LoongFlow — PEES Iterative Problem Solving
Use this skill when the user wants to iteratively improve a solution — optimization, evolution, structured retries with learning, or any task that benefits from multiple rounds of refinement rather than a one-shot attempt.
Step 1: Analyze and Advise
Before starting, analyze the task and advise the user on which mode to use. Present both options clearly:
Native PEES (recommended for simple tasks):
- Best for: single-file fixes, small features, bug fixes, focused improvements
- How it works: You run Plan-Execute-Evaluate-Summary iterations yourself within this conversation
- Pros: Fast, no setup, no external dependencies, transparent workspace with full history
- Cons: Limited to ~5 iterations, single-threaded, no population-based evolution
LoongFlow Engine (recommended for complex tasks):
- Best for: optimization problems, multi-file projects, tasks needing many iterations (50+), population-based evolution with diversity preservation
- How it works: Downloads the LoongFlow framework, creates a
general_agent task, runs evolutionary optimization in the background, monitors via cron
- Pros: Powerful evolutionary engine with multi-island model, Boltzmann selection, MAP-Elites diversity, checkpointing, cost tracking
- Cons: Requires
ANTHROPIC_API_KEY and ANTHROPIC_BASE_URL, setup time, runs as background process
- Source: https://github.com/baidu-baige/LoongFlow
Ask the user which mode they prefer before proceeding.
Step 2: Follow the Mode Guide
Once the user chooses, read the corresponding reference file for detailed instructions:
- Native PEES → Read
references/native-pees.md and follow it
- LoongFlow Engine → Read
references/engine-mode.md and follow it
Architecture Reference
LoongFlow supports three tiers for agent projects:
| Tier |
Description |
Best For |
| Simple |
ReAct loop + persistent memory |
Chatbots, tool calling, format conversion |
| Standard |
ReAct + self-evaluation + iterative improvement |
Code review, document generation, data analysis |
| Advanced |
PEES evolution loop with loongflow-memory |
Math optimization, algorithm design, NP-hard problems |
Complexity Assessment
Task Analysis
├── Only needs conversation + simple tools? → SIMPLE
├── Needs file operations or code generation?
│ ├── Has numerical evaluation metric? → ADVANCED
│ └── No numerical metric? → STANDARD
└── Needs iterative optimization?
├── Has clear scoring function? → ADVANCED
└── Qualitative improvement? → STANDARD
1---2name: loongflow3description: PEES (Plan-Execute-Evaluate-Summary) iterative problem-solving methodology with LoongFlow engine for complex tasks. Use when tasks need structured iteration, optimization, evolution, or when user mentions loongflow/PEES/PES.4---56# LoongFlow — PEES Iterative Problem Solving78Use this skill when the user wants to iteratively improve a solution — optimization, evolution, structured retries with learning, or any task that benefits from multiple rounds of refinement rather than a one-shot attempt.910## Step 1: Analyze and Advise1112Before starting, analyze the task and advise the user on which mode to use. Present both options clearly:1314**Native PEES (recommended for simple tasks):**15- Best for: single-file fixes, small features, bug fixes, focused improvements16- How it works: You run Plan-Execute-Evaluate-Summary iterations yourself within this conversation17- Pros: Fast, no setup, no external dependencies, transparent workspace with full history18- Cons: Limited to ~5 iterations, single-threaded, no population-based evolution1920**LoongFlow Engine (recommended for complex tasks):**21- Best for: optimization problems, multi-file projects, tasks needing many iterations (50+), population-based evolution with diversity preservation22- How it works: Downloads the LoongFlow framework, creates a `general_agent` task, runs evolutionary optimization in the background, monitors via cron23- Pros: Powerful evolutionary engine with multi-island model, Boltzmann selection, MAP-Elites diversity, checkpointing, cost tracking24- Cons: Requires `ANTHROPIC_API_KEY` and `ANTHROPIC_BASE_URL`, setup time, runs as background process25- Source: https://github.com/baidu-baige/LoongFlow2627**Ask the user which mode they prefer before proceeding.**2829## Step 2: Follow the Mode Guide3031Once the user chooses, read the corresponding reference file for detailed instructions:3233- **Native PEES** → Read `references/native-pees.md` and follow it34- **LoongFlow Engine** → Read `references/engine-mode.md` and follow it3536## Architecture Reference3738LoongFlow supports three tiers for agent projects:3940| Tier | Description | Best For |41|------|-------------|----------|42| **Simple** | ReAct loop + persistent memory | Chatbots, tool calling, format conversion |43| **Standard** | ReAct + self-evaluation + iterative improvement | Code review, document generation, data analysis |44| **Advanced** | PEES evolution loop with loongflow-memory | Math optimization, algorithm design, NP-hard problems |4546### Complexity Assessment4748```49Task Analysis50├── Only needs conversation + simple tools? → SIMPLE51├── Needs file operations or code generation?52│ ├── Has numerical evaluation metric? → ADVANCED53│ └── No numerical metric? → STANDARD54└── Needs iterative optimization?55 ├── Has clear scoring function? → ADVANCED56 └── Qualitative improvement? → STANDARD57```
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