Agent Pattern Verification Skill

Verify AI agent code for common anti-patterns that can cause infinite loops, runaway retries, tool mismatches, and context overflow. This skill complements ControlKeel's security and compliance validation with agent-specific pattern detection.

When to Use

Trigger this skill when:

• Reviewing agent code before deployment
• Validating LangGraph, CrewAI, AutoGen, or LangChain workflows
• User asks about "agent safety", "infinite loops", "retry limits", or "tool consistency"
• Before merging agent workflow changes

Verification Flow

1. Call `ck_context` to load the session context, risk tier, and existing findings
2. Detect agent framework by scanning imports and configuration files
3. Run pattern checks using the checklist below
4. Persist findings with ck_finding for each issue discovered
5. Generate structured report with pattern-matched vs heuristic tagging

Pattern Checks

All checks are classified as:

• [PATTERN] - Mechanical check, high reliability (applied exactly as specified)
• [HEURISTIC] - Judgment-based, best-effort (requires interpretation)

1. Loop Safety [PATTERN]

Apply mechanically. Do not pass a loop because it "looks like it might terminate."

Heuristic Fallback: After applying pattern table, scan for:

• Loops where termination depends entirely on external/runtime state with no timeout
• Generator functions that yield indefinitely without documented exit
• Event/polling loops without timeout parameters
• Recursive call chains across multiple functions without depth tracking

Flag as ⚠️ Warning: "Potential unbounded loop not matching known patterns — verify termination condition manually"

2. Retry Limit Enforcement [PATTERN]

Apply mechanically. If required parameter is absent, flag as ❌ Issue.

Python — Decorator-based:

Python — HTTP client retry:

Python — AWS SDK (boto3):

Note: boto3 without explicit retry config uses SDK defaults (3 attempts) — do not flag absence.

JavaScript/TypeScript:

Custom retry loops (all languages):

Heuristic Fallback: Scan for:

• Functions/decorators with "retry" in name not in tables above
• Imported modules with "retry" in package name (e.g. stamina, aiohttp_retry)
• Loops with sleep + exception handling + re-invocation without visible counter
• Config keys like max_retries, retry_count, attempts

Flag as ⚠️ Warning: "Potential retry pattern not matching known libraries — verify retry bounds manually"

3. Tool Registry Consistency [PATTERN]

Step 1: Collect defined tools

Scan tool definition files. A name found by any pattern counts as registered.

Python — decorator patterns:

Python — dict/list patterns:

TypeScript/JavaScript:

Step 2: Collect tool references from prompts

Scan .md, .txt, prompts.py for backtick-quoted identifiers naming capabilities.

Step 3: Cross-reference

Heuristic: Find where tools are defined and where LLM is invoked. If tools exist but are never connected to the LLM call, flag as ❌ Issue: "Tools defined but never connected to LLM invocation"

Heuristic Fallback: Scan for tool-like structures:

• Dicts with both "description" and "parameters" keys
• Functions with structured docstrings (name, params, return)
• Variables named tools, tool_list, available_tools, functions
• Classes with run(), execute(), or __call__() methods

Include in count and note: "Tool detected via heuristic — verify this is an intended agent tool."

4. Context Size Awareness [PATTERN]

Formula: token_estimate = len(file_content_chars) / 4

Exclude: skills/ directories (loaded on demand, not embedded)

Heuristic Fallback:

• Estimates within 20% of threshold → flag with tokenizer recommendation
• Dynamic prompts (f-strings, .format()) → flag if template alone is large
• Multiple concatenated prompts → estimate combined size
• Prompts with includes → note effective size may be larger

5. LangGraph Graph Cycle Analysis [PATTERN]

(Only when LangGraph is detected)

Detection steps: a. Find graph file (graph.py, graph.ts, or file with StateGraph/MessageGraph) b. Build edge map:

• workflow.add_edge(source, dest) — unconditional edge
• workflow.add_conditional_edges(source, fn, mapping) — extract destinations from mapping

c. Identify cycles: nodes reachable from themselves d. For each cycle, check if END (or "__end__") is reachable via conditional edge

Framework Detection

Identify the agent framework by checking imports:

Also check for framework config files: langgraph.json, crew.yaml.

File Discovery

Priority files:

• graph.py, graph.ts - Agent workflow definitions
• tools.py, tools.ts, tools/*.py, tools/*.ts - Tool implementations
• state.py, state.ts - State schemas
• prompts.py, prompts/*.md, system.md - Prompt templates
• agent.py, agent.ts - Main agent logic

Directories to check:

• src/agent/, agent/, src/, project root
• lib/, app/, packages/

Exclude from analysis:

• skills/ directory — these are skill definitions, not agent system prompts

Report Format

Generate a structured report with the following format:

# Agent Pattern Verification Report

**Project:** [project name or path]
**Date:** [current date]
**Framework:** [LangGraph | CrewAI | AutoGen | LangChain | Custom | None]
**Files analyzed:** [count]

## Summary

✅ X checks passed | ⚠️ Y warnings | ❌ Z issues

### By Category

| Category | Pass | Warn | Issue |
|----------|------|------|-------|
| Loop Safety | X | X | X |
| Retry Limits | X | X | X |
| Tool Consistency | X | X | X |
| Context Size | X | X | X |
| Graph Cycles | X | X | X |

## Detailed Findings

> `[P]` = pattern-matched (structurally reliable) · `[H]` = heuristic (best-effort judgment)

### ✅ Passing

- `[P]` All retry decorators have stop conditions
- `[P]` Tool registry consistent with prompt references

### ⚠️ Warnings

- `[P|H]` [Check name]: [Description]
  - **Location:** [file:line]
  - **Category:** [Loop Safety | Retry Limits | Tool Consistency | Context Size]
  - **Suggestion:** [How to address]

### ❌ Issues

- `[P|H]` [Check name]: [Description]
  - **Location:** [file:line]
  - **Category:** [Loop Safety | Retry Limits | Tool Consistency | Context Size]
  - **Rule:** [Which rule this violates]
  - **Fix:** [Specific remediation steps]

## Recommendations

1. **[Highest priority]** - [Specific action]
2. **[Second priority]** - [Specific action]
3. [Additional improvements]

---

*Report generated by ControlKeel Agent Pattern Verification v1.0*

Integration with CK Tools

Using ck_validate

For pattern-matched checks that can be expressed as code patterns, use ck_validate with:

• artifact_type: "source"
• intended_use: "code"
• domain_pack: "software" or relevant domain
• security_workflow_phase: "analysis"

Using ck_finding

Persist each issue with structured metadata:

ck_finding(
  category: "security",
  finding_family: "agent_pattern",
  affected_component: "agent/loop.py:45",
  severity: "warning",  # or "issue"
  evidence_type: "code_pattern",
  description: "Potential unbounded loop detected",
  suggested_fix: "Add MAX_ITERATIONS constant or break condition",
  check_type: "pattern"  # or "heuristic"
)

Using ck_context

Call at the start to:

• Load existing findings to avoid duplicates
• Understand the risk tier and compliance profile
• Get workspace context for file discovery

Additional Resources

For detailed check specifications and examples, see references/pattern-checklist.md.