diff --git a/.kiro/specs/active/024-generic-agent-interface-contrib/tasks.md b/.kiro/specs/active/024-generic-agent-interface-contrib/tasks.md
index dea52aed..22377f8f 100644
--- a/.kiro/specs/active/024-generic-agent-interface-contrib/tasks.md
+++ b/.kiro/specs/active/024-generic-agent-interface-contrib/tasks.md
@@ -15,4 +15,4 @@ No standardized input/output contract for agents. Hard to swap, compose, or benc
 - [x] 4. Refactor existing agents (fundamentals, sentiment, news, technical) to implement BaseAgent
 - [x] 5. Create AgentRegistry for pluggable agent discovery
 - [x] 6. Add agent benchmarking: compare outputs across different LLM backends
-- [ ] 7. Document interface for third-party agent contributions
+- [x] 7. Document interface for third-party agent contributions
diff --git a/docs/CONTRIBUTING_AGENTS.md b/docs/CONTRIBUTING_AGENTS.md
new file mode 100644
index 00000000..cc1cb041
--- /dev/null
+++ b/docs/CONTRIBUTING_AGENTS.md
@@ -0,0 +1,144 @@
+# Contributing Custom Agents
+
+This guide explains how to create and register third-party trading agents using the generic agent interface.
+
+## Interface Overview
+
+Every agent implements `BaseAgent.analyze(AgentInput) -> AgentOutput`. This contract ensures agents are interchangeable, composable, and benchmarkable.
+
+## Schemas
+
+### AgentInput
+
+```python
+from tradingagents.agents import AgentInput
+
+agent_input = AgentInput(
+    ticker="AAPL",
+    date="2025-01-15",
+    context={
+        "market_data": "...",
+        "news": "...",
+        "fundamentals": "...",
+        "sentiment": "...",
+        "technical_indicators": "...",
+    },
+)
+```
+
+| Field     | Type             | Required | Description                                      |
+|-----------|------------------|----------|--------------------------------------------------|
+| `ticker`  | `str`            | Yes      | Stock ticker symbol                              |
+| `date`    | `str`            | Yes      | Analysis date                                    |
+| `context` | `dict[str, str]` | No       | Optional data keyed by category (see keys above) |
+
+### AgentOutput
+
+| Field           | Type                                                        | Required | Description                    |
+|-----------------|-------------------------------------------------------------|----------|--------------------------------|
+| `rating`        | `"BUY" \| "OVERWEIGHT" \| "HOLD" \| "UNDERWEIGHT" \| "SELL"` | Yes      | 5-tier recommendation          |
+| `confidence`    | `float` (0.0–1.0)                                          | Yes      | Confidence in the rating       |
+| `price_targets` | `PriceTargets \| None`                                      | No       | Entry, target, and stop-loss   |
+| `thesis`        | `str`                                                       | Yes      | One-paragraph investment thesis|
+| `risk_factors`  | `list[str]`                                                 | No       | Key risks (defaults to `[]`)   |
+
+### PriceTargets
+
+| Field       | Type    | Description       |
+|-------------|---------|-------------------|
+| `entry`     | `float` | Entry price       |
+| `target`    | `float` | Target price      |
+| `stop_loss` | `float` | Stop-loss price   |
+
+## Creating a Custom Agent
+
+Subclass `BaseAgent` and implement `analyze`:
+
+```python
+from tradingagents.agents import BaseAgent, AgentInput, AgentOutput
+
+class MyCustomAgent(BaseAgent):
+    name: str = "my_custom_agent"
+
+    def __init__(self, llm) -> None:
+        self.llm = llm
+
+    def analyze(self, agent_input: AgentInput) -> AgentOutput:
+        # Your analysis logic here — call self.llm, fetch data, etc.
+        return AgentOutput(
+            rating="HOLD",
+            confidence=0.75,
+            thesis="The stock shows mixed signals...",
+            risk_factors=["Sector rotation risk", "Earnings uncertainty"],
+        )
+```
+
+The `__init__` signature is flexible — the only hard requirement is that `analyze` accepts `AgentInput` and returns `AgentOutput`.
+
+## Registering Your Agent
+
+Use `AgentRegistry` to make your agent discoverable:
+
+```python
+from tradingagents.agents import AgentRegistry
+
+registry = AgentRegistry()
+
+# Register a class (lazy instantiation with kwargs)
+registry.register("my_custom", MyCustomAgent, llm=my_llm)
+
+# Or register a pre-built instance
+agent = MyCustomAgent(llm=my_llm)
+registry.register("my_custom", agent)
+
+# Retrieve and use
+agent = registry.get("my_custom")
+output = agent.analyze(agent_input)
+
+# List all registered agents
+print(registry.list())
+```
+
+## Benchmarking Your Agent
+
+Compare your agent across LLM backends:
+
+```python
+from tradingagents.agents import (
+    AgentInput, LLMBackend, benchmark_agent, benchmark_agents,
+)
+
+agent_input = AgentInput(ticker="AAPL", date="2025-01-15")
+
+backends = [
+    LLMBackend(provider="openai", model="gpt-4o"),
+    LLMBackend(provider="anthropic", model="claude-sonnet-4-20250514"),
+    LLMBackend(provider="google", model="gemini-2.0-flash"),
+]
+
+# Single agent across backends
+report = benchmark_agent(MyCustomAgent, agent_input, backends)
+
+# Multiple agents across backends
+report = benchmark_agents(
+    [MyCustomAgent, FundamentalsAgent],
+    agent_input,
+    backends,
+)
+
+for row in report.summary():
+    print(row)
+```
+
+Each `BenchmarkResult` contains: `agent_name`, `provider`, `model`, `output`, `elapsed_seconds`, and `error` (if any).
+
+## Checklist
+
+Before submitting a PR with a new agent:
+
+- [ ] Subclasses `BaseAgent`
+- [ ] Sets a unique `name` class attribute
+- [ ] `analyze()` accepts `AgentInput` and returns `AgentOutput`
+- [ ] `rating` uses one of the 5 valid tiers
+- [ ] `confidence` is between 0.0 and 1.0
+- [ ] Agent is importable and added to `__all__` in `tradingagents/agents/__init__.py`