Skill: diamond2nv/competition-task-intelligence (version 0.5.0)

--- 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


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