Where this fits
You're building an agent that helps a product manager compare enterprise SSO providers. The PM asks, "Which one has the best Okta migration reviews?" Your agent has web_search and web_fetch, but if you just let it free-run, it will either summarize the first three search results or hallucinate a comparison. This skill gives your agent a disciplined workflow: define the research target, plan distinct search angles, fetch promising pages, verify claims, and cite sources that actually support the answer.
Why agents benefit
- Bounded research scope – The skill forces explicit criteria (e.g., "best" becomes "highest G2 rating, >50 reviews, migration-specific pain points") before searching, so the agent doesn't chase vague queries.
- Multi-source synthesis – Instead of trusting one page, the agent plans 2–4 distinct search angles, fetches candidates, and cross-checks facts across independent sources.
- Verification loop – Claims are checked against the fetched content; the agent can discard a source that contradicts stronger evidence, reducing hallucinated answers.
- Citation discipline – Every key claim in the final answer links to the fetched page that supports it, making the output auditable and useful for downstream decisions.
- Graceful degradation – If
web_searchorweb_fetchis unavailable, the agent states the limitation instead of pretending to have researched, preserving trust.
Practical scenarios
Competitive feature matrix – A sales engineer asks for a side-by-side comparison of three open-source vector databases. The agent defines evaluation criteria (query latency, indexing speed, license), searches vendor docs and benchmark posts, fetches the top sources, and produces a table with per-cell citations. Fact-checking a press release – A newsroom bot receives a claim about a company's revenue. The skill triggers a verification workflow: search for the official filing, fetch the SEC document, compare the number against analyst coverage, and flag discrepancies with source links. Time-sensitive local research – A travel assistant needs current visa requirements for a client's itinerary. The agent sets a time window ("this month"), searches government and embassy pages, fetches the official PDF, and cites the exact URL — avoiding stale blog advice.Add it to your agent workflow
Install the skill into your agent's skill directory:
npx skills add alfredxw/denova --skill web-research
Then invoke it in your agent's tool-calling logic when the task matches the skill's trigger description. For example, in a simple prompt-based agent:
User: Compare current pricing for AWS Lambda vs Google Cloud Functions for a low-traffic API.
Agent: [web-search "AWS Lambda pricing 2025 per request"] → [web-fetch pricing page] → [web-search "Google Cloud Functions pricing 2025"] → [fetch + verify] → synthesize with citations.
The skill's README (on the OpenAgentSkill directory) provides the full workflow steps — adapt them to your agent's orchestration layer.
Compare before adopting
Before making this a permanent part of your agent's toolkit, evaluate it against alternatives. Check the GitHub repo's commit history and issue activity for maintenance freshness. Compare it with other research-oriented skills like deep-research or a simple custom prompt — does the structured workflow actually reduce hallucination in your eval set? Also assess workflow fit: if your agent rarely needs multi-source synthesis, a lighter approach might suffice. Finally, look at quality signals: star count (currently 644) is a weak proxy — read the skill's tests and examples to see if the logic matches your use case.
Why it is worth tracking
This skill is worth watching because it addresses a core agent failure mode — shallow, uncited research — with a concrete, testable workflow. The repo's 644 stars and active author (alfredxw) suggest community interest, but more importantly, the skill's design (explicit criteria, verification loops, source citation) aligns with the growing demand for trustworthy agent outputs. Track it if you're building research-heavy agents; re-evaluate when the repo adds versioned releases or when your agent's evaluation shows citation quality as a bottleneck. Until then, it's a solid, low-risk addition to your agent's skill library.