Build and maintain a structured PDE equation registry, analyze competition tasks (difficulty, bottlenecks, score projections), generate strategic recommendat...
---
name: competition-task-intelligence
title: Competition Task Intelligence — PDE Equation Registry, Task Analysis, and Strategic Advising
description: >
Build and maintain a structured PDE equation registry, analyze competition tasks
(difficulty, bottlenecks, score projections), generate strategic recommendations
for research focus, and expose this intelligence via CLI and MCP tools.
category: mlops
author: Li Shen
version: 1.0.0
tags: [mlops, competition, strategy, equations, analysis, planning, pde, task-intelligence]
metadata:
hermes:
tags: [mlops, pde, competition, strategy, equations, analysis, ai4s]
homepage: https://github.com/diamond2nv/expflow
related_skills:
- expflow-pipeline-hpo
- experiment-lifecycle-governance
- clearml-metrics-logging-pattern
- agent4pde-competition-scoring
- pde-experiment-hyperparameters
created: 2026-05-19
updated: 2026-05-19
---
# Competition Task Intelligence
## Overview
System for structured PDE equation management and competition task analysis. Provides:
1. **PDE Equation Registry** — structured metadata (LaTeX, dimensions, params, datasets) for 11+ PDEs
2. **Task Analysis** — per-task difficulty assessment, bottleneck identification, proven strategy catalog
3. **Score Projection** — optimistic/expected/conservative score estimates with confidence levels
4. **Strategic Advising** — which task to focus on, suggested schedule, rationale
5. **CLI + MCP** — `expflow analyze` command group and MCP tools
## Installation
```bash
pip install expflow-pde
```
## Architecture
```
expflow_pde/equations.py ──── PDE equation static registry (11+ equations)
expflow_pde/analyze.py ──── Analysis engine (task intelligence, strategy)
expflow_pde/cli_analyze.py ──── CLI: analyze task/equations/status/advise
expflow_pde/mcp_server.py ──── MCP: exp_compare_scores, exp_list_workers
```
## 1. PDE Equation Registry
Each equation entry in `EQUATIONS` dict includes: full name, LaTeX, dimensions, parameters, competition task mapping, metrics, solver, data samples, and competition info.
### API
```python
from expflow_pde.equations import (
get_equations(), # All 11+ equations
get_equation(name), # Single equation
list_equations_for_task(task_id), # task1/task2/task3
get_equation_metrics(name, task), # Relevant STANDARD_METRICS
list_equation_names(), # Sorted names
list_competition_equations(), # Only competition equations
)
```
## 2. Task-Level Intelligence
### CLI
```bash
# Strategic advising (primary entry point)
expflow analyze advise
# Per-task analysis
expflow analyze task task1
expflow analyze task task3
# Equation reference
expflow analyze equations --task competition
# Competition overview
expflow analyze status
```
### Example Output
```
expflow analyze status
Task Score Difficulty Status Priority
────────────────────────────────────────────────────────────────────
task1 142/150 🟡 medium 🔴 In Progress high
task2 -/150 🔴 hard ⚪ Not Started low
task3 -/350 🔥 very_hard ⚪ Not Started medium
总分: 142/650 (508 pts remaining)
```
### Score Estimation
```python
from expflow_pde.analyze import estimate_score_potential, get_strategic_recommendation
estimates = estimate_score_potential("task1")
# Returns: {"optimistic": 148, "expected": 145, "conservative": 140, "confidence": "high"}
rec = get_strategic_recommendation()
# Returns: {"primary_focus": "task1", "remaining_headroom": {...}, "suggested_schedule": {...}}
```
### Difficulty Classification
| Label | Icon | Example | Meaning |
|-------|:----:|---------|---------|
| easy | 🟢 | Baseline tasks | High confidence, proven methods exist |
| medium | 🟡 | Task 1 | Known bottlenecks, clear path forward |
| hard | 🔴 | Task 2 | Multiple unknown challenges |
| very_hard | 🔥 | Task 3 (KS) | Chaotic dynamics, exponential error growth |
## Integration with Other Systems
### With experiment-lifecycle-governance
`compare-scores` gating builds on equation metrics from this system. When adding a new equation, its metrics must exist in `STANDARD_METRICS` for gating to work.
### With analyze-experiment-autoregressive-degradation
Chain: `analyze advise → decide task → run experiment → analyze degradation → feed back to _TASK_META`.
## Pitfalls
1. **`_TASK_META` becomes stale** — hardcoded scores must be updated after each submission
2. **Competition deadline hardcoded** — `get_strategic_recommendation()` has `remaining_days` from `2026-05-27`
3. **Scoring formula duplication** — Task 3 formulae are in both `equations.py` and `analyze.py`; keep synced
4. **No clearml import in analyze** — `analyze.py` uses only pure Python/stdlib for fast CLI startup
don't have the plugin yet? install it then click "run inline in claude" again.