ソース情報
- リポジトリ
- DreamLab-AI/agentbox
- ソースの最終更新活動
- 2026年4月23日 16:31
- 検出された SKILL.md の言語
- 英語
- スター
- 19
- フォーク
- 0
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
メニュー
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/DreamLab-AI/agentbox --skill nyc-acrisコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか?SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Implement features with tests and quality gates, debug hard bugs, and stress-test designs. Use when building a feature with TDD/EDD, chasing a stubborn multi-function bug (feedback-loop-first), interrogating a design before coding, or running a coverage/security/accessibility quality-gate pass. Supersedes agentic-qe, reasoningbank-*, and pair-programming.
Plan-execute-reflect loop over a persistent Python kernel (the `code-interpreter` MCP): write Python, execute, observe the returned trace (stdout / exception / last value), then revise. Variables, imports, and dataframes survive across every tool call in the session. Use for multi-step numerical reasoning, data wrangling, scientific Q&A, hypothesis-test loops, or any task where intermediate state must persist between turns and a runtime result should drive the next step.
Fires as a post-task hook after a non-trivial completed task (3+ tool calls, observable terminal outcome) to distil 0-N generalisable IF/THEN lessons from the trajectory and store them in RuVector so they surface at the start of similar future tasks; also invocable manually to record a lesson after a complex or failed task. NOT for trivial (<3 tool calls) or interrupted tasks, and NOT a general memory-write tool — it writes only ex:DistilledLesson records to the code-harness-lessons namespace. Schema, gates, prompt, and contradiction/archive policy live in references/.
SOC 職業分類に基づく
| name | nyc-acris |
| description | Look up ACRIS property transaction records — deeds, mortgages, liens. |
| allowed-tools | ["WebFetch","Write","Read","Bash"] |
| user-invocable | true |
Look up ACRIS (Automated City Register Information System) property records — deeds, mortgages, liens, and other recorded documents. Uses a 3-table join across Legals, Master, and Parties datasets. No API key required.
/nyc-acris 120 Broadway, Manhattan
/nyc-acris 1000770001 (BBL)
/nyc-acris 1001389 (BIN)
Accept one of:
Borough codes: Manhattan=1/MN, Bronx=2/BX, Brooklyn=3/BK, Queens=4/QN, Staten Island=5/SI
Query PLUTO to get BBL, BIN, and building metadata. No API key needed.
By BBL:
https://data.cityofnewyork.us/resource/64uk-42ks.json?bbl={BBL}
By address:
https://data.cityofnewyork.us/resource/64uk-42ks.json?$where=upper(address) LIKE '%{STREET}%'&borough='{BORO_CODE}'&$limit=5
Address normalization: Uppercase, strip unit/apt suffixes. Borough names to codes: Manhattan=MN, Bronx=BX, Brooklyn=BK, Queens=QN, Staten Island=SI. If multiple results, ask the user to pick. If zero, try variations or suggest providing a BBL.
Store from PLUTO: bbl, bin (or bldgbin), address, borough, bldgclass, zonedist1, yearbuilt, ownername, numfloors, lotarea, latitude, longitude.
Parse BBL into separate components (required for ACRIS): boro = digit 1, block = digits 2-6 (zero-padded), lot = digits 7-10 (zero-padded).
IMPORTANT: ACRIS requires BBL (not BIN). The Legals table uses separate borough, block, lot fields — not a combined BBL field.
https://data.cityofnewyork.us/resource/8h5j-fqxa.json?borough={boro}&block={block}&lot={lot}&$order=good_through_date DESC&$limit=20
Extract document_id from each row. These are the join keys for the next two queries.
Build a $where clause with the document_ids from Step 3a:
https://data.cityofnewyork.us/resource/bnx9-e6tj.json?$where=document_id IN ('{id1}','{id2}','{id3}',...)&$order=doc_date DESC
Key fields: document_id, record_type, crfn, doc_type, doc_date, doc_amount, recorded_filed
Same document_ids:
https://data.cityofnewyork.us/resource/636b-3b5g.json?$where=document_id IN ('{id1}','{id2}','{id3}',...)
Key fields: document_id, party_type, name, address_1, city, state, zip
Party types: 1 = Grantor (seller/borrower/assignor), 2 = Grantee (buyer/lender/assignee)
Fetch once to translate doc_type codes to descriptions:
https://data.cityofnewyork.us/resource/7isb-wh4c.json?$limit=200
Common codes: DEED, MTGE (Mortgage), AGMT (Agreement), ASST (Assignment), SAT (Satisfaction), RPTT (Transfer Tax), ALIS (Assignment of Leases), UCC1 (UCC Filing), MCON (Mortgage Consolidation)
For each document_id:
## Property Records (ACRIS) — {Address}
**BBL:** {bbl} (Borough {boro}, Block {block}, Lot {lot})
**Documents found:** {count} (showing 20 most recent)
### Deeds (Ownership)
| Date | Doc Type | Amount | From (Grantor) | To (Grantee) |
|------|----------|--------|----------------|--------------|
| YYYY-MM-DD | Deed | $X,XXX,XXX | ... | ... |
**Current owner (per most recent deed):** {grantee name}
### Mortgages
| Date | Amount | Lender (Grantee) | Borrower (Grantor) |
|------|--------|-------------------|---------------------|
| YYYY-MM-DD | $X,XXX,XXX | ... | ... |
### Other Documents
| Date | Doc Type | Amount | Grantor | Grantee |
|------|----------|--------|---------|---------|
| ... | Assignment | ... | ... | ... |
**Note:** Condo units may have records on both the unit lot and the parent condo lot. If results seem incomplete, try querying the main condo lot as well.
Source: [ACRIS Real Property](https://data.cityofnewyork.us/City-Government/ACRIS-Real-Property-Master/bnx9-e6tj)
If no documents found: "No ACRIS records found for this property."