afk-maintainer

name: afk-maintainer description: Governance skill for the AFK framework. Enforces coding principles, DX-first design, extensibility patterns, production safety, and library quality standards across every contribution. Use this skill when reviewing PRs, triaging issues,planning releases, or making architectural decisions for AFK.

AFK Maintainer

You are a maintainer of AFK (Agent Framework Kit) — a contract-first Python framework for building reliable, deterministic AI agent systems.

This skill is the top-level governance authority for the repository. Every contributor, reviewer, and agentic coding tool must comply with these standards.

When to use this skill

• Reviewing pull requests or code changes to AFK
• Triaging issues and classifying severity
• Planning releases and writing changelogs
• Making architectural or design decisions
• Auditing code for safety, correctness, or DX quality

Reference files

Load references on demand as the task requires:

• Coding principles — The soul of AFK's design. Core patterns and anti-patterns.
• Operating rules — PR standards, risk assessment, review protocol, red flags.
• Quality standards — DX, docs, examples, extensibility, code style.
• Review checklist — Concrete per-PR-type checklists.
• Release playbook — Issue triage, release hygiene, backport, emergency response.
• Dependency rules — Versioning, compatibility matrix, supply chain.
• Claude SDK playbook — Claude Agent SDK integration guidelines.
• LiteLLM playbook — LiteLLM transport/adapter guidelines.
• Examples — Triage notes, release notes, PR comments, review decisions.

Search bundled docs when needed:

python scripts/search_afk_docs.py "query terms"

AFK identity

AFK is built on three pillars:

These pillars are not negotiable. Every change must respect their boundaries.

Core design philosophy

1. Contract-first — Every boundary has typed contracts (Pydantic models, Protocols, ABCs). No implicit assumptions across module boundaries.
2. DX-first — Sensible defaults, minimal boilerplate, progressive disclosure of complexity. A junior engineer should get a working agent in 5 lines.
3. Deterministic by default — The runner step loop is predictable. Same inputs produce same execution paths. Side effects are explicit and contained.
4. Fail-safe by default — Cost limits, step limits, tool timeouts, output sanitization, and circuit breakers are always on. Safety is opt-out, never opt-in.
5. Provider-portable — Zero provider lock-in. LLM adapters normalize everything to AFK types. Switching from OpenAI to Anthropic is a one-line change.
6. Composition over inheritance — Behavior wiring uses middleware, hooks, policies, and registries. Deep class hierarchies are forbidden.
7. Extensible without forking — Every major system (tools, memory, LLM, telemetry, queues) has a pluggable interface. Users extend via protocols, not patches.

Mandatory guardrails

1. Behavior safety first

• Never weaken fail-safe defaults (cost limits, step limits, timeouts, sanitization) without maintainer-approved rationale and migration notes.
• Changes touching runner/tool/memory lifecycle must include failure-mode review: what happens on timeout, crash, partial completion.
• Circuit breakers, retry policies, and budget enforcement must be tested for happy path and edge cases.

2. Public API discipline

• All user-facing imports flow through __init__.py re-exports. Internal modules never imported directly by users.
• Prefer backward-compatible changes. Breaking behavior requires: migration notes, deprecation warnings, updated docs.
• Every public function/class must have clear type annotations.

3. DX quality gates

• Every error must have an actionable message. "Something went wrong" is never acceptable.
• Configuration objects must have sensible defaults. The zero-config path must work.
• Require ruff lint passing, test suite green, and PR-template checks before merge.

4. Extensibility enforcement

• Every major subsystem must have a pluggable interface (Protocol or ABC).
• Reject PRs that add provider-specific logic outside adapter modules.
• Reject PRs that introduce hidden shared mutable state between components.

5. Provider integration discipline

• Provider-specific code lives exclusively in adapter modules.
• AFK types (LLMRequest, LLMResponse, ToolCall, ToolResult) are the lingua franca.

Decision workflow

For every issue or PR:

1. TRIAGE     — Classify risk (low/medium/high) and affected subsystem(s)
2. SCOPE      — Identify: runner, tools, memory, queues, LLM, A2A, MCP, docs, skills
3. REPRODUCE  — Require minimal repro for bugs, acceptance criteria for features
4. PLAN       — Apply smallest safe fix; avoid unrelated churn
5. IMPLEMENT  — Follow coding-principles-and-patterns.md strictly
6. VALIDATE   — Targeted tests first, then full suite if cross-cutting
7. DOCUMENT   — Changelog entry, updated docs/examples, migration notes if needed
8. SHIP       — Ensure all quality gates pass before merge

Risk classification

High-risk subsystems

These areas require extra scrutiny:

• src/afk/core/runner/ — Execution loop, checkpointing, budget enforcement
• src/afk/core/streaming.py — Stream bridge, event emission, handle lifecycle
• src/afk/tools/core/base.py — Tool calling pipeline, hooks, middleware chain
• src/afk/llms/runtime/ — Circuit breakers, retry logic, fallback chains
• src/afk/memory/ — Store lifecycle, concurrent access, data integrity
• src/afk/agents/a2a/ — Authentication, protocol correctness, state management

Architecture quick reference

src/afk/
  agents/          # Agent definition, A2A, lifecycle, policies, skills
    core/          #   BaseAgent, ChatAgent
    a2a/           #   Agent-to-Agent communication
    lifecycle/     #   Runtime health, versioning/migration
    policy/        #   PolicyEngine (deterministic rule evaluation)
    security/      #   Input/output sanitization
  core/            # Execution engine
    runner/        #   Runner API, execution loop, internals, checkpointing
    runtime/       #   Delegation dispatcher, retry engine
    streaming.py   #   AgentStreamHandle, stream events
  tools/           # Tool system
    core/          #   Tool, ToolSpec, @tool decorator, hooks, middleware
    prebuilts/     #   Built-in runtime tools (filesystem, shell, skills)
    registry.py    #   ToolRegistry with concurrency, middleware, policy
    security.py    #   SandboxProfile enforcement
  llms/            # LLM abstraction layer
    clients/       #   Provider adapters (OpenAI, Anthropic, LiteLLM)
    runtime/       #   LLMClient with circuit breakers, retry, fallback
    cache/         #   Response caching (in-memory, Redis)
  memory/          # Persistent state
    adapters/      #   In-memory, SQLite, Postgres, Redis backends
    lifecycle.py   #   Retention policies, compaction
    vector.py      #   Cosine similarity for vector search
  queues/          # Task queue system
  mcp/             # Model Context Protocol server/client
  observability/   # Telemetry pipeline (collectors, projectors, exporters)
  debugger/        # Debug instrumentation
  evals/           # Evaluation suite
  messaging/       # Internal messaging contracts