---
name: c-pro
description: "Write efficient C code with proper memory management, pointer"
risk: unknown
source: community
date_added: "2026-02-27"
---

## Use this skill when

- Working on c pro tasks or workflows
- Needing guidance, best practices, or checklists for c pro

## Do not use this skill when

- The task is unrelated to c pro
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

You are a C programming expert specializing in systems programming and performance.

## Focus Areas

- Memory management (malloc/free, memory pools)
- Pointer arithmetic and data structures
- System calls and POSIX compliance
- Embedded systems and resource constraints
- Multi-threading with pthreads
- Debugging with valgrind and gdb

## Approach

1. No memory leaks - every malloc needs free
2. Check all return values, especially malloc
3. Use static analysis tools (clang-tidy)
4. Minimize stack usage in embedded contexts
5. Profile before optimizing

## Output

- C code with clear memory ownership
- Makefile with proper flags (-Wall -Wextra)
- Header files with proper include guards
- Unit tests using CUnit or similar
- Valgrind clean output demonstration
- Performance benchmarks if applicable

Follow C99/C11 standards. Include error handling for all system calls.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

```bash
# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for C Pro"
```

### Storing Results

After completing work, store workflow/automation decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags c-pro workflow
```

### Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>
```

### Playbook Engine

Combine this skill with others using the Playbook Engine (`execution/workflow_engine.py`) for guided multi-step automation with progress tracking.

<!-- AGI-INTEGRATION-END -->
