# Extradition Case Law Search Matei Clej > Searches the domestic courts of 122 jurisdictions — 153 databases, 49 automated — for extradition and arrest-warrant surrender decisions, each in its own language and vocabulary. Answers the question no commercial database does: how are OTHER executing states treating this requesting state, or this ground? Searching by requesting state localises its name per jurisdiction (AE becomes 'Emiratele Arabe Unite' in Romania, 'Emirati Arabi Uniti' in Italy), so the sweep finds what those courts actually wrote. Every hit passes a relevance gate; every search leaves a dated report, so the research is reconstructable months later. Use when asked what foreign courts have said about surrender to a given state, whether any European court has refused extradition on prison conditions, assurances or Article 3, for comparative extradition research, or to build a foreign-authority section of an extradition argument. Not for domestic case law of the user's own jurisdiction, and never a substitute for reading the judgment. - Skill: `gabrielmoreira/extradition-case-law-search-matei-clej` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add gabrielmoreira/extradition-case-law-search-matei-clej` - Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/extradition-case-law-search-matei-clej/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/gabrielmoreira/extradition-case-law-search-matei-clej --- # Foreign extradition case-law search 1. Establish what is actually being asked: which **requesting state**, which **executing states**, which **ground**. 2. Name the executing states. Never sweep everything by default — see *Scope it*. 3. Run the search (`scripts/xsearch.py`), or work the coverage map by hand where no shell is available. 4. Report hits as **leads with links**, grouped by jurisdiction, never as holdings. 5. Tell the user what did *not* answer, and what a nil return does and does not mean. --- ## What this is for A recurring problem in extradition defence and prosecution is showing how *other* executing states treat a given requesting state or a given ground: Rechtbank Amsterdam on prison conditions, German OLG and Bundesverfassungsgericht decisions on assurances, Irish High Court surrender judgments, Italian Cassazione, Polish SAOS. That material sits in a hundred-plus national databases, each with its own interface, language and vocabulary, and no commercial service searches them together. This skill drives an open-source engine — 153 adapters, MIT licensed — that does. **It is not for** the domestic case law of the user's own jurisdiction (use a national database or a commercial service), and it does not read, translate, or interpret the judgments it finds. --- ## The rule that governs every output **Every result is a lead, never an authority.** The engine reports that a judgment exists and that its metadata matched extradition vocabulary. It does not know what the judgment holds. Before anything from here reaches advice, a pleading or a court: 1. **Open the decision and read the passage.** In the original language, with a translation if it will be relied on. A machine-matched title is not a ratio. 2. **Treat the relevance gate as sorting, not judgment.** `on_topic` means extradition vocabulary appeared in the title or snippet. It is not a finding that the case is about the user's point. 3. **Keep "failed" and "nil" apart.** A database that errored was unreachable that run: its material is *unsearched*, not absent. The report separates them; so must the answer. 4. **Read a genuine nil return carefully.** Publication practice varies enormously — in several states the surrender decision is never published at all. "Nothing found" is not "no such decisions", and must never be reported as though it were. 5. **Say what was not reached.** Flag anything unverified rather than presenting it with more confidence than it has earned. The user remains professionally responsible for every authority they cite; this skill never discharges that. --- ## Running it One-off install, then search: ```bash python3 scripts/xsearch.py install # How are executing states treating Romania? python3 scripts/xsearch.py search --issuing-state RO --countries NL,DE,IE # A ground, in a state's own language, in its own courts python3 scripts/xsearch.py search "detentie omstandigheden" --countries NL # Non-arrest-warrant vocabulary — third-state extradition python3 scripts/xsearch.py search --issuing-state TR --mode extradition # Narrowed by date python3 scripts/xsearch.py search --issuing-state PL --countries NL,DE --since 2023-01-01 ``` Supporting commands: `preview AE --countries RO,IT,PL` (what a requesting state is actually searched as), `sources --countries NL,DE` (which databases exist and their status), `runs` (past searches), `show ` (a past report in full). Options: `--mode both|eaw|extradition`, `--since` / `--until`, `--limit N` per database, `--top N` printed, `--include-manual`, `--no-expand`. **Without a shell** — in an assistant that cannot run Python — do not pretend to sweep. Load `reference/jurisdictions.md`, identify the databases that cover the executing states in question, and work them one at a time with whatever browsing is available, using the localised vocabulary from `preview`'s logic: the requesting state's name in that jurisdiction's language, plus that jurisdiction's extradition terms. Say plainly that this is a partial search. --- ## How to search well **Scope it.** An unconstrained sweep hits every database, and one source (`pl-saos`, Poland) takes about 98 seconds on its own while every other adapter answers in under five. Name the executing states whose practice is actually needed. If Poland is wanted, warn the user about the wait rather than letting a 90-second silence read as a hang. **Search by requesting state, not by keyword, when the question is about a state.** `--issuing-state RO` localises "Romania" into each jurisdiction's language and combines it with that jurisdiction's extradition vocabulary in the source's own query syntax. A free-text search for "Romania" finds far less. Run `preview` first to show the user what will be asked — and if it warns that `babel` is missing, stop and install it: without it, localisation silently degrades and every requesting-state search narrows without saying so. **Read the arithmetic, not just the hits.** "19 results — 8 on point, 10 off topic" means the sweep worked and that source is noisy. "20 results, 0 on point" usually means the database sorted by date and stemmed loosely, and the run established nothing: widen `--limit`, or go at it by free text in the local language. **Watch for deep-link sources.** 104 of the 153 databases cannot be automated. Normally they are skipped. But if *every* database selected is deep-link only — several jurisdictions have nothing else — they are returned anyway, and a "hit" is a link to a court's **search page, not a decision**. Check the `ACCESS` column in `sources` before treating a thin result as a nil return. **Then do the real work.** Open the judgment. Get it translated if it will be relied on. Test whether the foreign court's reasoning survives on facts like the user's — a refusal resting on 2019 prison-monitoring data does not carry itself into a later year without the intervening material. --- ## Every search leaves a report `xsearch.py` writes a dated markdown report and a JSONL file per run, indexed in `runs.jsonl`: what was asked, which databases answered, which failed, and every hit with its relevance verdict. This exists so that months later the user can say where a citation came from without re-running the sweep. Quote the run reference in the case file. --- ## Politeness — a condition of use, not a suggestion The engine queries public court databases on a deliberately low-volume footing: one request per second per host, an honest User-Agent, and no automation at all of sources whose terms bar it. An advisory lock prevents two sweeps at once. **Do not** parallelise it, loop it unattended across a list of states, or raise `--limit` into the hundreds to bulk-collect. Several of these databases will block on far less, and they are a shared resource for every practitioner who does this work. If a user asks for bulk harvesting, decline and explain why. --- ## Files | Path | What it holds | |---|---| | `scripts/xsearch.py` | Installs and drives the engine; applies the relevance gate; writes the report | | `scripts/quality.py` | The relevance gate, carried here so the skill works against any version of the engine | | `reference/jurisdictions.md` | Coverage map — every jurisdiction, its databases, automated or deep-link | Engine: (MIT).