Agent skill
control-loop-extraction
Extract and analyze agent reasoning loops, step functions, and termination conditions. Use when needing to (1) understand how an agent framework implements reasoning (ReAct, Plan-and-Solve, Reflection, etc.), (2) locate the core decision-making logic, (3) analyze loop mechanics and termination conditions, (4) document the step-by-step execution flow of an agent, or (5) compare reasoning patterns across frameworks.
Install this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dowwie/control-loop-extraction
SKILL.md
Control Loop Extraction
Extracts and documents the core agent reasoning loop from framework source code.
Process
- Locate the loop - Find the main agent execution loop
- Classify the pattern - Identify ReAct, Plan-and-Solve, Reflection, or Tree-of-Thoughts
- Extract the step function - Document the LLM → Parse → Decide flow
- Map termination - Catalog all loop exit conditions
Reasoning Pattern Identification
Pattern Signatures
ReAct (Reason + Act)
# Signature: Thought → Action → Observation cycle
while not done:
thought = llm.generate(prompt) # Reasoning
action = parse_action(thought) # Action selection
observation = execute(action) # Environment feedback
prompt = update_prompt(observation) # Loop continuation
Plan-and-Solve
# Signature: Upfront planning, then execution
plan = llm.generate("Create a plan for...")
for step in plan.steps:
result = execute_step(step)
if needs_replan(result):
plan = replan(...)
Reflection
# Signature: Act → Self-critique → Adjust
while not done:
action = llm.generate(prompt)
result = execute(action)
critique = llm.generate(f"Evaluate: {result}")
if critique.needs_adjustment:
prompt = adjust_approach(critique)
Tree-of-Thoughts
# Signature: Branch → Evaluate → Select
thoughts = [generate_thought() for _ in range(n)]
scores = [evaluate(t) for t in thoughts]
best = select_best(thoughts, scores)
Step Function Analysis
The "step function" is the atomic unit of agent execution. Extract:
- Input Assembly - How context is constructed for the LLM
- LLM Invocation - The actual model call
- Output Parsing - How raw output becomes structured actions
- Action Dispatch - Tool execution vs. final response routing
Key Code Patterns
# Common step function structure
def step(self, state):
# 1. Assemble input
messages = self._build_messages(state)
# 2. Call LLM
response = self.llm.invoke(messages)
# 3. Parse output
parsed = self._parse_response(response)
# 4. Dispatch
if parsed.is_tool_call:
return self._execute_tool(parsed.tool, parsed.args)
else:
return AgentFinish(parsed.final_answer)
Termination Condition Catalog
Common Termination Patterns
| Condition | Implementation | Risk |
|---|---|---|
| Step limit | if step_count >= max_steps |
May cut off valid execution |
| Token limit | if total_tokens >= max_tokens |
May truncate mid-thought |
| Explicit finish | if action.type == "finish" |
Relies on LLM cooperation |
| Timeout | if elapsed > timeout |
Wall-clock unpredictable |
| Loop detection | if state in seen_states |
Requires state hashing |
| Error threshold | if error_count >= max_errors |
May exit on recoverable errors |
Anti-Pattern: No Termination Guard
# DANGEROUS: No exit condition
while True:
result = agent.step()
if result.is_done: # What if LLM never outputs done?
break
Fix: Always include a step counter:
for step in range(max_steps):
result = agent.step()
if result.is_done:
break
else:
logger.warning("Hit max steps limit")
Output Template
## Control Loop Analysis: [Framework Name]
### Reasoning Topology
- **Pattern**: [ReAct | Plan-and-Solve | Reflection | Tree-of-Thoughts | Hybrid]
- **Location**: `path/to/agent.py:L45-L120`
### Step Function
- **Input Assembly**: [Description of context building]
- **LLM Call**: [Method and parameters]
- **Parser**: [How output is structured]
- **Dispatch Logic**: [Tool vs Finish decision]
### Termination Conditions
1. [Condition 1 with code reference]
2. [Condition 2 with code reference]
3. ...
### Loop Detection
- **Method**: [Heuristic | State hash | None]
- **Implementation**: [Code reference or N/A]
Integration Points
- Prerequisite:
codebase-mappingto identify agent files - Feeds into:
comparative-matrixfor pattern comparison - Feeds into:
architecture-synthesisfor new loop design
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