All Services

Converts an HTTP request (with optional auth and headers) into a formatted cURL command string

Fetches a paginated JSON feed of news articles from StreamSage.news

Parses a webpage or HTML snippet to find duplicate attribute values across strong-emphasis elements, filtered by a search term and target attribute.

Parses a public webpage or supplied HTML and returns a text-length ranking for <strong> elements, optionally filtered by a search term or attribute, compared against a list of previous texts.

Extracts and computes text length statistics for <strong> (strong-emphasis) elements on a given webpage or HTML snippet via static parsing.

Analyzes structured observations data by computing category frequency counts and equal-width histogram distributions for valid_items or total_items fields, returning two analytical reports for up to 200 rows.

Analyzes up to 200 rows of structured observations to produce IQR-based outlier flags and a secondary-measure weighted mean for valid_items or total_items fields.

Compares two keyed datasets of structured observations (valid_items, total_items) and fills missing values, producing two analytical reports for up to 200 rows.

Computes numeric distribution statistics and fills missing values (zero, mean, or median) for up to 200 rows of structured observations with valid_items and total_items fields, delivering two analytical reports per call.

Analyzes up to 200 rows of structured observation records (valid_items, total_items) producing two local analytical reports: a numeric distribution summary and a keyed dataset comparison between two supplied datasets.

Computes numeric distribution statistics and secondary-measure weighted mean for up to 200 rows of structured observations with valid_items and total_items fields, returning two analytical reports.

Filters structured observation rows by a numeric interval on a chosen field and detects adjacent row changes, returning two analytical reports for up to 200 rows.

Applies a numeric interval filter and generates a base-100 index series on up to 200 rows of structured observation data with valid_items and total_items fields.

Applies a numeric interval filter and generates a daily sum time series for structured observation records with valid_items and total_items fields, returning two analytical reports for up to 200 rows.

Computes numeric row ordering and daily sum series for up to 200 rows of structured observation data with valid_items and total_items fields, returning two independent analytical reports.

Ranks rows numerically by a chosen field and compares two structured datasets keyed by row identifier, producing two analytical reports from supplied observation records.

Sorts structured observation rows by a numeric field and filters them to a specified numeric interval, producing two analytical reports on up to 200 supplied rows.

Analyzes up to 200 rows of structured observation records by applying numeric row ordering and computing two-measure correlation between valid_items and total_items fields.

Audits record quality and compares two keyed datasets of structured observations (valid_items, total_items) across up to 200 rows, returning two independent analytical reports.

Runs three analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 rows of structured observations data containing valid_items and total_items fields.

Runs three local analytical reports on structured observations data: record quality audit, string normalisation, and daily sum series — up to 200 rows, for valid_items or total_items.

Audits record quality, normalises strings, and orders rows numerically for up to 200 rows of structured observations data containing valid_items and total_items fields, producing 3 independent analytical reports.

Performs a record quality audit and two-measure correlation analysis on up to 200 rows of structured observations containing valid_items and total_items fields.

Normalises string fields and orders rows numerically for structured data observation records with valid_items and total_items fields, returning 2 analytical reports for up to 200 rows.

Computes Pearson/rank correlation between valid_items and total_items, then compares two keyed row datasets to identify differences — all offline, up to 200 rows, two reports per call.

Validates that declared note byte sizes fall within per-note and per-notebook byte ceilings, without accessing note content or tokenizing text.

Counts joint absences of source, locator, and confidence-basis metadata fields across study notes without imputation or quality scoring

Compares two pinned study-note projections to identify changes in source, locator, claim-kind, and confidence-basis fields across revisions.

Compares closed note-extraction field contracts between before and after schema states, identifying removals, type changes, and newly required fields for request narrowing validation.

Computes adjacent-row changes and fills missing values for study recruitment data (eligible_candidates, enrolled_candidates) across up to 200 rows, returning two independent analytical reports.

Runs two analytical reports on study recruitment data: adjacent row change detection and keyed dataset comparison, for up to 200 rows of eligible/enrolled candidate records.

Computes daily sum time series and base-100 indexed series for study recruitment data (eligible and enrolled candidates) across up to 200 rows, returning two analytical reports.

Computes daily sum time series and performs keyed dataset comparison for study recruitment data (eligible and enrolled candidates) across up to 200 rows.

Computes a daily sum time series and a Pearson/Spearman correlation between eligible_candidates and enrolled_candidates from supplied study recruitment row data.

Analyzes study recruitment records to detect duplicate row keys and compute adjacent row changes for eligible_candidates or enrolled_candidates fields.

Generates a duplicate key report and a base-100 index series for up to 200 rows of study recruitment data (eligible and enrolled candidates), returning two independent analytical reports.

Analyzes up to 200 study recruitment rows to produce a duplicate key report and a date-by-category pivot table for eligible and enrolled candidate counts.

Analyzes up to 200 study recruitment records to produce a duplicate key report and a numeric row ordering report for eligible and enrolled candidate fields.

Analyzes supplied study recruitment data (eligible_candidates, enrolled_candidates) to produce a duplicate key report and a trailing moving-average series for up to 200 rows.

Deduplicates exact duplicate rows and computes correlation between eligible_candidates and enrolled_candidates for up to 200 study recruitment records.

Computes grouped numeric summaries and numeric row ordering for study recruitment data (eligible and enrolled candidates) across up to 200 rows, returning two independent analytical reports.

Computes numeric distribution statistics and a base-100 index series for study recruitment data (eligible and enrolled candidates) across up to 200 rows, producing two independent analytical reports.

Analyzes study recruitment data (eligible and enrolled candidates) by computing numeric row ordering and adjacent row changes across up to 200 supplied rows.

Sorts study recruitment records by numeric value and computes the Pearson/rank correlation between eligible_candidates and enrolled_candidates for up to 200 rows.

Runs two local analytical reports on study recruitment data: a record quality audit and an adjacent row change detection, for up to 200 rows of eligible/enrolled candidate records.

Runs two independent analytical reports — a record quality audit and a Base-100 index series — on up to 200 rows of study recruitment data (eligible_candidates and enrolled_candidates fields).

Runs two analytical reports on supplied study recruitment data: a record quality audit and numeric row ordering for eligible_candidates or enrolled_candidates fields, up to 200 rows.

Runs two analytical reports on study recruitment data: a record quality audit and a secondary-measure weighted mean, for up to 200 rows of eligible/enrolled candidate data.

Runs three local analytical reports on study recruitment records: a record quality audit, string normalisation, and numeric row ordering — all on supplied data, no external calls.

Computes a secondary-measure weighted mean and fills missing values explicitly for up to 200 study recruitment records covering eligible and enrolled candidates.

Runs two analytical reports on study recruitment data: string normalisation and adjacent row changes for eligible_candidates or enrolled_candidates across up to 200 rows.

Runs two local analytical reports on study recruitment data: string normalisation of course group labels and an equal-width histogram over a chosen numeric field (eligible or enrolled candidates).

Normalises strings and compares two keyed study recruitment datasets (eligible vs enrolled candidates) across up to 200 rows, producing two independent analytical reports.

Normalises string fields and filters study recruitment rows by a numeric interval (min/max) on eligible or enrolled candidates, returning two analytical reports for up to 200 rows.

Normalises string fields and orders rows numerically for study recruitment records (eligible_candidates, enrolled_candidates), producing 2 analytical reports for up to 200 rows.

Computes a trailing moving average series and a Pearson/two-measure correlation report for study recruitment data (eligible_candidates and enrolled_candidates) from up to 200 supplied rows.

Computes a two-measure correlation and a date-by-category pivot table for supplied study recruitment data (eligible_candidates, enrolled_candidates) across up to 200 rows.

Computes adjacent row changes and a base-100 index series for style guide audit metrics (checked_items or flagged_items) across up to 200 rows, producing 2 analytical reports.

Runs two analytical reports on style guide audit data: detects adjacent row changes and performs keyed dataset comparison across checked_items and flagged_items fields, for up to 200 rows.

Analyzes style guide audit records to produce category frequency distributions and numerically ordered row rankings for checked_items and flagged_items fields.

Runs two local analytical reports on style guide audit data: a duplicate key report and an adjacent row changes analysis, for up to 200 rows of checked_items or flagged_items.

Analyzes style guide audit records to produce a duplicate key report and category frequency breakdown for checked_items and flagged_items across up to 200 rows.

Runs two analytical reports on style guide audit data: a duplicate key report and a trailing moving average series for checked_items or flagged_items over up to 200 rows.

Generates equal-width histogram and missing-value-filled distribution reports for style guide audit data (checked_items, flagged_items) across up to 200 rows.

Computes an equal-width histogram and a secondary-measure weighted mean for style guide audit records (checked_items, flagged_items) over up to 200 rows.

Deduplicates exact duplicate rows and fills missing values in style guide audit records (checked_items, flagged_items) for up to 200 rows, producing 2 independent analytical reports.

Deduplicates exact duplicate rows and flags statistical outliers using IQR analysis for style guide audit records (checked_items, flagged_items), producing two independent analytical reports for up to 200 rows.

Runs two local analytical reports on style guide audit data: exact row deduplication and numeric distribution for checked_items or flagged_items fields across up to 200 rows.

Deduplicates style guide audit rows and filters records by a numeric interval on checked_items or flagged_items, returning 2 analytical reports for up to 200 rows.

Deduplicates rows and applies numeric ordering to style guide audit records (checked_items, flagged_items) across up to 200 rows, producing 2 independent analytical reports.

Runs two local analytical reports on style guide audit data: a grouped numeric summary and adjacent row change analysis for checked_items or flagged_items across document groups.

Analyzes style guide audit records (checked_items, flagged_items) by document group, producing a grouped numeric summary and a trailing moving-average series for up to 200 rows.

Runs two local statistical reports on style guide audit records: IQR-based outlier detection and a secondary-measure weighted mean, across up to 200 rows of checked/flagged item counts.

Sorts style guide audit rows numerically and fills missing checked_items or flagged_items values using zero, mean, or median imputation, returning two reports for up to 200 rows.

Sorts style guide audit rows numerically and computes a trailing moving average series for checked_items or flagged_items across up to 200 rows.

Runs two analytical reports on style guide audit rows: a record quality audit and adjacent row change detection for checked_items or flagged_items fields.

Runs a record quality audit and a daily sum time series report on up to 200 style guide audit rows (checked_items, flagged_items), returning two independent analytical reports.

Runs three local analytical reports on style guide audit records: a record quality audit, exact row deduplication, and a numeric interval filter on checked_items or flagged_items, for up to 200 rows.

Runs a record quality audit and a secondary-measure weighted mean on up to 200 style guide audit rows (checked_items, flagged_items), returning two independent analytical reports.

Computes a secondary-measure weighted mean and fills missing values in style guide audit records (checked_items, flagged_items) for up to 200 rows, returning 2 independent analytical reports.

Runs two local analytical reports on style guide audit data: string normalisation and daily sum time series for checked_items or flagged_items across up to 200 rows.

Normalises strings and fills missing values in style guide audit records (checked_items, flagged_items) for up to 200 rows, producing 2 analytical reports.

Runs two local analytical reports on style guide audit records: string normalisation on text fields and numeric distribution analysis on checked_items or flagged_items, for up to 200 rows.

Computes a trailing moving-average time series and a secondary-measure weighted mean over supplied style guide audit records (checked_items, flagged_items) for up to 200 rows, returning two independent analytical reports.

Normalises string fields in subscription activity records (plan names, dates) across up to 200 typed rows for local data-quality processing.

Computes adjacent row-to-row changes in invoiced or credited amounts across up to 200 subscription billing credit records.

Computes category-frequency counts across subscription billing credit records, broken down by provider, date, and amounts.

Computes a local numeric distribution report for subscription billing credit or invoiced amount records (up to 200 rows), returning descriptive analytics with no enrichment or forecasting.

Audits up to 200 subscription billing credit records for data quality issues, returning local descriptive analytics on the supplied dataset.

Normalises string fields (provider names, dates, etc.) across up to 200 subscription billing credit records and returns a local descriptive analytics report.

Searches live public web pages about subscription billing platforms with a focus on analyst-oriented content, returning source links and snippets.

Searches live public web pages for information about subscription billing platforms targeted at operations teams, returning source links and text snippets.

Searches live public web pages for information about subscription billing platforms targeted at technology buyers, returning source links and snippets.

Validates a subscription CSV file and reports all blank or empty rows within it

Validates cell length constraints in a subscription CSV import file, reporting any cells that exceed configured length limits across up to 1000 rows.

Validates a declared CSV contract for subscription import data, checking columns, values, and structure against configured rules for up to 1000 rows.

Validates a subscription import CSV for spreadsheet formula injection flags, checking up to 1000 rows for formula-like cell content.

Validates and audits a subscription CSV table for whitespace issues, supporting up to 1000 rows with quoted cells and configurable delimiter.

Generates a fixed-holdout learning curve for subscription request text classification (upgrade/downgrade/cancel/renew) using a naive-Bayes baseline on up to 200 labelled records.

Splits up to 200 labelled subscription-intent text records into grouped train/test partitions with naive-Bayes baseline evaluation