[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QG9JFSBPASJ1XBA4J2JQZT":3,"skill-same:01M6QG9JFSBPASJ1XBA4J2JQZT":116},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":14,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6QG9JFSBPASJ1XBA4J2JQZT","usfiscaldata",null,"Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"query","for","treasury","exchange rates","spending","economics","scientific","revenue","statistics","data","databases","daily","use","edit","write","read","Bash","open","api","2026-11-10 10:12:30",false,"---\nname: usfiscaldata\ndescription: Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.\nlicense: MIT\nallowed-tools: Read Write Edit Bash\nmetadata:\n  version: '1.3'\n  category: scientific-databases\n  maintainer: Kalaris Labs\n  tags: US Treasury, fiscal data, economics, public finance, open data\n---\n\n# U.S. Treasury Fiscal Data API\n\nFree, open REST API from the U.S. Department of the Treasury for federal financial data. No API key or registration required.\n\n**Base URL:** `https:\u002F\u002Fapi.fiscaldata.treasury.gov\u002Fservices\u002Fapi\u002Ffiscal_service`\n\nBrowse [54 datasets and 179 data tables](https:\u002F\u002Ffiscaldata.treasury.gov\u002Fdatasets\u002F) via the dataset search. Verify endpoint paths on each dataset's API Quick Guide — paths change over time.\n\n## Installation\n\n```bash\nuv pip install requests pandas\n```\n\n## Quick Start\n\n```python\nimport requests\nimport pandas as pd\n\nBASE_URL = \"https:\u002F\u002Fapi.fiscaldata.treasury.gov\u002Fservices\u002Fapi\u002Ffiscal_service\"\n\n# Get the current national debt (Debt to the Penny)\nresp = requests.get(f\"{BASE_URL}\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_to_penny\", params={\n    \"sort\": \"-record_date\",\n    \"page[size]\": 1\n})\ndata = resp.json()[\"data\"][0]\nprint(f\"Total public debt as of {data['record_date']}: ${float(data['tot_pub_debt_out_amt']):,.0f}\")\n```\n\n```python\n# Get Treasury exchange rates for recent quarters\nresp = requests.get(f\"{BASE_URL}\u002Fv1\u002Faccounting\u002Fod\u002Frates_of_exchange\", params={\n    \"fields\": \"country_currency_desc,exchange_rate,record_date\",\n    \"filter\": \"record_date:gte:2024-01-01\",\n    \"sort\": \"-record_date\",\n    \"page[size]\": 100\n})\ndf = pd.DataFrame(resp.json()[\"data\"])\n```\n\n## Authentication\n\nNone required. The API is fully open and free.\n\n## Core Parameters\n\n| Parameter | Example | Description |\n|-----------|---------|-------------|\n| `fields=` | `fields=record_date,tot_pub_debt_out_amt` | Select specific columns |\n| `filter=` | `filter=record_date:gte:2024-01-01` | Filter records |\n| `sort=` | `sort=-record_date` | Sort (prefix `-` for descending) |\n| `format=` | `format=json` | Output format: `json`, `csv`, `xml` |\n| `page[size]=` | `page[size]=100` | Records per page (default 100) |\n| `page[number]=` | `page[number]=2` | Page index (starts at 1) |\n\n**Filter operators:** `lt`, `lte`, `gt`, `gte`, `eq`, `in`\n\n```python\n# Multiple filters separated by comma\n\"filter=country_currency_desc:in:(Canada-Dollar,Mexico-Peso),record_date:gte:2024-01-01\"\n```\n\n## Key Datasets & Endpoints\n\n### Debt\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| Debt to the Penny | `\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_to_penny` | Daily |\n| Historical Debt Outstanding | `\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_outstanding` | Annual |\n| Schedules of Federal Debt | `\u002Fv1\u002Faccounting\u002Fod\u002Fschedules_fed_debt` | Monthly |\n\n### Daily & Monthly Statements\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| DTS Operating Cash Balance | `\u002Fv1\u002Faccounting\u002Fdts\u002Foperating_cash_balance` | Daily |\n| DTS Deposits & Withdrawals | `\u002Fv1\u002Faccounting\u002Fdts\u002Fdeposits_withdrawals_operating_cash` | Daily |\n| Monthly Treasury Statement (MTS) | `\u002Fv1\u002Faccounting\u002Fmts\u002Fmts_table_1` (18 tables — see [datasets-fiscal.md](references\u002Fdatasets-fiscal.md)) | Monthly |\n\n### Interest Rates & Exchange\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| Average Interest Rates on Treasury Securities | `\u002Fv2\u002Faccounting\u002Fod\u002Favg_interest_rates` | Monthly |\n| Treasury Reporting Rates of Exchange | `\u002Fv1\u002Faccounting\u002Fod\u002Frates_of_exchange` | Quarterly |\n| Interest Expense on Public Debt | `\u002Fv2\u002Faccounting\u002Fod\u002Finterest_expense` | Monthly |\n\n### Securities & Auctions\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| Treasury Securities Auctions Data | `\u002Fv1\u002Faccounting\u002Fod\u002Fauctions_query` | As Needed |\n| Treasury Securities Upcoming Auctions | `\u002Fv1\u002Faccounting\u002Fod\u002Fupcoming_auctions` | As Needed |\n| Treasury Securities Buybacks | `\u002Fv1\u002Faccounting\u002Fod\u002Fbuybacks_operations` | As Needed |\n\n### Savings Bonds\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| I Bonds Interest Rates | `\u002Fv1\u002Faccounting\u002Fod\u002Fi_bonds_interest_rates` | Semi-Annual |\n| Savings Bonds Issues, Redemptions & Maturities | `\u002Fv1\u002Faccounting\u002Fod\u002Fsavings_bonds_report` | Monthly |\n\n## Response Structure\n\n```json\n{\n  \"data\": [...],\n  \"meta\": {\n    \"count\": 100,\n    \"total-count\": 3790,\n    \"total-pages\": 38,\n    \"labels\": {\"field_name\": \"Human Readable Label\"},\n    \"dataTypes\": {\"field_name\": \"STRING|NUMBER|DATE|CURRENCY\"},\n    \"dataFormats\": {\"field_name\": \"String|10.2|YYYY-MM-DD\"}\n  },\n  \"links\": {\"self\": \"...\", \"first\": \"...\", \"prev\": null, \"next\": \"...\", \"last\": \"...\"}\n}\n```\n\n**Note:** All values are returned as strings. Convert as needed (e.g., `float()`, `pd.to_datetime()`). Null values appear as the string `\"null\"`.\n\n## Common Patterns\n\n### Load all pages into a DataFrame\n\nUse the bounded `fetch_all()` helper in [parameters.md](references\u002Fparameters.md). For small result sets, a single request with `page[size]=10000` may suffice when `meta.total-pages` is 1.\n\n```python\n# Single-page fetch when total-pages == 1\nparams = {\"sort\": \"-record_date\", \"page[size]\": 10000}\nresp = requests.get(f\"{BASE_URL}\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_outstanding\", params=params)\nresult = resp.json()\nif result[\"meta\"][\"total-pages\"] > 1:\n    raise ValueError(\"Use fetch_all() from parameters.md for multi-page results\")\ndf = pd.DataFrame(result[\"data\"])\n```\n\n### Aggregation (automatic sum)\n\nOmitting grouping fields triggers automatic aggregation:\n\n```python\n# Sum all deposits\u002Fwithdrawals by record_date and transaction type\nresp = requests.get(f\"{BASE_URL}\u002Fv1\u002Faccounting\u002Fdts\u002Fdeposits_withdrawals_operating_cash\", params={\n    \"fields\": \"record_date,transaction_type,transaction_today_amt\"\n})\n```\n\n## Reference Files\n\n- **[api-basics.md](references\u002Fapi-basics.md)** — URL structure, HTTP methods, versioning, data types\n- **[parameters.md](references\u002Fparameters.md)** — All parameters with detailed examples and edge cases\n- **[datasets-debt.md](references\u002Fdatasets-debt.md)** — Debt datasets: Debt to the Penny, Historical Debt, Schedules of Federal Debt, TROR\n- **[datasets-fiscal.md](references\u002Fdatasets-fiscal.md)** — Daily Treasury Statement, Monthly Treasury Statement, revenue, spending\n- **[datasets-interest-rates.md](references\u002Fdatasets-interest-rates.md)** — Average interest rates, exchange rates, TIPS\u002FCPI, certified interest rates\n- **[datasets-securities.md](references\u002Fdatasets-securities.md)** — Treasury auctions, savings bonds, SLGS, buybacks\n- **[response-format.md](references\u002Fresponse-format.md)** — Response objects, error handling, pagination, response codes\n- **[examples.md](references\u002Fexamples.md)** — Python, R, and pandas code examples for common use cases\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm network access, API keys (if required) and the database's current API documentation and rate limits.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Fetch a single known record and check the response format before bulk queries.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Identifiers resolve, record counts are reported, and the database version or access date is recorded.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| HTTP 429 or 5xx errors | Respect rate limits with backoff, batch requests, and report partial results honestly. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never invent accession numbers, IDs or records; report 'not found' instead.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n","# U.S. Treasury Fiscal Data API\n\nFree, open REST API from the U.S. Department of the Treasury for federal financial data. No API key or registration required.\n\n**Base URL:** `https:\u002F\u002Fapi.fiscaldata.treasury.gov\u002Fservices\u002Fapi\u002Ffiscal_service`\n\nBrowse [54 datasets and 179 data tables](https:\u002F\u002Ffiscaldata.treasury.gov\u002Fdatasets\u002F) via the dataset search. Verify endpoint paths on each dataset's API Quick Guide — paths change over time.\n\n## Installation\n\n```bash\nuv pip install requests pandas\n```\n\n## Quick Start\n\n```python\nimport requests\nimport pandas as pd\n\nBASE_URL = \"https:\u002F\u002Fapi.fiscaldata.treasury.gov\u002Fservices\u002Fapi\u002Ffiscal_service\"\n\n# Get the current national debt (Debt to the Penny)\nresp = requests.get(f\"{BASE_URL}\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_to_penny\", params={\n    \"sort\": \"-record_date\",\n    \"page[size]\": 1\n})\ndata = resp.json()[\"data\"][0]\nprint(f\"Total public debt as of {data['record_date']}: ${float(data['tot_pub_debt_out_amt']):,.0f}\")\n```\n\n```python\n# Get Treasury exchange rates for recent quarters\nresp = requests.get(f\"{BASE_URL}\u002Fv1\u002Faccounting\u002Fod\u002Frates_of_exchange\", params={\n    \"fields\": \"country_currency_desc,exchange_rate,record_date\",\n    \"filter\": \"record_date:gte:2024-01-01\",\n    \"sort\": \"-record_date\",\n    \"page[size]\": 100\n})\ndf = pd.DataFrame(resp.json()[\"data\"])\n```\n\n## Authentication\n\nNone required. The API is fully open and free.\n\n## Core Parameters\n\n| Parameter | Example | Description |\n|-----------|---------|-------------|\n| `fields=` | `fields=record_date,tot_pub_debt_out_amt` | Select specific columns |\n| `filter=` | `filter=record_date:gte:2024-01-01` | Filter records |\n| `sort=` | `sort=-record_date` | Sort (prefix `-` for descending) |\n| `format=` | `format=json` | Output format: `json`, `csv`, `xml` |\n| `page[size]=` | `page[size]=100` | Records per page (default 100) |\n| `page[number]=` | `page[number]=2` | Page index (starts at 1) |\n\n**Filter operators:** `lt`, `lte`, `gt`, `gte`, `eq`, `in`\n\n```python\n# Multiple filters separated by comma\n\"filter=country_currency_desc:in:(Canada-Dollar,Mexico-Peso),record_date:gte:2024-01-01\"\n```\n\n## Key Datasets & Endpoints\n\n### Debt\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| Debt to the Penny | `\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_to_penny` | Daily |\n| Historical Debt Outstanding | `\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_outstanding` | Annual |\n| Schedules of Federal Debt | `\u002Fv1\u002Faccounting\u002Fod\u002Fschedules_fed_debt` | Monthly |\n\n### Daily & Monthly Statements\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| DTS Operating Cash Balance | `\u002Fv1\u002Faccounting\u002Fdts\u002Foperating_cash_balance` | Daily |\n| DTS Deposits & Withdrawals | `\u002Fv1\u002Faccounting\u002Fdts\u002Fdeposits_withdrawals_operating_cash` | Daily |\n| Monthly Treasury Statement (MTS) | `\u002Fv1\u002Faccounting\u002Fmts\u002Fmts_table_1` (18 tables — see [datasets-fiscal.md](references\u002Fdatasets-fiscal.md)) | Monthly |\n\n### Interest Rates & Exchange\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| Average Interest Rates on Treasury Securities | `\u002Fv2\u002Faccounting\u002Fod\u002Favg_interest_rates` | Monthly |\n| Treasury Reporting Rates of Exchange | `\u002Fv1\u002Faccounting\u002Fod\u002Frates_of_exchange` | Quarterly |\n| Interest Expense on Public Debt | `\u002Fv2\u002Faccounting\u002Fod\u002Finterest_expense` | Monthly |\n\n### Securities & Auctions\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| Treasury Securities Auctions Data | `\u002Fv1\u002Faccounting\u002Fod\u002Fauctions_query` | As Needed |\n| Treasury Securities Upcoming Auctions | `\u002Fv1\u002Faccounting\u002Fod\u002Fupcoming_auctions` | As Needed |\n| Treasury Securities Buybacks | `\u002Fv1\u002Faccounting\u002Fod\u002Fbuybacks_operations` | As Needed |\n\n### Savings Bonds\n\n| Dataset | Endpoint | Frequency |\n|---------|----------|-----------|\n| I Bonds Interest Rates | `\u002Fv1\u002Faccounting\u002Fod\u002Fi_bonds_interest_rates` | Semi-Annual |\n| Savings Bonds Issues, Redemptions & Maturities | `\u002Fv1\u002Faccounting\u002Fod\u002Fsavings_bonds_report` | Monthly |\n\n## Response Structure\n\n```json\n{\n  \"data\": [...],\n  \"meta\": {\n    \"count\": 100,\n    \"total-count\": 3790,\n    \"total-pages\": 38,\n    \"labels\": {\"field_name\": \"Human Readable Label\"},\n    \"dataTypes\": {\"field_name\": \"STRING|NUMBER|DATE|CURRENCY\"},\n    \"dataFormats\": {\"field_name\": \"String|10.2|YYYY-MM-DD\"}\n  },\n  \"links\": {\"self\": \"...\", \"first\": \"...\", \"prev\": null, \"next\": \"...\", \"last\": \"...\"}\n}\n```\n\n**Note:** All values are returned as strings. Convert as needed (e.g., `float()`, `pd.to_datetime()`). Null values appear as the string `\"null\"`.\n\n## Common Patterns\n\n### Load all pages into a DataFrame\n\nUse the bounded `fetch_all()` helper in [parameters.md](references\u002Fparameters.md). For small result sets, a single request with `page[size]=10000` may suffice when `meta.total-pages` is 1.\n\n```python\n# Single-page fetch when total-pages == 1\nparams = {\"sort\": \"-record_date\", \"page[size]\": 10000}\nresp = requests.get(f\"{BASE_URL}\u002Fv2\u002Faccounting\u002Fod\u002Fdebt_outstanding\", params=params)\nresult = resp.json()\nif result[\"meta\"][\"total-pages\"] > 1:\n    raise ValueError(\"Use fetch_all() from parameters.md for multi-page results\")\ndf = pd.DataFrame(result[\"data\"])\n```\n\n### Aggregation (automatic sum)\n\nOmitting grouping fields triggers automatic aggregation:\n\n```python\n# Sum all deposits\u002Fwithdrawals by record_date and transaction type\nresp = requests.get(f\"{BASE_URL}\u002Fv1\u002Faccounting\u002Fdts\u002Fdeposits_withdrawals_operating_cash\", params={\n    \"fields\": \"record_date,transaction_type,transaction_today_amt\"\n})\n```\n\n## Reference Files\n\n- **[api-basics.md](references\u002Fapi-basics.md)** — URL structure, HTTP methods, versioning, data types\n- **[parameters.md](references\u002Fparameters.md)** — All parameters with detailed examples and edge cases\n- **[datasets-debt.md](references\u002Fdatasets-debt.md)** — Debt datasets: Debt to the Penny, Historical Debt, Schedules of Federal Debt, TROR\n- **[datasets-fiscal.md](references\u002Fdatasets-fiscal.md)** — Daily Treasury Statement, Monthly Treasury Statement, revenue, spending\n- **[datasets-interest-rates.md](references\u002Fdatasets-interest-rates.md)** — Average interest rates, exchange rates, TIPS\u002FCPI, certified interest rates\n- **[datasets-securities.md](references\u002Fdatasets-securities.md)** — Treasury auctions, savings bonds, SLGS, buybacks\n- **[response-format.md](references\u002Fresponse-format.md)** — Response objects, error handling, pagination, response codes\n- **[examples.md](references\u002Fexamples.md)** — Python, R, and pandas code examples for common use cases\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm network access, API keys (if required) and the database's current API documentation and rate limits.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Fetch a single known record and check the response format before bulk queries.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Identifiers resolve, record counts are reported, and the database version or access date is recorded.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| HTTP 429 or 5xx errors | Respect rate limits with backoff, batch requests, and report partial results honestly. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Never invent accession numbers, IDs or records; report 'not found' instead.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n",{"name":5,"license":15,"metadata":53,"description":7,"allowed-tools":58},{"tags":54,"version":55,"category":56,"maintainer":57},"US Treasury, fiscal data, economics, public finance, open data","1.3","scientific-databases","Kalaris Labs","Read Write Edit Bash","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-usfiscaldata","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill usfiscaldata",{"tool":74,"command":75},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill usfiscaldata",{"tool":77,"command":78},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill usfiscaldata","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002FSKILL.md",true,[82,88,92,96,100,104,108,112],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fapi-basics.md","REFERENCE","参考文档",3704,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fapi-basics.md",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fdatasets-debt.md",5333,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fdatasets-debt.md",{"path":93,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fdatasets-fiscal.md",8867,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fdatasets-fiscal.md",{"path":97,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fdatasets-interest-rates.md",6423,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fdatasets-interest-rates.md",{"path":101,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fdatasets-securities.md",7908,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fdatasets-securities.md",{"path":105,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Fexamples.md",9665,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fexamples.md",{"path":109,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Fparameters.md",5465,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fparameters.md",{"path":113,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":114,"sourceHtmlUrl":115},"references\u002Fresponse-format.md",5196,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-usfiscaldata\u002Freferences\u002Fresponse-format.md",[117,145,168,195,220,244],{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":126,"publishedAt":48},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[127,128,129,130,131,132,38,133,134,135,136,137,138,139,140,141,30,142,143,144],"youtube full","youtube","full","transcript","search","monitoring","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":48},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[152,153,154,155,131,156,157,158,159,160,161,162,43,163,41,164,165,166,167,30],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","strategy","platform","grow","posting","competitive",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":177,"publishedAt":48},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[174],{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[178,43,179,180,181,182,183,184,41,185,186,59,187,188,189,190,191,192,193,194],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":201,"publishedAt":48},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[202,203,204,205,206,207,208,209,210,41,211,137,212,213,214,215,216,217,218,219],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":227,"publishedAt":48},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[226],{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},[228,229,230,231,232,180,207,233,234,235,236,237,43,238,136,239,240,241,242,243],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":245,"name":246,"nameZh":6,"description":247,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":248,"categories":249,"keywords":250,"publishedAt":48},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[251,252,253,254,255,256,257,46,258,236,259,260,261,262,263,264,138,265,266,219],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]