# Watchtower > Run a scheduled web-surveillance sweep over a watchlist of topics using Tavily web_search, optional Tavily extraction, deterministic dedup/exclusion filtering, and a cited Markdown digest. Use when the user asks to run a sweep, run a watchtower sweep, monitor the watchlist, check the watchlist, or asks what changed since the last run. Trigger keywords - run a sweep, watchtower sweep, monitor watchlist, what changed. - Skill: `nvidia/watchtower` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add nvidia/watchtower` - Raw SKILL.md: https://api.skillmd.com/api/skills/nvidia/watchtower/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - License: Apache-2.0 - Author: NVIDIA (https://skillmd.com/u/nvidia) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/nvidia/watchtower --- # watchtower Sweep every topic in the active watchlist for genuinely new items, judge their relevance and significance, and write a cited digest plus a structured changelog. ## Design principle **Scripts enforce mechanics; the agent makes editorial judgments.** You choose search queries, assess source credibility, and judge relevance/significance. The scripts only decide mechanical questions: is this URL already seen, is the topic known, or is the host explicitly excluded? Never re-implement dedup or exclude filtering with your own judgment: always pipe candidates through `diff_state.py`, and always advance state through `commit_state.py`. Hard rules: - Never fabricate URLs. Cite only URLs returned by `web_search` or fetched with `tavily_extract` from a surviving `web_search` URL in this run. - Optional `seed_sources` are search hints, not a hard allowlist. Interesting off-source results may be included when they are credible and relevant. - Optional `exclude_domains` are a hard negative filter. Do not resurrect items dropped by `diff_state.py`. - If you need page text beyond the `web_search` snippet, fetch only surviving URLs with `tavily_extract`. Never fetch result URLs with `web_fetch`, browser tools, `curl`, or custom HTTP scripts. - Never advance state before both output files are written. ## Workspace root Treat `/sandbox/.openclaw/workspace` as the run root. Resolve the supplied watchlist path relative to that directory, and write `state/` and `outputs/` directly beneath it. Do not create a nested `workspace/watchtower/` directory. Because each `exec` call starts a new shell, use absolute paths or prefix each relative-path command with `cd /sandbox/.openclaw/workspace &&`. ## Run identity Set a run id at the start of the sweep: UTC date plus a short random suffix, e.g. `2026-07-06-k3f9`. Use it in both output filenames and in every item committed to state. ## Procedure In the commands below, replace `` with the watchlist path supplied in the sweep request. ### 1. Validate the active watchlist ```bash cd /sandbox/.openclaw/workspace && \ python3 ~/.openclaw/skills/watchtower/scripts/validate_watchlist.py "" ``` If validation fails, stop and report the error. Do not sweep an invalid watchlist. ### 2. Search each topic broadly For each topic, run 1-2 `web_search` queries built from the topic's `query`. Use optional fields as hints: - `lookback_days`: bias the query toward recent results, e.g. "past 30 days" or an equivalent time phrase. - `seed_sources`: run one source-biased query with `site:` operators when useful, but also allow a broader query so the sweep can find coverage elsewhere. - `exclude_domains`: do not manually apply these during search; `diff_state.py` enforces them in step 3. Example query pair: ```text new sanctions designation specially designated nationals list update past 14 days new sanctions designation specially designated nationals list update site:ofac.treasury.gov ``` ### 3. Collect candidates and filter deterministically Collect every result as a JSON line with fields `topic_id`, `url`, `title`, and any useful search-provided fields such as `snippet` or `content`; then pipe the batch through `diff_state.py`: ```bash cd /sandbox/.openclaw/workspace && \ " \ --state state/seen.json >survivors.jsonl ``` Only items that are unseen, belong to a known topic, and do not match that topic's `exclude_domains` survive. Everything dropped here is dropped for a mechanical reason — do not resurrect filtered items. ### 4. Extract and judge survivors only For each surviving item, judge relevance, source credibility, and significance against the topic's `why_it_matters`. Use the title and snippet/content returned by `web_search`; when you need fuller page text, call `tavily_extract` on the surviving URL. Do not extract anything that did not survive `diff_state.py`. Assign `high`, `medium`, or `low`. If an item is real but noise (a minor patch note, a duplicate announcement of something already digested under another URL, an incidental page match), log it as skipped with a one-line reason instead of digesting it. When in doubt, include it as `low` rather than omitting it silently. ### 5. Write the digest and changelog Write both files before touching state: - `outputs/digest-.md` — per topic: what changed, why it matters (grounded in the topic's `why_it_matters`), source/credibility notes, and source links. If no topic produced anything new, write a short "no changes" digest saying which topics were swept. - `outputs/changelog-.json` — a JSON array of `{topic_id, url, title, significance, summary}` for every digested item (empty array when nothing changed). ### 6. Commit state — only after both outputs exist Pipe the digested items (now including `run_id`) to `commit_state.py`: ```bash cd /sandbox/.openclaw/workspace && \