agentic-paper-digest-skill

# Agentic Paper Digest ## When to use - Fetch a recent paper digest from arXiv and Hugging Face. - Produce JSON output for downstream agents. - Run a local API server when a polling workflow is needed. ## Prereqs - Python 3 and network access. - LLM access via `OPENAI_API_KEY` or an OpenAI-compatible provider via `LITELLM_API_BASE` + `LITELLM_API_KEY`. - `git` is optional for bootstrap; otherwise `curl`/`wget` (or Python) is used to download the repo. ## Get the code and install - Preferred: run the bootstrap helper script. It uses git when available or falls back to a zip download. ```bash bash "{baseDir}/scripts/bootstrap.sh" ``` - Override the clone location by setting `PROJECT_DIR`. ```bash PROJECT_DIR="$HOME/agentic_paper_digest" bash "{baseDir}/scripts/bootstrap.sh" ``` ## Run (CLI preferred) ```bash bash "{baseDir}/scripts/run_cli.sh" ``` - Pass through CLI flags as needed. ```bash bash "{baseDir}/scripts/run_cli.sh" --window-hours 24 --sources arxiv,hf ``` ## Run (API optional) ```bash bash "{baseDir}/scripts/run_api.sh" ``` - Trigger runs and read results. ```bash curl -X POST http://127.0.0.1:8000/api/run curl http://127.0.0.1:8000/api/status curl http://127.0.0.1:8000/api/papers ``` - Stop the API server if needed. ```bash bash "{baseDir}/scripts/stop_api.sh" ``` ## Outputs - CLI `--json` prints `run_id`, `seen`, `kept`, `window_start`, and `window_end`. - Data store: `data/papers.sqlite3` (under `PROJECT_DIR`). - API: `POST /api/run`, `GET /api/status`, `GET /api/papers`, `GET/POST /api/topics`, `GET/POST /api/settings`. ## Configuration Config files live in `PROJECT_DIR/config`. 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=/path/to/.env`). **Environment (.env or exported vars)** - `OPENAI_API_KEY`: required for OpenAI models (litellm reads this). - `LITELLM_API_BASE`, `LITELLM_API_KEY`: use an OpenAI-compatible proxy/provider. - `LITELLM_MODEL_RELEVANCE`, `LITELLM_MODEL_SUMMARY`: models for relevance and summarization (summary defaults to relevance model if unset). - `LITELLM_TEMPERATURE_RELEVANCE`, `LITELLM_TEMPERATURE_SUMMARY`: lower for more deterministic output. - `LITELLM_MAX_RETRIES`: retry count for LLM calls. - `LITELLM_DROP_PARAMS=1`: drop unsupported params to avoid provider errors. - `WINDOW_HOURS`, `APP_TZ`: recency window and timezone. - `ARXIV_CATEGORIES`: comma-separated categories (default includes `cs.CL,cs.AI,cs.LG,stat.ML,cs.CR`). - `ARXIV_API_BASE`, `HF_API_BASE`: override source endpoints if needed. - `ARXIV_MAX_RESULTS`, `ARXIV_PAGE_SIZE`: arXiv paging limits. - `MAX_CANDIDATES_PER_SOURCE`: cap candidates per source before LLM filtering. - `FETCH_TIMEOUT_S`, `REQUEST_TIMEOUT_S`: source fetch and per-request timeouts. - `ENABLE_PDF_TEXT=1`: include first-page PDF text in summaries; requires `PyMuPDF` (`pip install pymupdf`). - `DATA_DIR`: location for `papers.sqlite3`. - `CORS_ORIGINS`: comma-separated origins allowed by the API server (UI use). - Path overrides: `TOPICS_PATH`, `SETTINGS_PATH`, `AFFILIATION_BOOSTS_PATH`. **Config files** - `config/topics.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 /api/papers` when `apply_topic_caps=1`. - `config/settings.json`: overrides fetch limits (`arxiv_max_results`, `arxiv_page_size`, `fetch_timeout_s`, `max_candidates_per_source`). Updated via `POST /api/settings`. - `config/affiliations.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). ## Mandatory workflow (follow step-by-step) 1. **You first MUST open and read the configuration from the github repo: https://github.com/matanle51/agentic_paper_digest you downloaded**: - Load `config/topics.json`, `config/settings.json`, and `config/affiliations.json` (if present). - Note current topic IDs, caps, and fetch limits before asking the user to change them. 2. **ASK THE USER TO PROVIDE IT'S PREFERENCES ABOUT THE FOLLOWING (HELP THE USER)**: - **Topics of interest** → update `config/topics.json` (`topics[].id/label/description/keywords`, `max_per_topic`). Show current defaults and ask whether to keep or change them. - **Time window (hours)** → set `WINDOW_HOURS` (or pass `--window-hours` to CLI) **only if the user cares**; otherwise keep default to 24h. - ASK THE USER TO FILL THE FOLLOWING PARAMETERS (explain the user why are their intent): `ARXIV_CATEGORIES`, `ARXIV_MAX_RESULTS`, `ARXIV_PAGE_SIZE`, `MAX_CANDIDATES_PER_SOURCE`. Ask whether to keep defaults and show the current values. - **Model/provider** → set `OPENAI_API_KEY` *or* `LITELLM_API_KEY` (+ `LITELLM_API_BASE` if proxy), and set `LITELLM_MODEL_RELEVANCE`/`LITELLM_MODEL_SUMMARY`. - **Do NOT ask by default**: timezone, quality vs cost, timeouts, PDF text, affiliation biasing, sources list. Use defaults unless the user requests changes. 3. **Confirm workspace path**: Ask where to clone/run. Default to `PROJECT_DIR="$HOME/agentic_paper_digest"` if the user doesn’t care. Never hardcode `/Users/...` paths. 4. **Bootstrap the repo**: Run the bootstrap script (unless the repo already exists and the user says to skip). 5. **Create or verify `.env`**: - If `.env` is missing, create it from `.env.example` (in the repo), then ask the user to fill keys and any requested preferences. - Ensure at least one of `OPENAI_API_KEY` or `LITELLM_API_KEY` is set before running. 6. **Apply config changes**: - Edit JSON files directly (or use `POST /api/topics` and `POST /api/settings` if running the API). 7. **Run the pipeline**: - Prefer `scripts/run_cli.sh` for one-off JSON output. - Use `scripts/run_api.sh` only if the user explicitly asks for UI/API access or polling. 8. **Report results**: - If results are sparse, suggest increasing `WINDOW_HOURS`, `ARXIV_MAX_RESULTS`, or broadening topics. ## Getting good results - Help the user define and keep topics focused and mutually exclusive so the classifier can choose the right IDs. - Use a stronger model for summaries than for relevance if quality matters. - If using openAI's model, defualy to gpt-5-mini for good tradeoff. - Increase `WINDOW_HOURS` or `ARXIV_MAX_RESULTS` when results are sparse, or lower them if results are too noisy. - Tune `ARXIV_CATEGORIES` to your research domains. - Enable PDF text (`ENABLE_PDF_TEXT=1`) when abstracts are too thin. - Use modest affiliation weights to bias ranking without swamping relevance. - BE PROACTIVE AND HELP THE USER TUNE THE SKILL FOR GOOD RESULTS! ## Troubleshooting - Port 8000 busy: run `bash "{baseDir}/scripts/stop_api.sh"` or pass `--port` to the API command. - Empty results: increase `WINDOW_HOURS` or verify the API key in `.env`. - Missing API key errors: export `OPENAI_API_KEY` or `LITELLM_API_KEY` in the shell before running.