Data x402 offerings

Wandelt CSV in JSON oder JSON in CSV um, ohne KI (Convert CSV to JSON or JSON to CSV, deterministic).

Validate an ISIN (International Securities Identification Number, ISO 6166): 2-letter country prefix + 9-char NSIN + Luhn check digit. Returns {valid, isin, country, nsin, embeddedCusip (for US/CA), checkDigit, reason}. Catches transposed characters in security master data that a regex misses. Structure + checksum only.

Use this service before importing CSV to check data quality: missing cells, duplicate rows, required columns and declared string, integer, number, boolean or date types. Provide raw CSV with unique h…

Catch drift when everything still looks green. Check available Apify Actor run history and sampled output for schema drift and unexpected empty results. Additional checks cover HTTP responses, credential validity and available quota. One paid call returns a timestamped verdict and supporting details. A successful run or valid credential does not guarantee useful data. This endpoint checks once; it does not send alerts.

Bulk snapshot of every Pan-African government reference series in one JSON file for 34 African countries: policy rates, VAT/GST, statutory interest, minimum wages, CPI, income, corporate and withholding tax, social contributions and holidays, plus official FX reference rates. Every value cited with source URL, effective date and last_confirmed. Suited to caching the full dataset; single values are GET /v1/{country}/{series}.

Extract structured JSON from text according to your JSON Schema (LLM) (1 × 1,000 characters at 0.030000 USDC each)

Store a persistent key-value memory for an AI agent (namespace-isolated, optional TTL and metadata). Returns the stored memory record as JSON.

Detect contradictory facts, negations and numeric mismatches before an agent acts

Extract hard constraints, soft preferences, exclusions, budgets and deadlines from a human request

Deduplicate facts and near-duplicate notes to save downstream agent tokens

Deterministic JSON validation and conservative repair for AI-agent payloads. Repairs unquoted object keys, single-quoted strings, trailing commas, Markdown code fences, UTF-8 BOMs and JSON-compatible…

Executes a question-bank assessment against answer fixtures you supply — an answer plus the score it should produce — using the native OpenOLAT QTI engine rather than a reimplementation of the scoring rules. Each fixture comes back with expected and actual score, and the discrepancy count says at a glance whether the bank behaves as its author believed. This is the regression test for a question bank before it goes in front of learners, where a mis-scoring item is expensive and hard to undo.

Rebuilds the orders you supply inside a private, non-root OpenCart database and checks your export against what the native code computes. Each export row is matched to a ledger order, and every difference is named: a total that does not add up under the stated arithmetic policy, a currency conversion applied at the wrong point, a localized status that maps to nothing.

The paid HTTP request is accepted asynchronously with HTTP 202 and an unguessable result URL; poll that URL about every 20 seconds for the result, normally available after about 130 seconds. Settlement happens only after a successful native run while the authorization is still valid. The run passes a course structure and a set of learners through the pinned OpenOlat learning-path implementation and reports, per learner, which nodes the original code actually unlocks and in what order.

Relocates a Java package across a bounded set of source files using OpenRewrite, which works on a type-attributed syntax tree rather than on text. That is the difference that matters: a textual search and replace hits strings, comments and same-named symbols from other packages, while this changes only what actually refers to the package. Both the before and after state are compiled and the result is reported, so a refactoring that breaks the build says so instead of being handed over as done.

Executes a caller-directed sequence of table operations in native OpenRefine and returns the transformed table together with the exported recipe. What distinguishes it from simply transforming data is the evidence: the recipe is persisted, the project is reopened, and the whole thing is replayed from the original input, with hashes reported at each stage.

Reconciles a column of names against a reference catalog that you supply — your catalog, not a public knowledge base — using native OpenRefine reconciliation with a caller-set edit distance and an optional required type. The result separates rows with a single candidate, rows with several, and rows with none, and it leaves the ambiguous ones ambiguous: an automatic pick would be the cheapest possible way to introduce a wrong identity into a reference dataset.

Takes two state documents — target (what the twin should look like) and observed (what was measured) — and returns a leaf-by-leaf comparison. Every leaf path carries one of five states: in_tolerance,…

Takes an OpenTofu state snapshot and a passphrase, encrypts the snapshot through the pinned OpenTofu state-encryption code, then proves the result three ways: the authorized key recovers the state with an identical semantic hash, a wrong key is rejected, and a tampered payload is rejected. What comes back is the encrypted artifact plus those proofs and the resource-instance count, never the plaintext.

Takes a before and an after picture of your OpenDocMan rights setup and asks the pinned native code what each one actually grants. Every user-document pair is evaluated in both states, and what comes back is which decisions changed, which of your declared expectations were violated in each state, and whether the seeded state survived untouched.

Clusters a key column with native OpenRefine fingerprinting and returns a consolidation plan tied to actual row ids. The plan distinguishes the two cases that matter: rows that repeat an identical value and can simply be merged, and rows that collide on the key but disagree in other fields — those are flagged as unresolved rather than silently collapsed, because merging them would destroy information. Excluded rows and singletons are reported too, so the plan accounts for every input row.

Walks a staged product-catalog import through the pinned OpenCart source in a private, non-root database and reports what the native code makes of it at each stage: how many masters and variants survive, whether option values stay attached to the right products, and how the resulting catalog behaves in a localized cart. The point is to find the damage before the import touches a live shop, where a broken option mapping shows up as a customer ordering a variant that does not exist.

Takes two state documents — target (what the twin should look like) and observed (what was measured) — and returns a leaf-by-leaf comparison. Every leaf path carries one of five states: in_tolerance, out_of_tolerance, missing_in_observed, unexpected_in_observed or type_mismatch.

Replays a grading scale through the native OpenOLAT implementation and compares, for each assessment, the grade that was recorded against the grade the scale actually yields. Every discrepancy comes back with the score, the band it falls into, the expected value and the recorded one, so a wrong entry can be corrected at the row instead of being argued about in the aggregate.