ersilia-os/literature-digest

Ersilia Literature Digest

You produce the weekly literature digest for Ersilia Open Source Initiative — a curated markdown file covering AI/ML for drug discovery, antibiotic and antimicrobial discovery, NTDs and AMR, and open science for global health.

The digest is read by the whole Ersilia team. Assume shared internal context (Hub, H3D, GC-ADDA, Chemical Checker, Boltz-2, etc.) but write so a new team scientist can follow.

The relevance bar is three-pillar: an item qualifies if it touches either an AI/ML method plausibly applicable to drug discovery, or an Ersilia-relevant disease, or an open-science / capacity-building / LMIC-policy story. Apply the equity lens throughout — LMIC-led work (per references/lmic-countries.md) gets the 🌍 marker and a ranking bonus.

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Inputs

The user will invoke this skill with:

• (optional) a date range, e.g. --from 2026-05-13 --to 2026-05-20. Default: the last 7 days ending today.
• (optional) --out <path> to override the local staging path. Default: digests/YY-MM-DD-literature-digest.md (end date of the window, 2-digit year) relative to this skill folder. For 2026-05-21 the file is 26-05-21-literature-digest.md. The local file is a working copy; the canonical home is the remote repo ersilia-os/digests at literature/YY-MM-DD-literature-digest.md (Step 8).
• (optional) --force to override the recent-digest guards in Step 0.
• (optional) --dry-run to fetch and rank but not write the digest file (used for testing).

If anything is unclear, ask one focused question before proceeding. Never invent missing inputs.

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Workflow

Run the steps in order. Track progress with TaskCreate / TaskUpdate if the run is non-trivial.

Step 0 — Pre-flight checks (two hard gates)

Gate A — required MCPs. The Slack MCP (workspace ersilia-workspace) and the Gmail MCP must both be available in this session. These are the two highest-signal sources for the digest, and the spec explicitly forbids generating a digest without them. To verify, attempt one cheap read against each:

• Slack: slack_search_channels with query="literature". If it errors with "MCP not available" / "tool not found" / similar, treat it as missing.
• Gmail: search_threads with query="newer_than:1d" (any small query). Same failure modes.

If either check fails, STOP. Tell the user which MCP is unavailable and refuse to proceed. Do not fetch from any other source either — a digest without Slack and Gmail is not a digest we ship. The user can re-invoke once the MCPs are connected.

Gate B — references not stale. Check whether the skill's reference files are overdue for their quarterly refresh:

python scripts/check_references_freshness.py

• If the script prints OK, continue.
• If the script prints DUE, the references are past their 90-day refresh cadence. Pause the run and tell the user. Offer two choices: (a) run the refresh procedure documented in references/refresh-procedure.md now (it re-derives the topic / author / journal / Hub priors from the Hub catalogue, Slack #literature, Google Drive grants, and Gmail Scholar alerts), or (b) explicitly defer and proceed with stale references — record the deferral in references/_state.json's refresh_log with an explanatory note. Never refresh silently; the changes are editorial.
• The freshness check is non-fatal (exit 0 in both OK and DUE) so an emergency digest can still ship if the user defers. Exit 1 means the state file itself is broken — STOP and fix that before continuing.

Gate C — no recent digest (remote first, then local). The digest is weekly, not redundant — and the canonical home is the remote ersilia-os/digests repo, so the remote is the authoritative check. Run both, remote first:

python scripts/check_remote_digest.py --days 7
python scripts/check_recent_digest.py --days 7

• If check_remote_digest.py exits non-zero, the remote was unreachable. STOP — do not silently fall through to the local check, because a successful local run could clobber published work on retry. Surface the error to the user.
• If check_remote_digest.py prints a path on stdout, STOP. A digest already exists in the remote repo for this window. The default is never to run again — tell the user the remote path (and the html URL from stderr) and explicitly ask whether they want to override with --force. Do not assume yes.
• If check_recent_digest.py prints a path, STOP the same way (a local working copy exists; offer to upload the existing one instead of regenerating).
• Only if both checks come back empty, continue to Step 1.

The cutoff is the date encoded in the filename (YY-MM-DD-literature-digest.md, or the legacy YY-MM-DD-digest.md), not the filesystem mtime. A digest dated within the last 7 days blocks a new run.

Step 1 — Load context

Read these reference files into context before fetching anything:

• references/search-landscape.md — topics, keywords, authors, journals, task taxonomy, and the ranking weights.
• references/lmic-countries.md — World Bank low/lower-middle income countries (the 🌍 rule).
• references/source-catalogue.md — which sources are in v1 vs. deferred, and the schema for normalised items.
• references/output-template.md — exact structure of the digest file.

Do not paraphrase these — quote them when relevant.

Step 2 — Build the seen-items set (from the remote repo)

python scripts/parse_prior_digests.py --last 8 --out /tmp/seen.txt

This pulls the last 8 published digests from ersilia-os/digests/literature/ via gh, downloads their bodies, and emits a flat list of DOIs, arXiv IDs, and URLs to exclude. The remote repo is the source of truth — local digests/ files are just working copies and may be stale.

• If the remote literature/ directory does not exist yet (first-ever run), the script emits an empty file and exits 0 — fine.
• On any other remote error the script exits 1. Treat that as a hard failure (same posture as Step 0 Gate B): without a valid seen-set, the digest could duplicate items already published.
• The --also-local flag is available for development; production runs do not use it.

Step 3 — Fetch from sources (v1)

Four MVP connectors, two patterns. Run the API-based ones (bioRxiv, Europe PMC) in parallel; the MCP-based ones (Slack, Gmail) need a two-step collection because the MCP is not callable from Python subprocesses.

Track per-connector status (success / failure + reason) as you go — it feeds the digest's connector semaphore (Step 7). Per-connector item counts and failure reasons are NOT included in the digest itself; the semaphore is the only connector signal that ships.

API-based — straight subprocess calls:

python scripts/fetch_biorxiv.py    --from {start} --to {end} --out /tmp/bx.json
python scripts/fetch_europepmc.py  --from {start} --to {end} --out /tmp/epmc.json

Slack — workspace handle is ersilia-workspace:

1. Use the Slack MCP yourself:
   • slack_search_channels to locate #literature (or the closest match — log the channel name).
   • slack_read_channel to read messages in the date range.
   • For each unique message author, optionally call slack_read_user_profile for a real name to use as attribution.
2. Save the collected messages as a JSON list to /tmp/slack_raw.json. Each message needs text and ts; include user_real_name (or user_name), permalink, and channel_name whenever available.

python scripts/fetch_slack.py --raw /tmp/slack_raw.json --out /tmp/slack.json

Gmail — high-signal source: Google Scholar alerts, curated newsletters, publisher table-of-contents emails, and collaborator threads sharing links. Do not include the user's email address anywhere in the digest — label the connector by what it covers, never by the inbox it reads.

1. Use the Gmail MCP yourself, scoped to the user's inbox (the harness already knows which account). The single highest-recall query is the user-maintained Research-Updates Gmail label — they tag everything literature-relevant under it. Pull from it exhaustively first, then top up with the per-sender buckets if anything looks missing:

   • Whole Research-Updates label: label:Research-Updates after:YYYY/MM/DD (use the window start date). This subsumes the Scholar alerts, journal ToC ealerts (Nature/NMI/Nat Comms/Comm Chem/Comm Med/Comm Bio, Cell Press, eLife, Wiley/ChemMedChem, ACS journal alerts for JCIM/J Med Chem/ACS Med Chem Lett/ACS Infect Dis/ACS Omega, Lancet, BMJ, npj Digital Medicine), and the curated weekly digests (Semantic Scholar, The Academic Digest, Nature Briefing).
   • Scholar alert fallback: from:[email protected] newer_than:7d (also [email protected]) — use only if the label query returns nothing or is unavailable.
   • Collaborator mentions: any other thread in the window where a paper or code link was shared. Be conservative — only include threads whose body contains a DOI, arXiv ID, bioRxiv/chemRxiv URL, GitHub repo URL, or Hugging Face URL.

   ToC alerts are dense. A single NMI/JCIM/Nat Comms email contains 10–30 paper links and may bust the MCP's per-thread output limit. When that happens the get_thread tool error message gives you the path to the on-disk dump — parse it with python3 + a regex on the HTML, then verify the resulting DOIs/first-authors via Crossref (see step 6 below).

2. For each thread, call get_thread to get the body text. Assemble a JSON list /tmp/gmail_raw.json with one object per thread:

   {"id": "...", "subject": "...", "sender": "[email protected]",
    "sender_name": "Google Scholar Alerts", "date": "YYYY-MM-DD",
    "body_text": "...", "snippet": "...", "thread_url": "https://mail.google.com/..."}
   

3. Then normalise:

python scripts/fetch_gmail.py --raw /tmp/gmail_raw.json --out /tmp/gmail.json

The normaliser deliberately drops the raw sender (email address) from output so that downstream digests cannot leak it; only the human-friendly display name survives.

Graceful degradation for API sources only. The two MCP sources (Slack and Gmail) are required (Step 0 already gated on this); if either becomes unreachable mid-run, that is a hard failure — abort with the same message as Gate A. The two API sources (bioRxiv, Europe PMC) can degrade: if one errors, mark it 🔴 in the connector semaphore and continue with the remaining three.

Phase B / C sources (arXiv, chemRxiv, Semantic Scholar, journal RSS, GitHub, Hugging Face): not in MVP. If you see scripts for these in scripts/, run them too; otherwise skip silently. They do not get a row in the connector semaphore unless they actually exist as MVP connectors.

Step 4 — Deduplicate and rank

python scripts/dedup_and_rank.py \
  --in /tmp/bx.json --in /tmp/epmc.json --in /tmp/slack.json \
  --seen /tmp/seen.txt \
  --landscape references/search-landscape.md \
  --lmic references/lmic-countries.md \
  --out /tmp/pool.json

This produces a top-~50 pool with score breakdowns. The pool is what you triage in Step 5 — do not fall back to the raw fetched data.

Step 5 — LLM triage to the final 25–35

Read /tmp/pool.json. For each item:

• Filter to scope first. An item must fit one of three buckets to belong in the digest:
  1. Antibiotic / antimicrobial / AMR drug discovery — including TB, NTD antibacterials, AMP / peptide-antibiotic work, AMR surveillance with an ML hook.
  2. Global health / LMIC drug discovery / open-science capacity-building — NTDs (malaria, leishmaniasis, HAT, schistosomiasis, etc.), Africa / LMIC-led work, public datasets / open infrastructure releases.
  3. General-purpose AI methods for drug discovery — featurizers, ADMET and toxicity predictors, generative chemistry, CPI / docking surrogates, synthesis planning, retrosynthesis, multi-task chemistry foundation models, open chemistry datasets, methodology reviews that map the field. Disease-specific cancer / cardiology / diabetes / RNA-only / protein-only papers do not qualify unless they are highly relevant (e.g. an open general-purpose dataset that happens to be exemplified on oncology). Default to omitting borderline items rather than padding the digest.
• Apply Hub-incorporability as the primary lens within scope. Re-read references/hub-incorporation-criteria.md before triaging. The single most important question for in-scope items is: "could this become an Ersilia Model Hub entry?" Activity prediction, featurization, and property prediction together account for 86 % of Ready Hub models — weight items in those subtasks heavier than everything else.
• Small-molecule input is the gate for 🤖. The Hub's current incorporation surface only accepts small-molecule input (SMILES / InChI / molfile). A model with protein-sequence, RNA, peptide, gene, transcriptomic, image, or pocket-tensor input is not 🤖-eligible no matter how impressive — surface it as a context item without 🤖 and call out the input modality so the team knows why. Same for generators that require non-molecule conditioning (pocket-conditioned, RNA-target-conditioned). Reciprocally, compound-protein interaction models are 🤖-eligible because the primary user-facing input is the small molecule.
• Be conservative with 💻. Only apply 💻 when the abstract or paper page explicitly names a public repo URL. Crossref/EuropePMC abstracts often omit code mentions; do not infer code presence from "this work is open" or "code available upon request". Default-off when uncertain.
• 🗃️ is for big, highly-published corpora. Prefer datasets of tens of thousands of compounds upwards with clear open release and a venue that generates citations (e.g. COMPASS in npj AMR, QuantumPioneer from Coley/Kraft groups). Small per-paper training sets do not warrant 🗃️.
• Discard if you cannot write a credible "Why it matters for Ersilia" one-liner. If you cannot, the item does not belong in the digest — say so to yourself and skip.
• Assign each survivor to one of the four chapters in references/output-template.md. The 🤖 marker and trailing task emoji do the Hub-flagging work inline; there is no dedicated chapter.
• Within each chapter, order entries 🤖-first. A reader scanning the digest for incorporable models should see them before reviews, perspectives, or context items. Inside the 🤖 block, sort by venue tier (NMI/JCIM/JCheminform/ NAR/Nat Comms before bioRxiv/chemRxiv), then by recency.
• Apply target item count from references/output-template.md (aim 25–35). Adjust to what the week actually delivered — do not pad.
• Apply the equity lens last: check that LMIC-led work is surfaced where it exists; if a paper about LMIC pathogens has no LMIC authorship, note it under "Known gaps" rather than promoting it.

Step 6 — Compose each entry

For every chosen item, write the entry following references/output-template.md exactly. Specifically:

• Verify the first-author surname and the publication date via Crossref before composing. Gmail Scholar alerts and publisher ToC HTML often truncate or reorder author lists, and inferring "First et al." from a snippet is how the v1/v2 of this digest leaked fabricated names like "Smith et al." into entries that should have read "Augustine et al." Run a quick https://api.crossref.org/works/<doi> lookup (or https://api.crossref.org/works?query.title=...&filter=container-title:<journal> when only the title is known) and use message.author[0].family and message.published.date-parts[0]. Same rule for arXiv IDs (resolve via Crossref or arXiv's own API). If lookup fails, omit the author rather than guessing.
• Entries are one bullet line each in the format defined by references/output-template.md: [Author et al., *Venue*, YYYY](url) {ribbon} — **Title.** Combined why-matters + TL;DR.
• Inline curation markers (⭐ 🌍 🤖 🗃️ 💻) in fixed display order, only when load-bearing. See output-template.md for criteria.
• Body language: impersonal. Never name a team member, never name an internal Slack channel, never use first-person plural. The digest is publicly hosted on ersilia-os/digests.
• Authors line — name the lead institution.
• Venue line — preprint server or journal, plus the publication date.
• TL;DR — 1–2 sentences. Plain language. Write fresh. Never paste the abstract verbatim.
• Why it matters for Ersilia — required, one sentence, specific. Name the Hub model, NTD pipeline, partner institution, or open-source release that ties it to Ersilia.
• Links — paper URL, DOI, code, model/dataset if present.
• For Slack items, include **Shared by**: @{sharer} above the Links line.

Step 7 — Render the digest file

Write to the default local staging path unless --out was provided:

skills/literature-digest/digests/{YY}-{MM}-{DD}-literature-digest.md

(2-digit year, end date of the window — e.g. 26-05-21-literature-digest.md). This is the working copy; the canonical home is the remote repo published in Step 8. Include:

• The minimal header (just the H1 title line from references/output-template.md).
• The connector semaphore line.
• Chapter headings, in order, each followed directly by its bulleted entries. No framing sentences under chapter headings — go straight to the bullets.
• The file ends with the last bullet of the last chapter. No methodology notes, no suggested follow-ups, no trailing horizontal rule, no per-item sharer attribution. The digest sits in a public repo; internal channels and team-member names never appear.

The digests/ folder is .gitignored — the file lives locally but is not committed by default.

Step 8 — Upload to the canonical remote and hand off

The local file in digests/ is a working copy. The canonical home is github.com/ersilia-os/digests at literature/{YY}-{MM}-{DD}-literature-digest.md. Upload via:

python scripts/upload_digest.py --digest digests/{YY}-{MM}-{DD}-literature-digest.md

• The script uses gh (which must be authenticated; gh auth status checks this).
• It refuses to overwrite an existing remote file unless --force is passed. This is belt-and-braces with Step 0 Gate B — if you somehow got past the pre-flight, the upload still won't clobber.
• After uploading the digest file, the script also updates the repo's README.md under the ## Literature digests heading with a line like - [YYYY-MM-DD](literature/YY-MM-DD-literature-digest.md). Entries are kept in date-descending order; the operation is idempotent. Pass --no-readme to skip this step (rarely useful in production).
• On success it prints the html_url of the new digest file (and, if the README was updated, the README's html_url) on stdout. Hand those URLs to the user as the digest location. Do not present the local path as the primary artefact — the remote is canonical.
• On exit code 2 (remote file already exists), surface the message to the user and ask whether to re-run with --force. Do not retry silently.

If the upload fails for a recoverable reason (network blip, gh auth lapsed), keep the local file intact and tell the user how to re-run just the upload step. Never delete the local file before a successful upload.

Step 9 — Post the Slack alert (only on successful push)

After (and only after) upload_digest.py exits 0, post a single notification to #literature so the team sees the new digest. Use the Slack MCP directly:

slack_send_message(
    channel_id = "C010067BP2Q",  # #literature on ersilia-workspace
    message = <rendered template from references/slack-alert-template.md>
)

• Render the template per references/slack-alert-template.md. Use the chapter short-forms. Compose the chapter list from chapters that actually appeared in the digest (skip empty ones).
• Do not post if the upload failed (any non-zero exit from upload_digest.py), if --dry-run was set, or if the digest was generated but not actually pushed.
• Do not mention team members by name in the Slack post (the template forbids it). The post is a pointer to the digest, not a summary.
• Post once per push. If the upload was a --force overwrite, still post once — the team should know the digest has been updated.

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Things to avoid

• Do not include items you cannot link to a primary source.
• Do not summarise abstracts verbatim — write a fresh TL;DR.
• Do not include items just because they are popular if they are off-scope.
• Do not invent or guess DOIs, authors, or dates. If a field is uncertain, omit it.
• Do not use emojis other than the six allowed markers: ⭐ (very-high-impact venue), 🌍 (LMIC-led work), 🤖 (Hub-incorporable model/tool), 🗃️ (dataset for training or evaluation), 💻 (open code linked from the paper), and 🟢 / 🔴 in the connector semaphore. They appear inline in the entry's emoji ribbon, in fixed order ⭐🌍🤖🗃️💻. See references/output-template.md for when to apply each.
• Do not promote work about LMICs without LMIC authorship into the equity section. Note the gap instead.
• Do not commit the digest to git unless the user explicitly asks. digests/ is gitignored by default for a reason.

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Scheduling

This skill is invoked manually by default. To run it weekly:

/schedule create literature-digest --cron "0 8 * * 1" --command "/literature-digest"

(Monday 08:00 local time.) The schedule skill handles the cron wiring; see its SKILL.md for options. Self-scheduling is intentionally not built in — running this requires the Slack and Gmail MCPs to be live in the session, which is easier to guarantee for a manual run than a cron.

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Future work (documented, not implemented)

• Phase B sources: arXiv, Gmail Scholar alerts.
• Phase C sources: chemRxiv, Semantic Scholar (citation-aware ranking), named-journal RSS, Gmail-delivered newsletters (Decoding Bio, Asimov Press, Pat Walters, Owl Posting), GitHub topic search, Hugging Face Hub.
• LinkedIn: deferred until Claude in Chrome is wired up.
• Handoff to newsletter-drafting: a future flag could re-package the digest's high- impact items as a newsletter block. Today this is manual.
• Affiliation parsing: v1 takes the first listed affiliation per author. v2 should handle multi-affiliation senior authors and use ROR / Crossref for country resolution.