id: "skill_7aefc997cd234d20aa9d4045314703c9" name: "by-hypothesis-debate" display-name: "BY Hypothesis-Debate" short-description: "Adversarial multi-agent strategy selection for protein/antibody design campaigns. Spawns competing hypothesis agents (conservative / aggressive / diverse), then ranks proposals with a reflection agent before committing GPU compute. Use when starting a novel-target campaign, when research findings are contradictory, or when multiple design modalities are viable." category: "strategy" keywords: "hypothesis debate, strategy selection, multi-agent, adversarial ranking, design strategy, novel target, BoltzGen, PXDesign, campaign planning" version: "1.0" last-updated: "2026-05-20"

BY Hypothesis-Debate Skill

Picking the wrong design strategy on a novel target wastes a GPU day. This skill forces an explicit, structured debate before any compute is spent: three hypothesis agents propose competing strategies in parallel, then a reflection agent ranks them against a fixed rubric. The winner becomes the campaign config.

The pattern is borrowed from adversarial ML and red-team review — it works because three independently-constrained agents will surface failure modes that a single planner misses, and the reflection step exposes hidden assumptions that would otherwise propagate into the campaign plan.

When to Use This Skill

✅ Use this skill when:

• Starting a campaign against a novel target (0–1 PDB structures, no SAbDab antibodies, <10 papers)
• Research findings have CONTRADICTED confidence (sources disagree on epitope, modality, or scaffold)
• Multiple viable modalities are on the table (VHH vs scFv vs de novo binder) and the user has not pinned one
• The user explicitly says "compare approaches", "explore options", "which strategy would you pick"
• Target difficulty is rated novel or exploratory by by-research
• A previous campaign against the same target failed with hit rate < 5% (this skill helps replan, not retry)

❌ Do NOT use this skill when:

• Target is well-studied with a clear precedent (e.g., TNF-alpha, PD-L1, HER2) → use by-campaign-manager directly
• The user has already specified modality, scaffolds, and tier — debating wastes turns
• Running a preview / feasibility campaign — overhead of 3-agent debate is not worth it for 5–10 designs
• Iterating on a previously-approved campaign (round 2, 3, … reuse the prior strategy)
• The campaign is gated on lab submission — debate occurs upstream of /by:approve-lab, not after

Quick Start

Invoke from the orchestrator (main session) after by-research has written research.md and recommended_hotspots.json to the campaign directory:

python3 scripts/orchestrate_debate.py \
  --research-dir campaigns/IL23R/campaign_20260520_001/research \
  --campaign-config campaigns/IL23R/campaign_20260520_001/campaign_config.yaml \
  --output-dir campaigns/IL23R/campaign_20260520_001/debate

Expected runtime: 4–8 minutes (3 hypothesis agents run in parallel ~3 min each, reflection agent runs sequentially ~2 min). Expected output: debate/proposals/ with three strategy_proposal.json files, debate/ranking.json, and an updated campaign_config.yaml reflecting the winning strategy.

Inputs

Required:

• Research report directory — must contain research.md, recommended_hotspots.json, design_recommendation.json, and validated_findings.json from by-research (8-phase pipeline)
• Campaign config skeleton — a YAML file with target, pdb_id, compute block, but modality, scaffolds, tier are TBD

Alternative inputs:

• `campaign_context.json` — if present (from /by:plan-campaign), the user's preferences seed the agents but do NOT override the debate. Agents see the preference as a prior, not a constraint
• `previous_debate.json` — if rerunning after a failed campaign, prior debate gives historical signal to the reflection agent

Optional:

• Debate budget — number of hypothesis agents (default 3; 2 acceptable for tight token budgets, 4–5 for high-stakes campaigns)
• Reflection temperature — default 0.2 for ranking stability; raise to 0.5 for genuinely novel targets to encourage challenging the rubric
• Tie-break mode — merge (default), aggressive_wins, user_decides

See references/agent-profiles.md for full input specifications including agent-specific directive templates.

Outputs

All outputs land under <campaign_dir>/debate/:

strategy_proposal.json Schema

Every hypothesis agent writes one of these. Validation: scripts/validate_proposal.py.

{
  "agent": "conservative",
  "version": "1.0",
  "target": "IL23R",
  "modality": "VHH",
  "protocol": "nanobody-anything",
  "scaffolds": ["caplacizumab", "ozoralizumab"],
  "tier": "standard",
  "num_designs_per_scaffold": 5000,
  "compute_provider": "local",
  "epitope": {
    "pdb_id": "6WDQ",
    "target_chain": "A",
    "residues": [78, 79, 80, 112, 113, 114, 115],
    "range_notation": "A78-A80,A112-A115"
  },
  "rationale": "Well-validated VHH scaffolds + low-novelty epitope reduces risk. Expected hit rate 20-40% based on prior IL-family campaigns.",
  "expected_hit_rate": "20-40%",
  "expected_wall_time_hours": 8,
  "key_risks": [
    "VHH may sterically clash with N-glycan at N91",
    "ipSAE may underestimate cytokine-receptor interface quality"
  ],
  "mitigation": "Run Protenix 5-seed ensemble validation on top 10 designs",
  "confidence": 0.78,
  "research_source_ids": ["src_001", "src_003", "src_007"]
}

ranking.json Schema

The reflection agent writes one per debate.

{
  "version": "1.0",
  "target": "IL23R",
  "ranked_at": "2026-05-20T14:32:00Z",
  "candidates": [
    {
      "agent": "conservative",
      "scientific_rigor": 0.85,
      "feasibility": 0.90,
      "innovation": 0.40,
      "risk_adjusted_confidence": 0.72,
      "composite_score": 0.7625,
      "rank": 1
    },
    {
      "agent": "diverse",
      "scientific_rigor": 0.70,
      "feasibility": 0.75,
      "innovation": 0.70,
      "risk_adjusted_confidence": 0.65,
      "composite_score": 0.7100,
      "rank": 2
    },
    {
      "agent": "aggressive",
      "scientific_rigor": 0.55,
      "feasibility": 0.50,
      "innovation": 0.95,
      "risk_adjusted_confidence": 0.45,
      "composite_score": 0.6125,
      "rank": 3
    }
  ],
  "winner": "conservative",
  "tie_break_applied": false,
  "rationale": "Conservative strategy wins on feasibility (0.90) and rigor (0.85); Diverse came close but lower research-source coverage.",
  "dissenting_notes": "Aggressive proposal flagged a real concern about VHH glycan clash — fold this into the conservative plan's mitigation.",
  "merged_recommendations": [
    "Add 'cross_check_glycan' flag from aggressive proposal to winning config",
    "Reserve 1500 designs from diverse proposal for alt-epitope exploration in run_002"
  ]
}

For the full schema with field-by-field type constraints, see references/ranking-rubric.md.

Clarification Questions

⚠️ CRITICAL: ASK THIS FIRST — confirm research is complete before debating.

1. Research output present? (ASK THIS FIRST)

• Does <campaign_dir>/research/research.md exist? If not → run by-research first; debate is pointless without target context
• Are there recommended_hotspots.json AND design_recommendation.json? If not → debate has no input
• If validated_findings.json shows zero HIGH confidence findings → tell user; the debate will likely produce low-confidence proposals

1. Has the user pinned a modality?

• If campaign_context.json specifies modality → seed the agents but allow them to challenge it
• If user said "use VHH" in conversation → still run debate; agents may surface a reason VHH is wrong here
• If user said "VHH only, do not consider alternatives" → SKIP debate, go straight to by-campaign-manager

1. What is the compute budget?

• Local GPU available? → all proposals can use compute_provider: local
• HPC (RunPod) only? → agents must size designs against HPC time budget
• Tamarind only? → agents must consider per-design cost in feasibility scoring

1. How novel is the target really?

• 0 PDB, 0 SAbDab, <5 papers → use full 3-agent debate, raise reflection temperature
• 0 PDB, 0 SAbDab, but has homologs with known binders → run debate; the Conservative agent will lean on homologs
• Has PDB but no antibodies → likely doesn't need debate; consider skipping

1. Tie-break preference?

• Default merge — winner gets best ideas folded in from runners-up
• aggressive_wins — when user explicitly wants the highest-novelty option to break ties
• user_decides — escalate to user when top 2 scores differ by < 5%

1. Is this a replan after failure?

• If yes, point to campaigns/<target>/.../previous_debate.json. Reflection agent will downrank strategies similar to the failed one
• Add the failure mode (e.g., "all designs had ipSAE < 0.4") to the orchestration prompt so agents avoid it

1. What is the primary goal of this campaign?

• Hit rate → favors Conservative (well-trodden paths)
• Diversity → favors Diverse (broad epitope/scaffold coverage)
• Novelty / first-in-class → favors Aggressive (high-novelty hypotheses)
• Balanced → reflection rubric weights all four dimensions equally

For decision detail and example transcripts, see references/agent-profiles.md §2.

Standard Workflow

🚨 MANDATORY: USE `scripts/orchestrate_debate.py` — DO NOT INLINE THE TASK() CALLS 🚨

The orchestrator script wraps Task() dispatch, agent prompt construction, JSON validation, and writes the debate log. Inlining the calls breaks the audit trail and skips proposal schema validation.

Step 1: Verify Research Inputs

ls <campaign_dir>/research/
# Must show: research.md, recommended_hotspots.json,
#            design_recommendation.json, validated_findings.json

✅ VERIFICATION: All four files exist and validated_findings.json has at least 1 finding with confidence HIGH or MEDIUM.

❌ DON'T: Start the debate if research is incomplete — agents will hallucinate.

Step 2: Run the Orchestrator

python3 scripts/orchestrate_debate.py \
  --research-dir <campaign_dir>/research \
  --campaign-config <campaign_dir>/campaign_config.yaml \
  --output-dir <campaign_dir>/debate \
  --num-agents 3 \
  --tie-break merge

Expected stdout:

✓ Loaded research from <campaign_dir>/research
✓ Spawned conservative agent (task_id=tsk_001)
✓ Spawned aggressive agent (task_id=tsk_002)
✓ Spawned diverse agent (task_id=tsk_003)
✓ Collected 3/3 proposals
✓ Validated all proposals against schema
✓ Spawned reflection agent (task_id=tsk_004)
✓ Ranking complete: winner=conservative, composite=0.7625
✓ Updated campaign_config.yaml
✓ Debate completed: 7 minutes 12 seconds

Step 3: Validate Proposals (Automatic)

The orchestrator calls scripts/validate_proposal.py on each proposal before passing to the reflection agent. If a proposal fails validation, it is dropped and reported. The reflection agent never sees malformed proposals.

Step 4: Review the Decision

Read <campaign_dir>/debate/decision_summary.md. Present the winner to the user with the dissenting notes from runners-up.

Step 5: Hand Off to by-campaign-manager

Once the user approves the winner, the updated campaign_config.yaml flows directly into by-campaign-manager for execution. No re-planning needed.

Script Failure Hierarchy

1. Fix and Retry (90%) — Missing Python package, stale research files → fix the underlying issue, re-run orchestrate_debate.py
2. Modify Script (5%) — Need to add an agent persona (e.g., safety-engineer) → edit scripts/orchestrate_debate.py, keep the rubric file in sync
3. Use as Reference (4%) — Want a one-off debate with custom prompts → read the script, run the Task() calls manually, write proposals by hand
4. Write from Scratch (1%) — Only if the entire campaign workflow is being replaced. Document the deviation in the campaign log

⚠️ CRITICAL — DO NOT:

• ❌ Skip JSON validation → malformed proposals corrupt the ranking
• ❌ Run agents sequentially → wastes wall-clock time; they must be parallel
• ❌ Edit a proposal after it's written → breaks the audit trail (write a follow-up note in debate_log.jsonl)
• ❌ Let the reflection agent score its own writeups (no recursion)

When Scripts Fail

Decision Points

Tie-Break Protocol (top 2 scores within 5%)

When the composite scores of the top two proposals differ by less than 0.05, the orchestrator applies the configured tie-break mode:

If three proposals are all within 5% of each other (rare, ~3% of debates), the orchestrator escalates regardless of mode — see references/fallback-decisions.md §1.

Conservative vs Aggressive vs Diverse — When Each Wins

When the Debate Itself Is Wrong

If the winning proposal contradicts the validated_findings.json HIGH-confidence findings (e.g., proposes a modality the research explicitly ruled out), the reflection agent must flag this and the orchestrator must escalate to the user. Never silently override the research.

Worked Examples

Example 1: Novel Target — IL23R Cytokine Receptor

Setup:

• 1 PDB (6WDQ), no SAbDab entries, 12 PubMed papers, all signaling-focused
• User asked: "design something against IL23R"
• Research confidence: 3 HIGH (interface residues from 6WDQ), 0 CONTRADICTED

Debate inputs: validated_findings.json flags N91 N-glycan adjacent to the proposed epitope. design_recommendation.json defaults to VHH.

Conservative proposal:

• Modality: VHH, scaffolds: caplacizumab + ozoralizumab, tier: standard, 5000/scaffold
• Composite score: 0.7625 (rigor 0.85, feasibility 0.90, innovation 0.40, risk 0.72)

Aggressive proposal:

• Modality: de novo binder via PXDesign, custom hotspot ring around N91 (avoid glycan)
• Composite score: 0.6125 (rigor 0.55 — relies on AlphaFold-only model, feasibility 0.50, innovation 0.95, risk 0.45)

Diverse proposal:

• Two sub-runs: 3000 VHH + 1500 de novo binder, different epitope faces
• Composite score: 0.7100 (rigor 0.70, feasibility 0.75, innovation 0.70, risk 0.65)

Winner: Conservative. Tie-break not applied (delta 0.0525 > 0.05).

Merged recommendation from runner-up (Diverse): Reserve 1500 designs from the standard budget for an alt-epitope run_002 if run_001 hit rate < 20%.

Dissent kept: Aggressive's glycan flag rolled into the Conservative mitigation plan as cross_check_glycan_clash: true.

Example 2: Multi-Modality Decision — Disordered Target

Setup:

• Intrinsically disordered protein, no PDB structure, 4 NMR papers
• User asked: "compare approaches" (explicit debate trigger)
• Research recommended both VHH and de novo binder, with caveats on both

Conservative proposal:

• VHH using AntiFold against a representative AlphaFold conformer
• Composite: 0.5450 (rigor 0.60, feasibility 0.55, innovation 0.40, risk 0.55) — low rigor; AF model unreliable for IDPs

Aggressive proposal:

• De novo cyclic peptide via PXDesign with conformational ensemble input
• Composite: 0.5800 (rigor 0.50, feasibility 0.55, innovation 0.90, risk 0.40) — high innovation but high variance

Diverse proposal:

• 1500 VHH + 1500 de novo cyclic peptide + 500 linear-binder controls
• Composite: 0.6300 (rigor 0.65, feasibility 0.65, innovation 0.65, risk 0.60)

Tie-break check: Top two (Diverse 0.6300, Aggressive 0.5800) differ by 0.05 exactly. Tie-break threshold is strict less-than-0.05, so NOT applied. Diverse wins outright.

Winner: Diverse — appropriate for a truly novel modality decision. The campaign config is split into three sub-runs with separate logs.

Reflection notes: "All three proposals scored < 0.7. Confidence is low. Run preview tier first (5–10 designs per sub-run), escalate to standard only after validation."

Common Issues

Best Practices

1. 🚨 CRITICAL: Always run by-research to completion before debating. Debate without research is fanfic
2. 🚨 CRITICAL: Never let the orchestrator skip the reflection agent. Three proposals without ranking is worse than one proposal
3. ✅ REQUIRED: Run hypothesis agents in parallel, never sequentially. Sequential dispatch leaks information from agent 1 into agent 3
4. ✅ REQUIRED: Validate every proposal against the schema before reflection. Malformed JSON breaks the ranking
5. ✅ Log every dispatch to debate_log.jsonl. The audit trail is how you debug post-hoc when a campaign goes sideways
6. ✅ Use merge tie-break by default — runners-up almost always have one or two ideas worth keeping
7. ✅ Re-run the debate after a failed campaign. Pass the failure mode in the orchestration prompt; reflection downranks repeats
8. ✅ Keep the Aggressive agent honest — its proposals are the highest-variance, so the reflection rubric must penalize unjustified novelty
9. ✨ Optional: Raise reflection temperature to 0.4–0.5 for truly novel targets; default 0.2 favors well-established choices
10. ❌ DON'T: Inline Task() calls in the main session. Always go through orchestrate_debate.py for the audit trail
11. ❌ DON'T: Let the user veto the debate output without recording the override in decision_summary.md. Decisions outside the rubric should be traceable

Suggested Next Steps

After the debate produces a winning strategy:

• Run `by-campaign-manager` — the updated campaign_config.yaml is now ready for sizing, tier confirmation, and launch. The debate's merged_recommendations flow into the campaign plan
• Run `by-design-workflow` if the user wants full orchestration through to design + screening
• Run `by-hypothesis-debate` again (with --reuse-research) only if the user wants to revisit the decision; debates are not free, so this is a deliberate choice
• Run `by-failure-diagnosis` if a downstream campaign using this debate's winner fails — pass the debate output in so the diagnosis knows what was rejected and why

Chaining rationale: debate writes the strategy; campaign-manager executes it. Keeping these separate means the debate output is reusable (you can switch the executor — local vs HPC — without rerunning the debate).

Related Skills

Upstream (run before):

• by-research — produces the research artifacts the debate consumes
• by-epitope-analysis — deeper epitope characterization if the research left gaps

Downstream (run after):

• by-campaign-manager — executes the winning strategy
• by-design-workflow — end-to-end orchestration including this debate
• by-failure-diagnosis — diagnoses why a debate-selected strategy failed

Alternative / Complementary:

• by-campaign-optimizer — active-learning loop that adjusts strategy within a campaign (not before)
• by-knowledge — queried by hypothesis agents for prior campaign evidence

References

Detailed Documentation

• references/agent-profiles.md — Full directive templates for Conservative, Aggressive, and Diverse hypothesis agents. Includes what each agent is told to weight, what to ignore, and the exact system-prompt skeleton
• references/ranking-rubric.md — Scoring formulas, weight configuration, calibration examples, and how the reflection agent justifies each axis score
• references/fallback-decisions.md — Decision trees for three failure modes: three-way ties, universally low scores, and reflection agent crashes
• references/example_debate_output.json — Complete fixture of a debate against IL23R: 3 proposals + ranking, suitable for testing and onboarding

Scripts

• scripts/orchestrate_debate.py — CLI orchestrator: reads research + campaign config, spawns 3 hypothesis tasks in parallel, collects proposals, invokes reflection, writes the winner into campaign_config.yaml
• scripts/validate_proposal.py — CLI validator: checks a proposal JSON against the schema documented in this SKILL.md. Exits non-zero on schema violation with a precise error message

Official Documentation

• Anthropic Claude Code Task() API — for the agent dispatch pattern
• templates/CLAUDE.md — overall BY orchestration guide (agent delegation protocol, model resolution)

Related Methodologies

• Adversarial debate as decision procedure: Irving, Christiano, Amodei (2018) AI safety via debate
• Red-team review for protein design strategy selection (internal BY practice; not yet published)

License: All scripts in this skill are LGPL-compatible and permit commercial use. No proprietary algorithms or datasets are embedded.