# Time Series Skill Router

> Read a validated problem-summary.txt and skills.catalog.yaml to recommend the smallest ordered sequence of repository skills for forecasting, classification, pattern discovery, feature aggregation, change point detection, or anomaly detection. Use this skill after a data-preparation skill has documented the problem, dataset, variables, validation plan, constraints, readiness, and leakage risks, or whenever an agent must choose and order skills from this repository without redundant library alternatives.

- Skill: `hibra999/time-series-skill-router` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add hibra999/time-series-skill-router`
- Raw SKILL.md: https://api.skillmd.com/api/skills/hibra999/time-series-skill-router/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: Hibra999 (https://skillmd.com/u/hibra999)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/hibra999/time-series-skill-router

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# Time-Series Skill Router

Turn the prepared problem contract into a minimal execution plan. Recommend skills only; do not run modeling or detection work unless the user also asks for execution.

## Routing Workflow

1. **Validate the input**
   - Read `problem-summary.txt` completely.
   - Require `task_type`, `problem`, `decision_or_output`, `dataset`, variable roles, `data_structure`, `validation_plan`, `leakage_risks`, `constraints`, `readiness`, and `open_questions`.
   - If the file is absent, stale, contradictory, or blocked on a fact that changes routing, run or recommend `forecasting-data-prep` for ordered time-series tasks or `ts-classification-data-prep` for classification, then resume routing with the refreshed file.

2. **Load the available skills**
   - Read `skills.catalog.yaml`; treat its IDs, stages, paths, dependencies, and preconditions as the source of truth.
   - Filter by the documented task, data shape, required output, constraints, and readiness.
   - Read each serious candidate's `SKILL.md` and only the references needed to verify support. Never invent capabilities from a skill name.

3. **Build the smallest valid chain**
   - Include the preparation skill that produced the summary as the first step, even when already completed.
   - Select one primary skill per required stage. Do not chain equivalent libraries for completeness.
   - Add feature aggregation only when the downstream method needs extracted tabular features.
   - Expand `depends_on` entries before their dependent skill and satisfy catalog preconditions.
   - Order work as preparation, optional feature construction, primary modeling or detection, then task-specific validation. Keep leakage checks active at every stage.

4. **Resolve ambiguity**
   - Prefer the candidate that directly supports the documented structure and output with the fewest adapters.
   - Respect an explicit library constraint when the skill supports the task.
   - When evidence cannot distinguish candidates, choose one primary recommendation and list at most two alternatives outside the execution chain with the fact that would change the choice.

5. **Return the plan**
   - Use the output contract below.
   - Keep reasons specific to `problem-summary.txt`; avoid generic capability summaries.
   - Do not proceed past a `readiness: blocked` item unless the plan first resolves that blocker.

## Output Contract

```text
Problem: <one-sentence interpretation>
Readiness: <ready or blocking fact>

Recommended order:
1. <skill-id> - <why it is required and its expected handoff>
2. <skill-id> - <why it is the best fit and its expected handoff>

Validation: <split/backtest, metrics, and leakage checks that remain mandatory>
Alternatives: <none, or up to two skill IDs with a concrete switch condition>
Assumptions: <none, or unresolved non-blocking assumptions>
```

## Reference

Read `references/routing-rules.md` to map task types to stages and apply the minimality rules consistently.