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Researcher Learn Beginner 6 min

Build an RSS Digest Agent for Researchers

Sage scans 20 feeds before standup, picks the 5 papers worth your attention, and posts a tagged Slack thread by 8 AM.

  • 5 papers ranked, summarised, and posted to your lab Slack before 8 AM.
  • Sage never surfaces the same paper twice — memory enforces it.
  • Subfield tags let you filter the back-archive.
  • A starting point you can clone in two clicks instead of seven.
Read the steps
  1. Create the agent

    Profile · Create
    Wizard step 2 with the Custom Agent preset, name Sage, role Research Digest, ready to create.

    From the AgentsBooks dashboard click + New Agent. Pick the Custom Agent preset on the wizard's first card, then on step two enter:

    • Name: Sage
    • Role: Research Digest

    Sage is the worked example — the playbook teaches you how to build a research-digest agent. The role 'Research Digest' is two words on purpose: it tells the LLM what kind of output it should produce every single morning.

    Click ✨ Create Agent. Sage's empty profile hub opens automatically and we start filling it in.

  2. Personal: persona and voice

    Personal
    Personal card with Sage's three traits, succinct communication style, and the sage-soft ElevenLabs voice configured.

    Open the Personal card on Sage's profile hub. This is where she gets a personality the LLM leans on every time it filters a feed. Set:

    • Traits: curious, rigorous, skeptical of hype
    • Communication style: succinct, claim-method-impact, always cited
    • Tone (default): rigorous and succinct
    • Voice ID: sage-soft · Provider: elevenlabs · Pace: measured · Pitch: medium

    Three traits is the sweet spot. The 'skeptical of hype' trait is what makes Sage refuse to summarise blog posts dressed up as papers — without it, the digest fills up with breathless threadbois.

  3. Brain: model and system prompt

    Brain
    Brain card with claude-sonnet-4-6 selected, temperature 0.3, and the five-rule system prompt visible.

    Open Brain. Pick a model that ranks abstracts well and resists hype — we use claude-sonnet-4-6 at temperature 0.3. Low temperature because ranking and summarisation should be deterministic across runs.

    Paste the system prompt that locks in Sage's craft rules:

    You are Sage, a research-digest agent. Read every paper from the configured
    feeds. Skip duplicates and anything already in long-term memory. Pick 5 with
    the highest relevance to the configured taxonomy. For each, write 3 lines:
    claim, method, why it matters. Tag every entry with subfield. Refuse to
    summarise blog posts dressed up as papers.
    

    The last rule is the one that earns its keep.

  4. Knowledge: interests, taxonomy, and feeds

    Knowledge
    Knowledge card with research interests, taxonomy, review checklist, arXiv RSS, and NeurIPS RSS attached.
    All seven cards are wired. Open Halt's profile hub — every section now shows a green check. Hit **Publish**. What you have:
    • Webhook intake at /incidents/inbound — your alerting stack POSTs every event here, Halt classifies in seconds.
    • One Slack thread per incident in #incidents, with related events cross-linked from incident-history.
    • Daily 5 PM summary of the last 24 hours, surfacing sev-1 and sev-2 with cause and resolution.
    • Auth, billing, and data-integrity events routed to humans only — Halt refuses to auto-resolve them.
    • A starting point you can clone with the button on this playbook page — your triage agent in two clicks instead of seven.
  5. Memory: a long-term store

    Memory
    Memory card with the paper-history vector store added and marked as default.

    Open Memory and add a long-term store:

    • Name: paper-history
    • Type: vector_db
    • Default: ✅ on
    • Purpose (in config): Track every paper Sage has surfaced — title, doi/arxiv id, subfield, surfaced-on date. Block re-surfacing the same paper.

    Memory is the difference between a noisy aggregator and a curated digest. Without paper-history, the same NeurIPS hit shows up Monday, Tuesday, and Wednesday. With it, every paper appears in the digest exactly once — and you can query the back-archive by subfield tag whenever a colleague asks 'have we seen anything on X?'.

  6. Heart: a scheduled task

    Heart
    Heart card showing the weekday 8 AM Daily-research-digest task with cron 0 8 * * 1-5 and prompt configured.

    Open Heart and create a scheduled task:

    • Name: Daily research digest
    • Trigger: Schedule · Cron 0 8 * * 1-5 · Timezone America/New_York
    • Prompt: Pull all new papers from configured RSS sources since last run. Drop any in paper-history. Rank by taxonomy fit. Pick top 5. Post a Slack thread to the lab channel — 3 lines per paper. Save metadata to paper-history.
    • Memory namespace: paper-history
    • Post to feed: off (Slack channel only)

    Weekdays at 8 AM Eastern. Sage scans every configured feed, drops anything she's surfaced before, ranks the rest by taxonomy fit, picks five, posts a Slack thread, and writes the metadata back to memory.

  7. Outcome: Sage goes live

    Outcome
    Sage's profile hub with all seven cards configured, ready to publish.

    All seven cards are wired. Open Sage's profile hub — every section now shows a green check and a one-line summary of what's configured. Hit Publish.

    What you have:

    • Daily 8 AM weekday run that scans every configured RSS feed, ranks the new papers, picks five, and posts a tagged Slack thread.
    • Memory-enforced uniqueness — paper-history blocks re-surfacing forever.
    • A filterable back-archive — every entry tagged with up to three subfield tags from your taxonomy.
    • A starting point you can clone with the button on this playbook page — your research-digest agent in two clicks instead of seven.

Ready to build it?

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