Files
uroma b60638f0a3 Add community skills, agents, system prompts from 22+ sources
Community Skills (32):
- jat: jat-start, jat-verify, jat-complete
- pi-mono: codex-cli, codex-5.3-prompting, interactive-shell
- picoclaw: github, weather, tmux, summarize, skill-creator
- dyad: 18 skills (swarm-to-plan, multi-pr-review, fix-issue, lint, etc.)
- dexter: dcf valuation skill

Agents (23):
- pi-mono subagents: scout, planner, reviewer, worker
- toad: 19 agent configs (Claude, Codex, Gemini, Copilot, OpenCode, etc.)

System Prompts (91):
- Anthropic: 15 Claude prompts (opus-4.6, code, cowork, etc.)
- OpenAI: 49 GPT prompts (gpt-5 series, o3, o4-mini, tools)
- Google: 13 Gemini prompts (2.5-pro, 3-pro, workspace, cli)
- xAI: 5 Grok prompts
- Other: 9 misc prompts (Notion, Raycast, Warp, Kagi, etc.)

Hooks (9):
- JAT hooks for session management, signal tracking, activity logging

Prompts (6):
- pi-mono templates for PR review, issue analysis, changelog audit

Sources analyzed: jat, ralph-desktop, toad, pi-mono, cmux, pi-interactive-shell,
craft-agents-oss, dexter, picoclaw, dyad, system_prompts_leaks, Prometheus,
zed, clawdbot, OS-Copilot, and more
2026-02-13 10:58:17 +00:00

3.0 KiB

Issue Output Schema

JSON schema for the structured issue output from sub-agents.

Schema

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "array",
  "items": {
    "type": "object",
    "required": [
      "file",
      "line_start",
      "severity",
      "category",
      "title",
      "description"
    ],
    "properties": {
      "file": {
        "type": "string",
        "description": "Relative path to the file containing the issue"
      },
      "line_start": {
        "type": "integer",
        "minimum": 1,
        "description": "Starting line number of the issue"
      },
      "line_end": {
        "type": "integer",
        "minimum": 1,
        "description": "Ending line number (defaults to line_start if single line)"
      },
      "severity": {
        "type": "string",
        "enum": ["HIGH", "MEDIUM", "LOW"],
        "description": "Criticality level of the issue"
      },
      "category": {
        "type": "string",
        "enum": [
          "security",
          "logic",
          "performance",
          "error-handling",
          "style",
          "other"
        ],
        "description": "Category of the issue"
      },
      "title": {
        "type": "string",
        "maxLength": 100,
        "description": "Brief, descriptive title for the issue"
      },
      "description": {
        "type": "string",
        "description": "Detailed explanation of the issue and its impact"
      },
      "suggestion": {
        "type": "string",
        "description": "Optional suggestion for how to fix the issue"
      }
    }
  }
}

Example Output

[
  {
    "file": "src/auth/login.py",
    "line_start": 45,
    "line_end": 48,
    "severity": "HIGH",
    "category": "security",
    "title": "SQL injection vulnerability in user lookup",
    "description": "User input is directly interpolated into SQL query without parameterization. An attacker could inject malicious SQL to bypass authentication or extract data.",
    "suggestion": "Use parameterized queries: cursor.execute('SELECT * FROM users WHERE username = ?', (username,))"
  },
  {
    "file": "src/utils/cache.py",
    "line_start": 112,
    "line_end": 112,
    "severity": "MEDIUM",
    "category": "error-handling",
    "title": "Missing exception handling for cache connection failure",
    "description": "If Redis connection fails, the exception propagates and crashes the request handler. Cache failures should be handled gracefully with fallback to direct database queries.",
    "suggestion": "Wrap cache operations in try/except and fall back to database on failure"
  }
]

Consensus Output

After aggregation, issues include additional metadata:

{
  "file": "src/auth/login.py",
  "line_start": 45,
  "line_end": 48,
  "severity": "HIGH",
  "category": "security",
  "title": "SQL injection vulnerability in user lookup",
  "description": "...",
  "suggestion": "...",
  "consensus_count": 3,
  "all_severities": ["HIGH", "HIGH", "MEDIUM"]
}