[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MSYJRZQQEDBPDDT3QP8AGK":3,"skill-same:01M6MSYJRZQQEDBPDDT3QP8AGK":108},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":30,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":6,"ownerHtmlUrl":67,"ownerBlogUrl":6,"ownerDescription":68,"updatedAt":69,"install":70,"resources":83},"01M6MSYJRZQQEDBPDDT3QP8AGK","agentic-paper-digest-skill",null,"Fetches and summarizes recent arXiv and Hugging Face papers with Agentic Paper Digest. Use when the user wants a paper digest, a JSON feed of recent papers, or to run the arXiv\u002FHF pipeline.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002FGit和GitHub\u002Fagentic-paper-digest\u002FSKILL.md",[17,22,25],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"agentic","agentic paper digest skill","paper","digest","use","for","papers","openai-compatible","with","via","either","python","curl","litellm","python3","access","api","pipeline","bootstrap","github","2026-11-10 10:12:30",false,"---\nname: agentic-paper-digest-skill\ndescription: Fetches and summarizes recent arXiv and Hugging Face papers with Agentic Paper Digest. Use when the user wants a paper digest, a JSON feed of recent papers, or to run the arXiv\u002FHF pipeline.\nhomepage: https:\u002F\u002Fgithub.com\u002Fmatanle51\u002Fagentic_paper_digest\ncompatibility: Requires Python 3, network access, and either git or curl\u002Fwget for bootstrap. LLM access via OPENAI_API_KEY or LITELLM_API_KEY (OpenAI-compatible).\nmetadata: {\"clawdbot\":{\"requires\":{\"anyBins\":[\"python3\",\"python\"]}}}\n---\n\n# Agentic Paper Digest\n\n## When to use\n- Fetch a recent paper digest from arXiv and Hugging Face.\n- Produce JSON output for downstream agents.\n- Run a local API server when a polling workflow is needed.\n\n## Prereqs\n- Python 3 and network access.\n- LLM access via `OPENAI_API_KEY` or an OpenAI-compatible provider via `LITELLM_API_BASE` + `LITELLM_API_KEY`.\n- `git` is optional for bootstrap; otherwise `curl`\u002F`wget` (or Python) is used to download the repo.\n\n## Get the code and install\n- Preferred: run the bootstrap helper script. It uses git when available or falls back to a zip download.\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Fbootstrap.sh\"\n```\n\n- Override the clone location by setting `PROJECT_DIR`.\n\n```bash\nPROJECT_DIR=\"$HOME\u002Fagentic_paper_digest\" bash \"{baseDir}\u002Fscripts\u002Fbootstrap.sh\"\n```\n\n## Run (CLI preferred)\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Frun_cli.sh\"\n```\n\n- Pass through CLI flags as needed.\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Frun_cli.sh\" --window-hours 24 --sources arxiv,hf\n```\n\n## Run (API optional)\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Frun_api.sh\"\n```\n\n- Trigger runs and read results.\n\n```bash\ncurl -X POST http:\u002F\u002F127.0.0.1:8000\u002Fapi\u002Frun\ncurl http:\u002F\u002F127.0.0.1:8000\u002Fapi\u002Fstatus\ncurl http:\u002F\u002F127.0.0.1:8000\u002Fapi\u002Fpapers\n```\n\n- Stop the API server if needed.\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Fstop_api.sh\"\n```\n\n## Outputs\n- CLI `--json` prints `run_id`, `seen`, `kept`, `window_start`, and `window_end`.\n- Data store: `data\u002Fpapers.sqlite3` (under `PROJECT_DIR`).\n- API: `POST \u002Fapi\u002Frun`, `GET \u002Fapi\u002Fstatus`, `GET \u002Fapi\u002Fpapers`, `GET\u002FPOST \u002Fapi\u002Ftopics`, `GET\u002FPOST \u002Fapi\u002Fsettings`.\n\n## Configuration\nConfig files live in `PROJECT_DIR\u002Fconfig`. Environment variables can be set in the shell or via a `.env` file. The wrappers here auto-load `.env` from `PROJECT_DIR` (override with `ENV_FILE=\u002Fpath\u002Fto\u002F.env`).\n\n**Environment (.env or exported vars)**\n- `OPENAI_API_KEY`: required for OpenAI models (litellm reads this).\n- `LITELLM_API_BASE`, `LITELLM_API_KEY`: use an OpenAI-compatible proxy\u002Fprovider.\n- `LITELLM_MODEL_RELEVANCE`, `LITELLM_MODEL_SUMMARY`: models for relevance and summarization (summary defaults to relevance model if unset).\n- `LITELLM_TEMPERATURE_RELEVANCE`, `LITELLM_TEMPERATURE_SUMMARY`: lower for more deterministic output.\n- `LITELLM_MAX_RETRIES`: retry count for LLM calls.\n- `LITELLM_DROP_PARAMS=1`: drop unsupported params to avoid provider errors.\n- `WINDOW_HOURS`, `APP_TZ`: recency window and timezone.\n- `ARXIV_CATEGORIES`: comma-separated categories (default includes `cs.CL,cs.AI,cs.LG,stat.ML,cs.CR`).\n- `ARXIV_API_BASE`, `HF_API_BASE`: override source endpoints if needed.\n- `ARXIV_MAX_RESULTS`, `ARXIV_PAGE_SIZE`: arXiv paging limits.\n- `MAX_CANDIDATES_PER_SOURCE`: cap candidates per source before LLM filtering.\n- `FETCH_TIMEOUT_S`, `REQUEST_TIMEOUT_S`: source fetch and per-request timeouts.\n- `ENABLE_PDF_TEXT=1`: include first-page PDF text in summaries; requires `PyMuPDF` (`pip install pymupdf`).\n- `DATA_DIR`: location for `papers.sqlite3`.\n- `CORS_ORIGINS`: comma-separated origins allowed by the API server (UI use).\n- Path overrides: `TOPICS_PATH`, `SETTINGS_PATH`, `AFFILIATION_BOOSTS_PATH`.\n\n**Config files**\n- `config\u002Ftopics.json`: list of topics with `id`, `label`, `description`, `max_per_topic`, and `keywords`. The relevance classifier must output topic IDs exactly as defined here. `max_per_topic` also caps results in `GET \u002Fapi\u002Fpapers` when `apply_topic_caps=1`.\n- `config\u002Fsettings.json`: overrides fetch limits (`arxiv_max_results`, `arxiv_page_size`, `fetch_timeout_s`, `max_candidates_per_source`). Updated via `POST \u002Fapi\u002Fsettings`.\n- `config\u002Faffiliations.json`: list of `{pattern, weight}` boosts applied by substring match over affiliations. Weights add up and are capped at 1.0. Invalid JSON disables boosts, so keep the file strict JSON (no trailing commas).\n\n## Mandatory workflow (follow step-by-step)\n1. **Read existing configuration**:\n   - Load `config\u002Ftopics.json`, `config\u002Fsettings.json`, and `config\u002Faffiliations.json` (if present).\n   - Note current topic IDs, caps, and fetch limits before asking the user to change them.\n2. **Map user intent to configuration (ask only what’s needed)**:\n   - **Topics of interest** → update `config\u002Ftopics.json` (`topics[].id\u002Flabel\u002Fdescription\u002Fkeywords`, `max_per_topic`).  \n     Show current defaults and ask whether to keep or change them.\n   - **Time window (hours)** → set `WINDOW_HOURS` (or pass `--window-hours` to CLI) **only if the user cares**; otherwise keep defaults.\n   - **Search scope** → set `ARXIV_CATEGORIES`, `ARXIV_MAX_RESULTS`, `ARXIV_PAGE_SIZE`, `MAX_CANDIDATES_PER_SOURCE`.  \n     Ask whether to keep defaults and show the current values.\n   - **Model\u002Fprovider** → set `OPENAI_API_KEY` *or* `LITELLM_API_KEY` (+ `LITELLM_API_BASE` if proxy), and set `LITELLM_MODEL_RELEVANCE`\u002F`LITELLM_MODEL_SUMMARY`.\n   - **API UI access** → set `CORS_ORIGINS` only if the user explicitly wants the UI on a different origin.\n   - **Do NOT ask by default**: timezone, quality vs cost, timeouts, PDF text, affiliation biasing, sources list. Use defaults unless the user requests changes.\n3. **Confirm workspace path**: Ask where to clone\u002Frun. Default to `PROJECT_DIR=\"$HOME\u002Fagentic_paper_digest\"` if the user doesn’t care. Never hardcode `\u002FUsers\u002F...` paths.\n4. **Bootstrap the repo**: Run the bootstrap script (unless the repo already exists and the user says to skip).\n5. **Create or verify `.env`**:\n   - If `.env` is missing, create it from `.env.example` (in the repo), then ask the user to fill keys and any requested preferences.\n   - Ensure at least one of `OPENAI_API_KEY` or `LITELLM_API_KEY` is set before running.\n6. **Apply config changes**:\n   - Edit JSON files directly (or use `POST \u002Fapi\u002Ftopics` and `POST \u002Fapi\u002Fsettings` if running the API).\n7. **Run the pipeline**:\n   - Prefer `scripts\u002Frun_cli.sh` for one-off JSON output.\n   - Use `scripts\u002Frun_api.sh` only if the user explicitly asks for UI\u002FAPI access or polling.\n8. **Report results**:\n   - Summarize run stats (`seen`, `kept`, window).\n   - If results are sparse, suggest increasing `WINDOW_HOURS`, `ARXIV_MAX_RESULTS`, or broadening topics.\n\n## Getting good results\n- Keep topics focused and mutually exclusive so the classifier can choose the right IDs.\n- Use a stronger model for summaries than for relevance if quality matters.\n- Increase `WINDOW_HOURS` or `ARXIV_MAX_RESULTS` when results are sparse, or lower them if results are too noisy.\n- Tune `ARXIV_CATEGORIES` to your research domains.\n- Enable PDF text (`ENABLE_PDF_TEXT=1`) when abstracts are too thin.\n- Use modest affiliation weights to bias ranking without swamping relevance.\n\n## Troubleshooting\n- Port 8000 busy: run `bash \"{baseDir}\u002Fscripts\u002Fstop_api.sh\"` or pass `--port` to the API command.\n- Empty results: increase `WINDOW_HOURS` or verify the API key in `.env`.\n- Missing API key errors: export `OPENAI_API_KEY` or `LITELLM_API_KEY` in the shell before running.\n","# Agentic Paper Digest\n\n## When to use\n- Fetch a recent paper digest from arXiv and Hugging Face.\n- Produce JSON output for downstream agents.\n- Run a local API server when a polling workflow is needed.\n\n## Prereqs\n- Python 3 and network access.\n- LLM access via `OPENAI_API_KEY` or an OpenAI-compatible provider via `LITELLM_API_BASE` + `LITELLM_API_KEY`.\n- `git` is optional for bootstrap; otherwise `curl`\u002F`wget` (or Python) is used to download the repo.\n\n## Get the code and install\n- Preferred: run the bootstrap helper script. It uses git when available or falls back to a zip download.\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Fbootstrap.sh\"\n```\n\n- Override the clone location by setting `PROJECT_DIR`.\n\n```bash\nPROJECT_DIR=\"$HOME\u002Fagentic_paper_digest\" bash \"{baseDir}\u002Fscripts\u002Fbootstrap.sh\"\n```\n\n## Run (CLI preferred)\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Frun_cli.sh\"\n```\n\n- Pass through CLI flags as needed.\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Frun_cli.sh\" --window-hours 24 --sources arxiv,hf\n```\n\n## Run (API optional)\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Frun_api.sh\"\n```\n\n- Trigger runs and read results.\n\n```bash\ncurl -X POST http:\u002F\u002F127.0.0.1:8000\u002Fapi\u002Frun\ncurl http:\u002F\u002F127.0.0.1:8000\u002Fapi\u002Fstatus\ncurl http:\u002F\u002F127.0.0.1:8000\u002Fapi\u002Fpapers\n```\n\n- Stop the API server if needed.\n\n```bash\nbash \"{baseDir}\u002Fscripts\u002Fstop_api.sh\"\n```\n\n## Outputs\n- CLI `--json` prints `run_id`, `seen`, `kept`, `window_start`, and `window_end`.\n- Data store: `data\u002Fpapers.sqlite3` (under `PROJECT_DIR`).\n- API: `POST \u002Fapi\u002Frun`, `GET \u002Fapi\u002Fstatus`, `GET \u002Fapi\u002Fpapers`, `GET\u002FPOST \u002Fapi\u002Ftopics`, `GET\u002FPOST \u002Fapi\u002Fsettings`.\n\n## Configuration\nConfig files live in `PROJECT_DIR\u002Fconfig`. Environment variables can be set in the shell or via a `.env` file. The wrappers here auto-load `.env` from `PROJECT_DIR` (override with `ENV_FILE=\u002Fpath\u002Fto\u002F.env`).\n\n**Environment (.env or exported vars)**\n- `OPENAI_API_KEY`: required for OpenAI models (litellm reads this).\n- `LITELLM_API_BASE`, `LITELLM_API_KEY`: use an OpenAI-compatible proxy\u002Fprovider.\n- `LITELLM_MODEL_RELEVANCE`, `LITELLM_MODEL_SUMMARY`: models for relevance and summarization (summary defaults to relevance model if unset).\n- `LITELLM_TEMPERATURE_RELEVANCE`, `LITELLM_TEMPERATURE_SUMMARY`: lower for more deterministic output.\n- `LITELLM_MAX_RETRIES`: retry count for LLM calls.\n- `LITELLM_DROP_PARAMS=1`: drop unsupported params to avoid provider errors.\n- `WINDOW_HOURS`, `APP_TZ`: recency window and timezone.\n- `ARXIV_CATEGORIES`: comma-separated categories (default includes `cs.CL,cs.AI,cs.LG,stat.ML,cs.CR`).\n- `ARXIV_API_BASE`, `HF_API_BASE`: override source endpoints if needed.\n- `ARXIV_MAX_RESULTS`, `ARXIV_PAGE_SIZE`: arXiv paging limits.\n- `MAX_CANDIDATES_PER_SOURCE`: cap candidates per source before LLM filtering.\n- `FETCH_TIMEOUT_S`, `REQUEST_TIMEOUT_S`: source fetch and per-request timeouts.\n- `ENABLE_PDF_TEXT=1`: include first-page PDF text in summaries; requires `PyMuPDF` (`pip install pymupdf`).\n- `DATA_DIR`: location for `papers.sqlite3`.\n- `CORS_ORIGINS`: comma-separated origins allowed by the API server (UI use).\n- Path overrides: `TOPICS_PATH`, `SETTINGS_PATH`, `AFFILIATION_BOOSTS_PATH`.\n\n**Config files**\n- `config\u002Ftopics.json`: list of topics with `id`, `label`, `description`, `max_per_topic`, and `keywords`. The relevance classifier must output topic IDs exactly as defined here. `max_per_topic` also caps results in `GET \u002Fapi\u002Fpapers` when `apply_topic_caps=1`.\n- `config\u002Fsettings.json`: overrides fetch limits (`arxiv_max_results`, `arxiv_page_size`, `fetch_timeout_s`, `max_candidates_per_source`). Updated via `POST \u002Fapi\u002Fsettings`.\n- `config\u002Faffiliations.json`: list of `{pattern, weight}` boosts applied by substring match over affiliations. Weights add up and are capped at 1.0. Invalid JSON disables boosts, so keep the file strict JSON (no trailing commas).\n\n## Mandatory workflow (follow step-by-step)\n1. **Read existing configuration**:\n   - Load `config\u002Ftopics.json`, `config\u002Fsettings.json`, and `config\u002Faffiliations.json` (if present).\n   - Note current topic IDs, caps, and fetch limits before asking the user to change them.\n2. **Map user intent to configuration (ask only what’s needed)**:\n   - **Topics of interest** → update `config\u002Ftopics.json` (`topics[].id\u002Flabel\u002Fdescription\u002Fkeywords`, `max_per_topic`).  \n     Show current defaults and ask whether to keep or change them.\n   - **Time window (hours)** → set `WINDOW_HOURS` (or pass `--window-hours` to CLI) **only if the user cares**; otherwise keep defaults.\n   - **Search scope** → set `ARXIV_CATEGORIES`, `ARXIV_MAX_RESULTS`, `ARXIV_PAGE_SIZE`, `MAX_CANDIDATES_PER_SOURCE`.  \n     Ask whether to keep defaults and show the current values.\n   - **Model\u002Fprovider** → set `OPENAI_API_KEY` *or* `LITELLM_API_KEY` (+ `LITELLM_API_BASE` if proxy), and set `LITELLM_MODEL_RELEVANCE`\u002F`LITELLM_MODEL_SUMMARY`.\n   - **API UI access** → set `CORS_ORIGINS` only if the user explicitly wants the UI on a different origin.\n   - **Do NOT ask by default**: timezone, quality vs cost, timeouts, PDF text, affiliation biasing, sources list. Use defaults unless the user requests changes.\n3. **Confirm workspace path**: Ask where to clone\u002Frun. Default to `PROJECT_DIR=\"$HOME\u002Fagentic_paper_digest\"` if the user doesn’t care. Never hardcode `\u002FUsers\u002F...` paths.\n4. **Bootstrap the repo**: Run the bootstrap script (unless the repo already exists and the user says to skip).\n5. **Create or verify `.env`**:\n   - If `.env` is missing, create it from `.env.example` (in the repo), then ask the user to fill keys and any requested preferences.\n   - Ensure at least one of `OPENAI_API_KEY` or `LITELLM_API_KEY` is set before running.\n6. **Apply config changes**:\n   - Edit JSON files directly (or use `POST \u002Fapi\u002Ftopics` and `POST \u002Fapi\u002Fsettings` if running the API).\n7. **Run the pipeline**:\n   - Prefer `scripts\u002Frun_cli.sh` for one-off JSON output.\n   - Use `scripts\u002Frun_api.sh` only if the user explicitly asks for UI\u002FAPI access or polling.\n8. **Report results**:\n   - Summarize run stats (`seen`, `kept`, window).\n   - If results are sparse, suggest increasing `WINDOW_HOURS`, `ARXIV_MAX_RESULTS`, or broadening topics.\n\n## Getting good results\n- Keep topics focused and mutually exclusive so the classifier can choose the right IDs.\n- Use a stronger model for summaries than for relevance if quality matters.\n- Increase `WINDOW_HOURS` or `ARXIV_MAX_RESULTS` when results are sparse, or lower them if results are too noisy.\n- Tune `ARXIV_CATEGORIES` to your research domains.\n- Enable PDF text (`ENABLE_PDF_TEXT=1`) when abstracts are too thin.\n- Use modest affiliation weights to bias ranking without swamping relevance.\n\n## Troubleshooting\n- Port 8000 busy: run `bash \"{baseDir}\u002Fscripts\u002Fstop_api.sh\"` or pass `--port` to the API command.\n- Empty results: increase `WINDOW_HOURS` or verify the API key in `.env`.\n- Missing API key errors: export `OPENAI_API_KEY` or `LITELLM_API_KEY` in the shell before running.\n",{"name":5,"homepage":56,"metadata":57,"description":7,"compatibility":61},"https:\u002F\u002Fgithub.com\u002Fmatanle51\u002Fagentic_paper_digest",{"clawdbot":58},{"requires":59},{"anyBins":60},[45,42],"Requires Python 3, network access, and either git or curl\u002Fwget for bootstrap. 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Use when a user wants passive DeFi yield on their funds. 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