/research-swarm — an argument map for any hypothesis (a swarm of research agents)

🧒 When reporting to a non-technical operator: end with a child-simple "In plain words" recap in their language. ⚖️ Doctrine: this skill IS the operational arm of protocol-epistemic-neutrality-fringe-research. NEVER judge or mock the hypothesis. Consensus ≠ truth. Build a map of arguments + confidence — an investigation, not a verdict. Bounded by operating-agreement safety invariants (no illegal/harm/bypass/fraud).

When to use

The operator hands a hypothesis — frontier science (antigravity, perpetual motion, anomalous materials, radical longevity, models of consciousness/time), an unusual business model (DAO/network states, no-employee AI co, token labor markets), or a controversial/stigmatized vertical (adult, gaming, gambling). Goal: a balanced, grounded argument map, not a yes/no.

The 5 lenses (run as a parallel swarm)

1. Consensus — steelman the current mainstream/established view. What does well-replicated science / the market actually hold, and why.
2. Skeptic — strongest arguments AGAINST. Where the idea breaks, hidden assumptions, falsification points, known failure modes.
3. Frontier — strongest arguments FOR. Steelman the believer: what would have to be true, adjacent emerging evidence, why it's not obviously impossible.
4. Historian — precedents and base rates. Ideas once dismissed then vindicated (and once hyped then debunked); the historical analog for THIS idea.
5. Experimental-Design — what measurement/experiment would confirm or refute, the cheapest decisive test, what data to gather first.

Each lens does light, targeted web grounding (WebSearch/WebFetch) — claims should be evidenced, not just model priors — but stays token-aware. The synthesis NEVER returns a verdict; it returns a confidence tier + open questions.

Control node — Verifier-Calibrator (added 2026-06-15, via Alpha R+DR)

After synthesis, one Verifier-Calibrator node runs (the swarm's acceptance function, not another opinion). It (1) audits the strongest for/against claims — supported / partial / unsupported, (2) re-derives the confidence tier from evidence (support density + cross-lens agreement), not vibe, lowering tiers that rest on unsupported or single-lens claims, and (3) may abstain: the new tier insufficient is a first-class honest answer when evidence is too thin to place the idea on the established→fringe axis. The calibrated tier + claim audit + flags surface in the note and dashboard. This is the DR-recommended control plane over the 5-lens content plane (gaps: no verifier / qualitative-not-calibrated tiers / no abstain lane). Decision: vault 02-Decisions\decision-research-swarm-v2-upgrade.

How to run

Step 0 — parse the hypothesis from the operator's message (everything after the trigger). If it's a vague one-liner, ask ONE clarifying question first (scope/domain), else proceed.

Step 1 — RECALL first (token law — cheap before LLM/web). Pull what the operator already thinks on the topic:

python "$IMPORTS_ROOT/brain_ask.py" "<hypothesis>" --k 8

Keep the synthesized recall as recall_context (the operator's prior notes/leanings on the topic — feeds every lens so the swarm builds on their own thinking, not from zero).

Step 2 — run the swarm via the Workflow tool (this skill IS your explicit opt-in to orchestrate):

Workflow({ scriptPath: "%USERPROFILE%\.claude\\skills\\research-swarm\\workflow.js",
           args: { hypothesis: "<hypothesis>", recall_context: "<from step 1>" } })

The script fans out the 5 lenses (parallel barrier — synthesis needs all), a synthesis agent merges them, then the Verifier-Calibrator audits the strongest claims and calibrates the tier (or abstains → insufficient). Returns the structured argument map object with a calibration block.

Step 3 — persist (backup first). python "$IMPORTS_ROOT/vault_backup.py" then:

• Write the map as a vault note in 03-Insights\Research-Swarm\<YYYY-MM-DD>-<slug>.md (frontmatter type: insight, origin: anton, tags: [research-swarm, argument-map]; body = for/against tables, confidence tier, historical analogs, open questions, the decisive experiment). Link it to [[protocol-epistemic-neutrality-fringe-research]] + [[insight-glavnaya-tsel-tsifrovoj-dvojnik]] and any concept the topic touches (no-orphan: ≥1 inbound).
• Dump the map object to <skill>\last_map.json and build the visual:

python "$USERPROFILE/.claude/skills/research-swarm/build_argmap_dashboard.py" <map.json> <out.html>

Dashboard → $OBSIDIAN_VAULT/_Dashboards/Research-Swarm/<slug>.html. Offer to open it (the operator works by eye).

Step 4 — reindex so the new note is searchable: python "$IMPORTS_ROOT/brain_embed_update.py".

Step 5 — report to the operator: the confidence tier, the 2-3 strongest for/against, the cheapest decisive experiment, link to the dashboard — and the ELI5 recap. NEVER editorialize a verdict; present the map.

Scaling (AK-47)

Default = 5 lenses, single pass. For a big/important hypothesis the operator can say "go deeper" → add a second round per lens or 3-vote adversarial verification of the strongest claims (loop-until-dry). Don't over-build by default.

Relation

Operational arm of vault protocol-epistemic-neutrality-fringe-research · memory [[epistemic-neutrality]]. Pairs with /alfa-search-recall-deepresearch (R+DR — recall→gap→deep-research→synthesis; the swarm is the multi-lens synthesis engine). Distinct from /ask (single recall) and /deep-research (web fan-out, single perspective). Serves [[main-goals]] goal #1 — the twin reasons like the operator-the-researcher.

Like this skill? It is one of 100 in second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.

Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).

Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_

Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.