy _template/ into agents/specialists/<name>/	Creates files on disk

Parameters

All parameters are passed via request.intent.parameters:

Parameter	Type	Default	Description
repo	str	(query text)	GitHub repo in owner/name format
name	str	(from scan)	Override for the scaffolded specialist directory name

Usage

Python API

from agents.specialists.repo_scanner.agent import RepoScannerSpecialist
from oss_agent_lab.contracts import Intent, Query, SpecialistRequest

specialist = RepoScannerSpecialist()

request = SpecialistRequest(
    intent=Intent(
        action="scan",
        domain="meta",
        confidence=0.95,
        parameters={"repo": "openai/swarm"},
    ),
    query=Query(user_input="openai/swarm"),
    specialist_name="repo_scanner",
)

response = await specialist.execute(request)
print(response.result["score"]["action"])   # "auto_scaffold" | "evaluate" | "watch" | "skip"
print(response.result.get("scaffold"))      # None or {"status": "created", "path": ..., "files": [...]}

CLI

oss-lab run repo_scanner "openai/swarm"

Response Shape

{
  "repo": "openai/swarm",
  "scan": {
    "repo": "openai/swarm",
    "name_suggestion": "swarm",
    "capabilities_detected": ["agent_orchestration"],
    "has_python": true,
    "has_tests": true,
    "has_readme": true,
    "recommendation": "auto_scaffold"
  },
  "score": {
    "repo": "openai/swarm",
    "estimated_score": 87.04,
    "action": "auto_scaffold",
    "signals": {
      "discovery": 32.1,
      "quality": 28.5,
      "durability": 26.44,
      "github_star_velocity": 0.87,
      "readme_quality": 0.91,
      "test_coverage": 0.75,
      "maintenance_activity": 0.60,
      "community_depth": 0.44
    }
  },
  "scaffold": {
    "status": "created",
    "path": "/home/jeremy/000-projects/oss-agent-lab/agents/specialists/swarm",
    "files": ["__init__.py", "agent.py", "SKILL.md", "tools.py"]
  }
}

Scoring Thresholds

Score	Action	Description
>= 80	auto_scaffold	Immediately scaffold specialist + flag for review
60-79	evaluate	Queue for human evaluation
40-59	watch	Add to watch list; re-score weekly
< 40	skip	Not ready for wrapping

Source

Wraps jeremylongshore/oss-agent-lab.