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data-extraction

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Mis à jour7 juillet 2026 à 15:52

Use when the user wants to extract data, pull data from a source, load a CSV or JSON export into the workspace, connect a data source, refresh data, ingest raw platform exports, or normalize raw data for the marketing-analytics skills. Trigger on 'extract data', 'pull data from <platform>', 'load CSV/JSON into workspace', 'connect a data source', 'refresh data', 'normalize raw data', 'import my ad export', 'get the campaign data in', or 'set up the workspace'. This is the upstream skill every other marketing-analytics skill depends on: it lands source exports in workspace/raw/ and writes validated, normalized files to workspace/processed/. Run this first when a downstream skill reports a missing or malformed workspace input. When the user wants analysis, modeling, or reporting rather than getting data in, route to the matching analytics skill instead (see references/skill-index.md).

Installation

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Source

weisberg

weisberg/agile_agentic_analytics

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Analystes en études de marché et spécialistes en marketingProfessions des affaires et des opérations financières·SOC 13-1161

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name	data-extraction
description	Use when the user wants to extract data, pull data from a source, load a CSV or JSON export into the workspace, connect a data source, refresh data, ingest raw platform exports, or normalize raw data for the marketing-analytics skills. Trigger on 'extract data', 'pull data from <platform>', 'load CSV/JSON into workspace', 'connect a data source', 'refresh data', 'normalize raw data', 'import my ad export', 'get the campaign data in', or 'set up the workspace'. This is the upstream skill every other marketing-analytics skill depends on: it lands source exports in workspace/raw/ and writes validated, normalized files to workspace/processed/. Run this first when a downstream skill reports a missing or malformed workspace input. When the user wants analysis, modeling, or reporting rather than getting data in, route to the matching analytics skill instead (see references/skill-index.md).
disable-model-invocation	false

Data Extraction & Workspace Ingestion

Skill ID: data-extraction Priority: P0 — Foundational (upstream of the entire portfolio) Role: Data-preparation. This skill lands and normalizes source data. It does NOT model, score, forecast, or interpret — that is the job of the downstream analytics skills. It is fix-allowed only within workspace/raw/ and workspace/processed/.

Contract

When to use

A downstream skill (attribution-analysis, paid-media, clv-modeling, experimentation, email-analytics, web-analytics, funnel-analysis, audience-segmentation, seo-content, crm-lead-scoring, social-analytics, voc-analytics) reports a missing workspace input.
The user has a CSV/JSON export, a manual copy-paste of a report, or a described API pull and wants it landed and normalized for analysis.
The workspace is empty or stale and needs a refresh.

When NOT to use

The data is already in workspace/processed/ and the user wants analysis → route to the matching analytics skill.
The user wants a live platform connection this skill cannot make on its own. This skill does not hold credentials or call vendor APIs directly; it ingests exports and documents the pull. Describe the API pull generically and land the resulting file.
The user wants reporting, dashboards, or interpretation → reporting and the domain skills own that.

Inputs (any one of)

Source type	What you receive	How to handle
CSV/JSON export	A file path or uploaded file	Copy verbatim into `workspace/raw/`.
API pull (described)	Platform, date range, fields	Document the request; land the returned file in `workspace/raw/`.
Manual paste	Tabular text in the conversation	Write it to `workspace/raw/<slug>.csv` before validating.

Data contract references

shared/schemas/data_contracts.md — canonical data contract field/type/required definitions per dataset.
shared/utils/common_transforms.py — load_csv_with_validation, normalize_dates, detect_missing_windows, save_json_output, ensure_directory.
references/skill-index.md — the portfolio map naming which skill consumes which file.

Outputs

File	Purpose
`workspace/raw/<name>.csv` or `.json`	Source export, landed verbatim (audit trail).
`workspace/processed/<name>.csv`	Type-coerced, date-normalized, deduplicated copy for analysis.
`workspace/raw/extraction_manifest.json`	Row counts, date ranges, null rates, duplicate keys, and the source note per file.

Canonical target filenames (match these so downstream skills find their inputs)

Downstream skill	Expected `workspace/raw/` file(s)
paid-media, attribution-analysis	`campaign_spend_{platform}.csv`; attribution also `conversions.csv`, `external_factors.csv`
paid-media	`search_terms_{platform}.csv`, `creative_performance_{platform}.csv`
clv-modeling, audience-segmentation	`transactions.csv`; also `subscriptions.csv`, `behavioral_events.csv`
experimentation	`experiment_data.csv`, `pre_experiment_covariates.csv`
email-analytics	`email_sends.csv`, `email_flows.csv`
crm-lead-scoring	`crm_leads.csv`, `lead_activities.csv`
web-analytics, funnel-analysis	`ga4_reports.csv`, `events.csv`; funnel also `revenue.csv`
seo-content	`search_console.csv`, `content_inventory.csv`
social-analytics	`social_performance_{platform}.csv`, `social_mentions.csv`, `competitor_social.csv`
voc-analytics	`survey_responses.csv`
competitive-intel	`competitor_data.csv`

paid-media further normalizes its raw files into workspace/processed/unified_media_performance.csv; this skill produces the per-file processed copies those steps read.

Workflow

Complete each step before the next. If a gate fails, STOP and report — do not silently proceed with partial or unvalidated data.

Intake. Confirm three things: (a) which dataset(s) the user is landing, (b) which downstream skill will consume them, and (c) the source type (CSV/JSON export, described API pull, or manual paste). If the target dataset is unclear, read references/skill-index.md and ask which analysis the user is heading toward, then map it to a canonical target filename above.
Identify source type and land raw. Write the source verbatim into workspace/raw/<canonical-name> (use ensure_directory from common_transforms.py first). Never edit the raw file after landing it — it is the audit trail. For a described API pull, record the platform, endpoint/report, date range, and field list in the manifest's source_note.
Validate against the contract (HARD GATE). Look up the matching schema in shared/schemas/data_contracts.md and run: python3 scripts/validate_extract.py workspace/raw/<file> --contract <name> (or --required-cols col1,col2,... when no named contract applies). If required columns are missing, STOP. Report exactly which contract fields are absent and the columns that were found. Do not normalize or hand a broken file to a downstream skill.
Normalize. Load with load_csv_with_validation(path, required_columns, date_columns, numeric_columns), standardize date columns with normalize_dates(df, date_col), coerce numeric/decimal fields, and drop exact duplicate rows. Keep the same base filename and write the cleaned frame to workspace/processed/<canonical-name>.
Data-quality checks. For every landed file, record: row count; min/max of each date column and any gaps via detect_missing_windows; per-column null rate; and duplicate count on the dataset's key columns. Flag (do not silently fix) any check that crosses a concern threshold — e.g. >5% nulls in a required column, zero rows, or duplicate keys.
Write the manifest and set completion status. Save workspace/raw/extraction_manifest.json via save_json_output. Emit one of:
- DONE — all files landed, validated, normalized, and clean.
- DONE_WITH_CONCERNS — landed and normalized, but quality flags remain (state them).
- BLOCKED — a hard gate failed (missing required columns, empty file); name the file and the missing/failed contract fields.
- NEEDS_CONTEXT — cannot proceed without the source file, the target dataset, or the downstream skill (state exactly what is missing).

Output Format

Report to the user in this shape:

## Data Extraction — <dataset(s)>
Status: DONE | DONE_WITH_CONCERNS | BLOCKED | NEEDS_CONTEXT

Landed:
- workspace/raw/<file>            (<rows> rows, <date_min>..<date_max>)
- workspace/processed/<file>      (normalized: <n> dupes dropped, <k> date rows repaired)

Quality:
- <file>: nulls <col>=<pct>; duplicate keys=<n>; gaps=<windows>

Manifest: workspace/raw/extraction_manifest.json
Next: run the <downstream-skill> skill (reads workspace/raw/<file>).

extraction_manifest.json per-file keys: raw_path, processed_path, row_count, date_range, null_rates, duplicate_keys, contract, source_note, status.

Anti-Patterns

Do not normalize or forward a file that failed contract validation. A missing required column is a hard STOP, not a warning to paper over.
Do not invent, impute, or backfill values to make a file "pass." Land what exists; flag gaps in the manifest.
Do not edit workspace/raw/ files after landing them. Corrections go to workspace/processed/; raw is the immutable audit trail.
Do not rename fields off-contract. If a source column differs from the contract, map it explicitly during normalization and record the mapping in source_note.
Do not model, score, or interpret here. Landing and normalizing is the whole job; hand analysis to the downstream skill.
Do not claim a live API connection this skill cannot make. Describe the pull and land the resulting export.
Do not skip the manifest. Downstream skills and reruns depend on the recorded row counts, date ranges, and quality flags.