How do I install the Worldenergydata Source Readiness skill?

Run npx skillmds@latest add vamseeachanta/worldenergydata-source-readiness-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Worldenergydata Source Readiness safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Worldenergydata Source Readiness?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Worldenergydata Source Readiness free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Worldenergydata Source Readiness?

vamseeachanta (@vamseeachanta) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Worldenergydata Source Readiness by vamseeachanta

Name: Worldenergydata Source Readiness
Author: vamseeachanta

Worldenergydata Source Readiness

Summarize worldenergydata source readiness, freshness, data locations, and scheduler blockers from repo metadata. Use when asked for worldenergydata data completeness, latest data dates, data locations, refresh status, scheduler source health, or acceptance-criteria inputs for data-source freshness.

vamseeachanta Updated Sep 21, 2026

File contents

WorldEnergyData Source Readiness

Quick Start

From the worldenergydata repo root, run:

python .claude/skills/worldenergydata-source-readiness/scripts/source_readiness_summary.py

For JSON output:

python .claude/skills/worldenergydata-source-readiness/scripts/source_readiness_summary.py --format json

What To Report

Use this skill to answer, in one pass:

data group / module name
catalog status and freshness status
latest known date
repo-local data location
external data location, if metadata records one
scheduler output location, if configured
scheduler success manifest timestamp, if present
dataset count, record count, file count, and data size
blocker issue or operational gap, when known from current GitHub issue state
contract freshness/completeness status from data/source-refresh-acceptance-contract.json, when acceptance decisions are needed

Source Of Truth Order

docs/data/source-refresh-acceptance-criteria.md and data/source-refresh-acceptance-contract.json for pass/fail acceptance decisions.
data/freshness-scorecard.json for module-level status.
data/modules/<module>/_metadata.json for file counts, sizes, external roots, and newest file modified date.
data/modules/<module>/manifest.json for successful scheduler refresh timestamp.
config/scheduler/scheduler_config.yml for scheduler job names and output directories.
data/catalog.yaml for dataset paths and row counts.

If these disagree, state the disagreement instead of collapsing it. In particular, distinguish:

metadata refresh date: when the module inventory was generated
newest file modified date: newest known local file timestamp
scheduler success date: when a scheduler job last completed successfully
source data vintage: the newest business/date field inside the dataset; do not claim this unless inspected directly

Run the contract validator before using a source summary as acceptance evidence:

python scripts/audit/validate_source_refresh_contract.py

Acceptance Criteria Drafting Pattern

For Tier-A data-source readiness, require each source to expose:

source_data_latest_date
last_successful_refresh
data_location
record_count
freshness_status
refresh_cadence
blocker_issue or none

Treat a source as green only when it has a successful scheduler manifest or another documented refresh proof within its cadence. Use docs/data/source-refresh-acceptance-criteria.md for the enum meanings, scorecard-to-contract mapping, source-date basis rules, and scheduler manifest rule.

Details

See references/readiness-fields.md for field definitions and interpretation rules.

---name: worldenergydata-source-readiness-2description: Summarize worldenergydata source readiness, freshness, data locations, and scheduler blockers from repo metadata. Use when asked for worldenergydata data completeness, latest data dates, data locations, refresh status, scheduler source health, or acceptance-criteria inputs for data-source freshness.---# WorldEnergyData Source Readiness## Quick StartFrom the worldenergydata repo root, run:```bashpython .claude/skills/worldenergydata-source-readiness/scripts/source_readiness_summary.py```For JSON output:```bashpython .claude/skills/worldenergydata-source-readiness/scripts/source_readiness_summary.py --format json```## What To ReportUse this skill to answer, in one pass:- data group / module name- catalog status and freshness status- latest known date- repo-local data location- external data location, if metadata records one- scheduler output location, if configured- scheduler success manifest timestamp, if present- dataset count, record count, file count, and data size- blocker issue or operational gap, when known from current GitHub issue state- contract freshness/completeness status from `data/source-refresh-acceptance-contract.json`, when acceptance decisions are needed## Source Of Truth Order1. `docs/data/source-refresh-acceptance-criteria.md` and `data/source-refresh-acceptance-contract.json` for pass/fail acceptance decisions.2. `data/freshness-scorecard.json` for module-level status.3. `data/modules/<module>/_metadata.json` for file counts, sizes, external roots, and newest file modified date.4. `data/modules/<module>/manifest.json` for successful scheduler refresh timestamp.5. `config/scheduler/scheduler_config.yml` for scheduler job names and output directories.6. `data/catalog.yaml` for dataset paths and row counts.If these disagree, state the disagreement instead of collapsing it. In particular, distinguish:- **metadata refresh date**: when the module inventory was generated- **newest file modified date**: newest known local file timestamp- **scheduler success date**: when a scheduler job last completed successfully- **source data vintage**: the newest business/date field inside the dataset; do not claim this unless inspected directlyRun the contract validator before using a source summary as acceptance evidence:```bashpython scripts/audit/validate_source_refresh_contract.py```## Acceptance Criteria Drafting PatternFor Tier-A data-source readiness, require each source to expose:- `source_data_latest_date`- `last_successful_refresh`- `data_location`- `record_count`- `freshness_status`- `refresh_cadence`- `blocker_issue` or `none`Treat a source as green only when it has a successful scheduler manifest or another documented refresh proof within its cadence.Use `docs/data/source-refresh-acceptance-criteria.md` for the enum meanings, scorecard-to-contract mapping, source-date basis rules, and scheduler manifest rule.## DetailsSee [references/readiness-fields.md](references/readiness-fields.md) for field definitions and interpretation rules.

Frequently asked questions