AI Agent Fixer

AI Agent Fixer

Overview

A domain-specialized variant of issue-fixer for the AI Agent / AI Specification open-source ecosystem. Inherits the full 3-workflow lifecycle (Fix / Discover / Track) from issue-fixer. This skill adds:

1. AI Agent landscape knowledge — domain topics, known repos, ecosystem map
2. AI-specific discovery patterns — MCP, A2A, agent-framework search rounds
3. AI-domain difficulty rubric — adapted to LLM/agent/protocol issue patterns
4. AI quality signals — commit freshness, spec compliance, issue responsiveness

REQUIRED BASE SKILL: You MUST load and reference issue-fixer for the core workflow (A1–A6, B1–B3, C0–C2). This skill only overrides domain-specific parts. Do not re-derive the general workflow — the base skill defines it.

When to Use

flowchart TD
    A["用户输入"] --> P1{"含 AI Agent 关键词?<br/>agent / MCP / A2A / LLM<br/>--discover agent-*"}
    P1 -->|yes| B["Call ai-agent-fixer<br/>→ AI Agent discover mode"]
    P1 -->|no| P2{"已经是 issue-fixer 流程<br/>但仓库是 AI 域?"}
    P2 -->|yes| B
    P2 -->|no| C["回退到 issue-fixer<br/>通用流程"]

Input → Workflow routing

Keyword → AI domain

When NOT to Use

• Repo is not AI-related (Linux desktop, embedded, plain web) — use issue-fixer or the domain-specific skill
• User explicitly requests the generic issue-fixer flow
• AI keywords appear only incidentally (e.g. "LLM" in a blog title) with no AI contribution intent
• Task is local-only (no GitHub discovery/fix/PR) — no skill needed

Quick Reference

Workflow B: AI Agent Discovery (overrides issue-fixer B)

B0. Domain detection

Use the Keyword → AI domain table in When to Use. Parse the user's intent into an AI Agent domain, then apply that domain's search template.

B1. AI repo discovery

Use domain templates from references/repo-discovery.md. Round-based broadening: start with high-specificity topics, relax to broader AI topics, then fall back to keyword search.

Key differences from generic issue-fixer B1:

• Star thresholds are lower (50–100 instead of 200–500) — AI ecosystem is young
• Primary languages are Python and TypeScript (not C/Go/Rust)
• Keywords use AI domain vocabulary: model-context-protocol, a2a, ai-agents, agent-framework
• Quality signals prioritize commit recency and issue response time over raw star count
• Repo lists in the reference are snapshots — verify freshness with gh before trusting them

B2. AI-specific difficulty analysis

Use references/difficulty-analysis.md for AI-domain scoring. Key patterns that score Easy in AI repos:

• Tool description typos / grammar fixes
• Missing Pydantic field validators
• README / documentation gaps (very common in fast-moving AI repos)
• Import path fixes after refactor
• Missing error handling on API calls
• Type annotation corrections

B3. Common AI issue types

See references/difficulty-analysis.md for the full rubric.

Workflow A & C

Inherited from issue-fixer with these AI-domain-specific adjustments:

A4. AI-domain analysis notes

When analyzing AI Agent issues, pay attention to:

• Non-deterministic behavior — LLM responses vary; distinguish deterministic bugs from model behavior variance
• API version compatibility — AI repos depend on fast-moving upstream libraries; check pinned versions
• Async patterns — most AI Agent code uses asyncio; ensure proper await/coroutine handling
• Tool/function schema — JSON Schema generation for LLM function calling is a common pain point

C1. AI-domain review handling

When tracking AI-domain PRs:

• LLM call patterns and tool integration code often receive maintainer scrutiny
• Pay attention to protocol compliance reviews (MCP init sequence, A2A card exchange)
• Async error handling in agent loops is a frequent review topic
• Understand MCP protocol lifecycle: initialize → list tools → call tool → shutdown

AI Agent Landscape (Quick Reference)

Key AI Topics for Search: ai-agents, agent-framework, llm-agent, multi-agent, model-context-protocol, mcp, mcp-server, a2a, agent2agent, agents-sdk, function-calling, tool-use, agent-orchestration, opencode, coding-agent, agentic-coding

See references/repo-discovery.md for full topic tables and search commands.

Common Mistakes

Safety Guardrails

Same as issue-fixer with two AI-domain additions:

• No live LLM API calls — never use real API keys for testing; use mocks/fixtures
• No model behavior modifications (prompt changes that affect output quality without understanding consequences → escalate)
• Respect API terms of service — don't patch rate limits or ToS restrictions
• Protocol compliance is critical — MCP/A2A changes must preserve spec compliance
• All inherited from issue-fixer: no main commits, no force-push, no test deletion, max 15 files, draft PR by default, no AI identification

Rationalization Table — Why Agents Skip This Skill and Why That Fails

Red Flags — STOP and Route Here

When you catch yourself thinking any of these, you are ABOUT to skip AI Agent domain specialization. Stop, load this skill, and use AI-specific discovery:

• "I'll just use --topic=linux, it's close enough"
• "Low stars = low quality" (for AI repos)
• "I know enough about LLMs, I don't need the domain rubric"
• "A quick real API call to verify won't hurt"
• "The user didn't say --discover but I'll use generic mode" (if they mentioned AI keywords)
• "I don't need to load issue-fixer, I know the workflow"
• "This AI repo's issues look like any other project's issues"
• "The reference list is fresh enough, no need to verify"

All of these mean: Stop. Load ai-agent-fixer. Then load issue-fixer for the workflow.

Preflight

Same as issue-fixer:

gh auth status          # GitHub CLI authenticated
git --version           # Git available

Refuse to work directly on main. Read repo instruction files before touching code.