ILO Labour Statistics (ILOSTAT): unemployment, wages, informality

io.github.SidneyBissoli由 GitHub 帳號 SidneyBissoli 發布v1.7.0更新於 Oct 29, 2026

Labour market data from the ILO (ILOSTAT) by country, year, sex and age, with provenance.

已驗證Streamable HTTP可網頁執行金融資料與分析

概覽

AI 產生的概覽

查詢國際勞工組織官方勞動力統計(ILOSTAT),依國家、年份、性別與年齡取得失業、就業、工資、工時與非正式就業資料,並附來源資訊。

可以做什麼
  • 以關鍵字檢索約1,200個ILOSTAT指標資料流,並把日常用語與美式說法對應到ILO術語
  • 檢視某個資料流的維度、代碼表、預設選擇與資料版本,並列出國家、性別、年齡等維度的有效代碼
  • 依維度代碼與期間篩選取得統計觀測值,並附觀測狀態與單位說明
  • 以Deep Research方式在資料流目錄中執行search與fetch,回傳編號、標題與公開頁面連結
  • 使用內建資源與現成提示詞產生國家勞動市場概況、國家比較或指標趨勢
適用情境
適合需要在助理內取得可引用、可追溯的ILO勞動市場精確數據,而非依賴模型記憶數字的情況。可用於國家概況、跨國比較,以及失業、工資、工時或非正式就業等指標的趨勢分析。
執行需求
可使用託管的Streamable HTTP端點(免帳號、免API金鑰),或在Node 20以上執行本機npm套件ilo-mcp-server(stdio方式)。需要能連線至ILOSTAT的SDMX API;未宣告任何憑證。
安裝前請注意
託管方式會讓查詢經過第三方社群營運的服務,該服務與ILO無隸屬關係,也未獲其認可。資料僅為ILO目前發布的版本,之後可能修訂,模型估計值每次發布都會重新估算。資料查詢為即時呼叫,每次最多30個國家或地區。資料以CC BY 4.0發布,須標註ILO出處。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 ILO Labour Statistics (ILOSTAT): unemployment, wages, informality,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.SidneyBissoli%2Filo-mcp-server/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "ilo-labour-statistics-ilostat-unemployme": {
      "type": "http",
      "url": "https://ilo.sidneybissoli.com/mcp"
    }
  }
}

README

ILO Labour Statistics (ILOSTAT) — MCP Server

[MCP] [CI] [Version] [Tools] [Resources] [Prompts] [npm] [MCP Registry] [ilo-mcp-server MCP server] [smithery badge] [License: MIT] [Status]

🇧🇷 Leia em Português

A public, hosted, provenance-first MCP server for the International Labour Organization (ILO) statistics — the ILOSTAT database — no installation, no account, no API key. Point your MCP client at the hosted endpoint and ask about unemployment, employment, wages, working time and other labour indicators by country, year, sex and age. It runs on Cloudflare Workers over Streamable HTTP and talks to the official ILOSTAT SDMX REST API.

Independent project. This is an unofficial, community-built client of the ILO's public ILOSTAT API — not affiliated with or endorsed by the International Labour Organization. Data remain © ILO under CC BY 4.0; see Data license and attribution.

Every response carries a provenance block (source URL, data vintage, real retrieval timestamp, license, ILO citation) — exact figures with an audit trail, not numbers guessed from training data.

🇧🇷 Em português. Servidor MCP remoto e hospedado (nada para instalar, sem conta e sem chave) para as estatísticas de mercado de trabalho da OIT — desemprego, emprego, salários, jornada e informalidade por país, ano, sexo e idade, direto no Claude, no ChatGPT ou em qualquer cliente MCP, com proveniência e citação da fonte em cada resposta: README em português.

Questions it answers

In plain language, inside the MCP client — the assistant picks the tool and the filters:

  • "What has happened to unemployment in Brazil since 2015?" (ilo_get_data)
  • "Compare youth unemployment in Brazil, Mexico and South Africa." (ilo_compare_countries)
  • "How large is the gender pay gap, and where does the ILO publish it?" (ilo_search_indicators → ilo_get_data)
  • "What share of employment in India is informal?" (ilo_search_indicators → ilo_get_data)
  • "Which ILOSTAT dataflow has average monthly earnings by sex and economic activity?" (ilo_search_indicators)
  • "Which country, age and sex codes can I filter this indicator by?" (ilo_list_dimension_values)
  • "Give me a labour-market profile of Viet Nam." (ilo_country_labour_profile)

Ask in your words, not the ILO's. ILOSTAT is worded in British statistical English, and the catalogue is matched on the dataflow name — so the everyday or US word used to return nothing at all. Measured over the 1,212 dataflows of the official catalogue (2026-09-13), and fixed since 0.6.0: the search translates the term and tells you it did.

you askhits beforeILOSTAT writeshits
labor, labor force0labour, labour force176, 122
wages, salary0earnings107
informality0informal133
gender2sex1,131
productivity0output per worker4
jobless0unemployment108

Comparison with the alternatives

Anyone who already works with ILOSTAT has good tools, and this server replaces none of them — it sits somewhere else in the chain: it answers the question at the point where the question is asked, inside the assistant, with source, vintage and licence attached to the answer. Detail, side-by-side examples and the measured numbers in docs/alternatives.md.

ToolWhat it isWhen to prefer it
ilo-mcp-server (this)Remote MCP server, hosted, nothing to install: 6 tools over the ~1,200 ILOSTAT dataflows, with a provenance block per answerThe question is asked in an assistant (Claude, ChatGPT, Cursor, Claude Code) and the answer has to be auditable
Rilostat 2.5.0 (R, CRAN)The ILO's own R package, written by ILO staff: bulk download, metadata, filtering and reshapingYou are in R and want the dataset in a data frame — a whole table, repeatedly, for analysis
sdmx1 2.27.0 / pandaSDMX 1.10.0 (Python)Generic SDMX clients; ILO is one of ~36 sources they knowYour pipeline is Python and you want SDMX objects, or the same code across several SDMX agencies
DBnomics (API, dbnomics for Python, rdbnomics 0.6.4 for R)Aggregator that republishes 1,071 ILO datasets next to other providers, one API for allYou want ILO series alongside IMF, OECD, Eurostat in a single interface
ILOSTAT SDMX REST APIThe source itself, which this server callsYou are building your own client and want full control
cyanheads/ilostat-mcp-server 0.1.2Another ILOSTAT MCP server, hosted or local: tells survey values from ILO modelled estimates row by row, takes up to 300 areas per call and stages large results as dataframes you query with SQLYou need reported vs modelled on every row, or many countries and SQL over a large pull
Pipeworx mcp-ilostat 0.1.4The ILOSTAT pack of the Pipeworx gateway: positional SDMX keys, answers without source, licence or value statusYou already work through the Pipeworx gateway and want ILOSTAT next to its other sources

Do not use this server when you need a whole dataset rather than an answer (Rilostat's bulk download is the right tool), when the question is not labour statistics published by the ILO (education → UNESCO UIS, national accounts → IMF/World Bank), or when you need microdata: ILOSTAT publishes aggregates, and so does this server.

Sister servers, same design and same provenance block, for other official sources: IBGE (Brazilian statistics), BCB (Central Bank of Brazil), Senado (Brazilian Senate open data), SIH/SUS (Brazilian hospital admissions) and medical terminologies (ICD-11, ICD-10, LOINC, RxNorm, ATC, MeSH).

Use it (hosted — no setup)

Point any MCP client at the Streamable HTTP endpoint:

https://ilo.sidneybissoli.com/mcp

Claude Desktop / Claude Code and other clients with native remote support:

json
{  "mcpServers": {    "ilostat": {      "url": "https://ilo.sidneybissoli.com/mcp"    }  }}

For clients that launch MCP servers as a command, use the mcp-remote bridge:

json
{  "mcpServers": {    "ilostat": {      "command": "npx",      "args": ["-y", "mcp-remote", "https://ilo.sidneybissoli.com/mcp"]    }  }}

The ilo-mcp-server.sidneybissoli.workers.dev hostname is also served, as a secondary.

ChatGPT (Deep Research)

ChatGPT deep research (and company knowledge, and research workflows over the Responses API) only uses an MCP server that exposes exactly search and fetch — this server does, on top of the ilo_* tools. Point the connector at the hosted endpoint, no key required:

https://ilo.sidneybissoli.com/mcp

search ranks the query against the full ILOSTAT dataflow catalogue (~1,200 SDMX dataflows — employment, unemployment, wages, working time, informality, SDG labour indicators) and returns { id, title, url } (ind:<DATAFLOW_ID>, e.g. ind:DF_UNE_2EAP_SEX_AGE_RT); fetch returns the dataflow as readable Markdown — name, data vintage, dimensions and codelists, the ILO's default selection and how to query it with ilo_get_data — with the public ILOSTAT data explorer page as url, which is what ChatGPT cites. Both carry the same provenance block as every other tool, in structuredContent and _meta (the text channel is the contract's JSON). In ChatGPT's developer mode (Settings → Security and login → Developer mode) any tool is callable — the ilo_* tools remain the ones to use for data.

Run locally (stdio)

Prefer not to route queries through a third-party host? The same server also runs as a local stdio process that talks directly to the official ILOSTAT API — same 6 tools, resources and prompts, same limits, same provenance block, no Cloudflare in the loop.

No install needed — the package is on npm (ilo-mcp-server, Node ≥ 20):

json
{  "mcpServers": {    "ilostat": {      "command": "npx",      "args": ["-y", "ilo-mcp-server"]    }  }}

Or from source:

bash
git clone https://github.com/SidneyBissoli/ilo-mcp-servercd ilo-mcp-servernpm installnpm run buildnode dist/cli.js   # serves MCP over stdio (Ctrl+C to stop)

(then point the client at node /path/to/ilo-mcp-server/dist/cli.js).

Differences from the hosted server, all due to the absence of Cloudflare bindings: the SDMX cache lives in process memory (structures and codelists are reused within a session, not across sessions); the search catalogue is downloaded from the official endpoint on the first search (its real retrieved_at is reported in provenance); no usage metrics, rate limit or auth. Logs go to stderr — stdout carries only the JSON-RPC stream. The repository Dockerfile builds this runtime (used by the Glama registry).

Tools

ToolWhat it doesSource
ilo_search_indicatorskeyword search over ~1,210 dataflows (paginated by offset)local catalogue (no upstream call)
ilo_get_indicator_metadatadimensions, codelists, vintage and default selection of a dataflowcached structure (miss → upstream)
ilo_list_dimension_valuesvalid codes of one dimension (paginated by offset)cached codelist (miss → upstream)
ilo_get_dataobservations filtered by dimension and period1 live REST call per query
searchChatGPT Deep Research contract: ranks a query against the full dataflow catalogue, returns { id, title, url } (ind:<DATAFLOW_ID>)in-memory index built from the local catalogue (24 h)
fetchChatGPT Deep Research contract: one dataflow as readable Markdown with the public data explorer page as urlcached structure (miss → upstream)

Typical flow: ilo_search_indicators → ilo_get_indicator_metadata / ilo_list_dimension_values to discover valid filter codes → ilo_get_data with country and period filters.

Every response carries the portfolio provenance block (@sbissoli/mcp-provenance, modes concise/detailed via the provenance_mode parameter) on three channels: structuredContent, namespaced _meta (com.sidneybissoli.ilostat/*) and a text footer. Since 1.1.0 the block includes retrieval, the origin diagnostic of the call — how many requests went to ILOSTAT, how many attempts they took, which anomalies were overcome and whether the answer is unstable — measured by the portfolio's common fetch (@sbissoli/mcp-upstream); null when the answer did not touch ILOSTAT (catalogue or cached structure).

Resources and prompts

Three resources (static, text/markdown, no upstream call) that a client can attach to the context before calling tools — they save the 2–3 discovery calls most sessions spend on "which dataflow, which codes":

URIContent
ilostat://guidetool workflow, stable code conventions (REF_AREA ISO3 + X-aggregates, SEX, AGE, FREQ, dataflow id suffixes), limits, reporting rules
ilostat://reference/key-dataflowsverified dataflow ids by topic (unemployment, employment, participation, wages, hours, informality, NEET, SDG 8, productivity)
ilostat://reference/provenancemeaning of every provenance field and how to cite the ILO

Three prompts — ready-made workflows that chain the tools and end with the citation rules (arguments are strings; period arguments optional):

PromptArgumentsResult
ilo_country_labour_profilecountry, start_period, end_periodlabour-market profile of one country (unemployment, participation, employment ratio, informality, NEET, earnings, hours)
ilo_compare_countriescountries, indicator, start_period, end_periodcomparison table across countries/aggregates in one data call, flagging modelled estimates vs reported data
ilo_indicator_trendindicator, country, start_period, end_periodtime series of one indicator with first/last, peak/trough and OBS_STATUS breaks

Every dataflow id quoted in the resources and prompts is checked against the catalogue seed by the test suite, so the documentation cannot point at an id the search would not find.

Behaviour and limits

  • REF_AREA is required in ilo_get_data, up to 30 areas per call. The ILO gateway times out (HTTP 504) on unrestricted queries, so the server never issues one; for broad panels, split the areas into batches and/or paginate by period (start_period/end_period). The error message explains how.
  • One live REST call per data query. Data is never cached — every ilo_get_data result is fetched from ILOSTAT at request time. Dataflow structures (TTL 24 h) and codelists (TTL 7 days, shared across dataflows) are cached.
  • data_vintage is the dataflow's last-update date as published by the ILO (LAST_UPDATE annotation, normalised to ISO).
  • retrieved_at is always the real instant of extraction from ILOSTAT, preserved alongside any cached value — never the build or response time. Cached responses say so (served_from_cache: true).
  • The indicator catalogue is a local snapshot (~1,210 dataflows), refreshed periodically; its own retrieved_at is reported in the provenance of ilo_search_indicators, so its age is always visible.
  • Every upstream call carries an identifiable User-Agent (service URL + contact), so ILO administrators can reach the operator.
  • Every upstream call has a timeout and a retry policy (since 1.1.0, measured against the live API): 65 s per attempt — above the ~61 s the ILO gateway itself takes to reject an unrestricted query with 504, so a legitimate wide query is never cut short by this server —, up to 3 attempts on 5xx, 429 (honouring Retry-After) and network errors, 70 s in total per call. Timeouts, HTTP 504 (query too broad) and 404 (no observations) are never retried. What happened is reported in the provenance retrieval field.
  • Language: English; timezone: UTC (ILO data is published in English).

Provenance fields

  • derived — true only for real transformation (aggregation, server-computed rate, interpolation, harmonisation), always with a derivation_note; unit conversion and rounding do not count. This server does not transform values, so derived is always false (and absent from the concise block).
  • notices (ilo_get_data) — each distinct value of OBS_STATUS (the SDMX status/disclaimer channel, e.g. "Break in series"), UNIT_MEASURE and UNIT_MULT (e.g. "Thousands": the unit and scale are part of the number) found in the rows, with the number of observations carrying it. Repeated in the text footer; absent when there is nothing to say. Technical per-observation attributes (DECIMALS etc.) stay on the rows (rows[].attributes).
  • revision (since contract 1.3, server 1.6.4) — always current: ILOSTAT serves only its current release and may revise it later; on data answers the note says the ILO re-estimates modelled series (_2 ids) at each release, past years included.

Surface fingerprint, checkable from the registry

Every release publishes, in its MCP Registry entry, the sha256 of the surface this version serves (initialize, tools, resources, prompts) and which methods answer without a credential. A surface change without a version bump fails the build; the fingerprint in the registry lets a client check the same thing on its own side. Canonical form and procedure: SPEC.md of @sbissoli/mcp-surface. To check it yourself (Node 18+, no dependencies):

sh
curl -sO https://raw.githubusercontent.com/SidneyBissoli/mcp-br-commons/main/packages/mcp-surface/exemplos/verify.mjsnode verify.mjs io.github.SidneyBissoli/ilo-mcp-server

Data license and attribution

  • ILOSTAT data and metadata: CC BY 4.0 (since 2023-05-03; license verified 2026-08-04).
  • ILO attribution in every response (citation field): International Labour Organization, ILOSTAT, https://ilostat.ilo.org/data/, accessed <date>.
  • The ILO logo is not used. This service is not endorsed by the ILO.

Self-hosting / development

Everything below is only needed to run your own instance — it is not required to use the public server.

bash
npm installnpm run typecheck && npm test   # offline suite (parsers, key, tools, output contract, resources/prompts, in-memory catalogue, vocabulary, eval fixtures)npm run dev                     # http://localhost:8787/mcp (Worker)npm run build && npm start      # stdio runtime (dist/cli.js)
# Catalogue seed (D1) — required before first use:node scripts/seed-catalog.mjs   # downloads via curl and generates scripts/seed-catalog.sqlnpx wrangler d1 execute ilostat-catalog --local  --file=scripts/seed-catalog.sqlnpx wrangler d1 execute ilostat-catalog --remote --file=scripts/seed-catalog.sql
npm run deploynode scripts/smoke-mcp.mjs      # smoke test against production (initialize → 6 tools → search → fetch → errors)npm run manifest:lhm            # regenerate tools/resources/prompts in lhm.plugin.json from the real server# (the seed also writes tests/fixtures/catalog-ids.txt — the versioned id list the tests check resources/prompts against)

Bindings (see wrangler.jsonc): KV SDMX_CACHE, D1 CATALOG_DB, Durable Object USAGE (SQLite-backed usage counters), CF_VERSION_METADATA. Optional Bearer auth (wrangler secret put API_KEY); token-bucket rate limit per IP.

Notes for operators:

  • ILOSTAT returns JSON only when negotiated via the Accept header (application/vnd.sdmx.{structure,data}+json); ?format= is ignored and returns XML.
  • The ILO gateway answers HTTP 500 (languageTag1) to the Accept-Language: * header that Node's fetch (undici) sends by default; every upstream call therefore sets Accept-Language: en explicitly (Cloudflare's runtime sends no such header, so the Worker was never affected). It also expects an identifiable User-Agent.
  • Catalogue refresh is manual (no cron): quarterly, or immediately if a dataflow that exists upstream does not show up in search. Procedure: the three seed commands above. Data queries are always live, so only the search catalogue can age — and its age is exposed in provenance.

Evals

@sbissoli/mcp-evals: 24 fixtures in evals/fixtures/queries.ts, validated offline in npm test. The run with a real model (npm run eval) uses the Anthropic API and needs ANTHROPIC_API_KEY (without it, it exits with instructions). Run of 2026-08-07: top-1 100% (24/24) — evals/results/.

End-to-end: 10 complex questions with a single verifiable answer in evals/e2e/evaluation.xml, answers validated manually against production (evals/e2e/validacao-respostas.md). Run of 2026-08-07 (Sonnet): 9/10 exact string; 10/10 substantive — evals/results/2026-08-07-e2e.md.

Endpoints

RoutePurpose
/landing page (service identity + contact — public)
/healthliveness
/statusversion, tool/resource/prompt counts and names, provenance contract version, current deploy (feeds the README badges)
/metricsaggregated usage (MCP endpoint only; no IPs, no query content)
/.well-known/mcp/server-card.jsonMCP server card for directory scanners (serverInfo, tools, resources, prompts — derived from the running server)
/mcpMCP Streamable HTTP

Security

Snyk Agent Scan (2026-08-07): passed — report in security/.

License

Code: MIT. Data: ILOSTAT, CC BY 4.0 (see "Data license and attribution" above).

Privacy

Privacy policy of the hosted service: PRIVACY.md.

Contact

Sidney da Silva Pereira Bissoli — [email protected]. This service is not endorsed by the ILO.

來源:README.md,提交 076febe

工具

6

ilo_search_indicators

Search ILOSTAT indicators

Search the ILOSTAT catalogue of ~1,200 indicator dataflows by keywords in the name or id (e.g. "unemployment rate sex age"). All terms must match (AND, case-insensitive), so start with 2–3 English words and drop terms if you get 0 results. Everyday and US wording is resolved to the ILO's own (labor→labour, wages/salary→earnings, informality→informal, gender→sex, productivity→output per worker); when that happens the response says so in vocabulary_notes. Results are ranked by ILO relevance weight, not by match count. Reading the id tells you the shape: suffix _RT = rate/ratio, _NB = number (usually thousands); dataflows whose second token starts with 2 (e.g. DF_UNE_2EAP_…) are ILO modelled estimates with full country/year coverage, the others are reported national data. Returns dataflow ids to use with ilo_get_data / ilo_get_indicator_metadata. Searches the local catalogue only — it does not return statistical values (use ilo_get_data), does not search dimension codes such as countries (use ilo_list_dimension_values) and does not cover non-ILO sources.

輸入參數limitquery必填offsetprovenance_mode

ilo_get_indicator_metadata

Get ILOSTAT indicator metadata

Structure of one ILOSTAT dataflow: dimensions (in SDMX key order), their codelists, the time dimension, the source's default selection and the data vintage (last update at the ILO). Use before ilo_get_data to know which filters exist. Does not return statistical values and does not list the codes themselves (use ilo_list_dimension_values).

輸入參數dataflow必填provenance_mode

ilo_list_dimension_values

List valid codes of a dimension

Valid codes (id + label) of one dimension of an ILOSTAT dataflow — e.g. the country/area codes of REF_AREA (ISO 3166-1 alpha-3 such as BRA, plus X-codes for aggregates such as X01 World) or the categories of SEX (SEX_T/SEX_M/SEX_F) and AGE. Use `search` to resolve a name to a code (e.g. search "Brazil") instead of paging through hundreds of codes; codelists are shared across dataflows, so a code found here is valid wherever the same codelist is used. Use to build correct ilo_get_data filters. Does not return statistical values, does not say which codes actually have data for a given dataflow, and is not applicable to the time dimension (filter it via start_period/end_period in ilo_get_data).

輸入參數limitoffsetsearchdataflow必填dimension必填provenance_mode

ilo_get_data

Get ILOSTAT data

Statistical observations from one ILOSTAT dataflow, filtered by dimension codes (filters, e.g. {"REF_AREA": ["BRA","ARG"], "SEX": "SEX_T"}) and period (start_period/end_period, e.g. "2015"/"2024"). REF_AREA is required, maximum 30 areas per call — for broad panels, split areas into batches and/or paginate by period. Unfiltered dimensions return all their categories. Does not aggregate, convert or otherwise transform values (raw ILOSTAT data only): read each value with the unit and multiplier the ILO states in its row attributes (UNIT_MEASURE, UNIT_MULT — e.g. UNIT_MULT 3 = thousands). Read TIME_PERIOD with the FREQ column (annual, quarterly or monthly) and OBS_STATUS when present (break in series, estimated or provisional value). Values are the current release, dated by data_vintage (the dataflow LAST_UPDATE): the ILO revises published figures and re-estimates modelled series (_2 ids) at each release, past years included. Does not search indicators (use ilo_search_indicators).

輸入參數filters必填dataflow必填end_periodstart_periodprovenance_modelast_n_observations

fetch

Deep Research Document

Returns the full document for an id obtained from `search`, as { id, title, text, url, metadata }: `text` is the readable content (Markdown) and `url` the canonical public page to cite. Companion of `search` in the OpenAI Deep Research contract, over the ILOSTAT labour statistics (≈1,200 SDMX dataflows: employment, unemployment, wages, working time, informality, SDG labour indicators) catalog. Only ids returned by `search` are valid; an unknown id returns an error. The `ilo_*` tools remain the tools for data queries. Behavior: read-only and idempotent — a live GET against the public source when the document needs it.

輸入參數id必填

版本歷史

14
  1. v1.7.0最新Oct 29, 2026
  2. v1.6.4Oct 16, 2026
  3. v1.6.3Oct 15, 2026
  4. v1.6.1Oct 9, 2026
  5. v1.5.0Oct 8, 2026
  6. v1.4.2Oct 8, 2026
  7. v1.4.1Oct 7, 2026
  8. v1.4.0Oct 6, 2026
  9. v1.3.0Oct 4, 2026
  10. v1.2.1Oct 2, 2026
  11. v1.1.0Sep 27, 2026
  12. v1.0.0Sep 25, 2026
  13. v0.6.1Sep 17, 2026
  14. v0.6.0Sep 16, 2026

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