Agentic Researcher

Agentic Researcher — The Evolver

Generate candidate answers to open-ended questions, evaluate them against criteria, keep the best, mutate, and iterate. Where the spelunker's other tools verify claims (is X true?), this skill creates and refines solutions (what's the best X?).

Inspired by the generate → evaluate → select → mutate loop that AlphaEvolve uses for algorithm discovery, adapted for general research questions where multiple valid answers exist.

When to Use (vs. Standard Spelunker Pipeline)

Key signal: If the answer must be constructed rather than found, use this skill.

Guiding Principles

1. Never present one option as if it's the only option. Show the landscape.
2. Every recommendation needs a trade-off map. "Best" is relative to criteria.
3. Evaluate before you advocate. Generate candidates first, then evaluate. Don't start with a conclusion.
4. Use the confidence framework. Recommendations carry confidence tags per ../spelunker/references/confidence-framework.md.
5. Iterate only when it improves signal. Stop when additional rounds produce marginal gains.

How to Run

Input

From the spelunker orchestrator:

• The generative question (restated)
• The detected domain
• Depth mode (quick / standard / deep)
• User constraints (budget, timeline, technology, values)

Phase 1 — Frame the Solution Space

Define what "good" means before generating candidates:

1. Extract evaluation criteria from the question and constraints. Every generative question implies criteria even if unstated.
2. Weight the criteria. Hard requirements (must-have, pass/fail) vs. soft preferences (weighted).
3. Define the evaluation method per criterion: measurable (numeric), comparable (ordinal), or judgmental (reasoned assessment).
4. Map the candidate space dimensions to ensure diversity in Phase 2.

See references/evaluation-methods.md for scoring scales, weighting defaults, and trade-off visualization formats.

Phase 2 — Generate Initial Candidates

Create 3-5 diverse candidates spanning the solution space. Use generation strategies: established options, analogical transfer, constraint inversion, extreme positions, and hybrids. See references/evaluation-methods.md for strategy details.

For each candidate, produce:

• A clear description (1-3 sentences)
• Which solution space dimensions it occupies
• Hypothesized strengths and weaknesses
• What evidence would confirm or undermine it

Research grounding: Quick-search each candidate to verify it's real. Use source-triangulator at quick depth if needed. Flag or replace candidates based on nonexistent technology or debunked approaches.

Phase 3 — Evaluate Candidates

Score each candidate against the rubric from Phase 1:

1. Evidence gathering — For each candidate × criterion, search for evidence using source-triangulator standards. Tag evidence quality: Strong / Weak / None.
2. Scoring — Measurable (data + citation), comparable (relative rank + evidence), judgmental (reasoned + flagged as judgment).
3. Build the evaluation matrix — candidates as rows, weighted criteria as columns.
4. Identify trade-offs — Where does each candidate excel vs. sacrifice?

Phase 4 — Select and Mutate (Evolutionary Loop)

Skip in quick mode. Quick mode stops after Phase 3.

1. Select top 2-3 candidates.
2. Mutate — Generate 1-2 variants per selected candidate: repair weaknesses, import strengths from others, tighten constraints. See references/evaluation-methods.md for mutation operators and convergence detection.
3. Re-evaluate mutated candidates with the same rubric.
4. Convergence check — Stop when scores stabilize or iteration budget exhausted.

Phase 5 — Synthesize Recommendation

1. Ranked recommendation with confidence tag and reasoning.
2. Trade-off map — what the user gains and sacrifices with each viable option.
3. Sensitivity analysis — would shifting criteria weights change the recommendation?
4. Evidence quality assessment — gaps that could change the ranking.
5. Next steps — what to validate before committing.

Phase 6 — Persistence (optional)

If the user wants the ranked recommendations persisted to their Obsidian vault, call vault-writer (infrastructure/vault-writer) with the brief as a type: note artifact and the relevant target_domain, slug, and tags. Pass companion_source_path if the full brief should also be archived to Raw/. The vault-writer integrity report will list any unresolved wikilinks the caller should address (either by linking to existing notes or by leaving as known skill-library refs).

Output Format

AGENTIC RESEARCH BRIEF
══════════════════════
Question: [restated question]
Mode: [quick / standard / deep] | Iterations: [N]

RECOMMENDATION
──────────────
[Top candidate] — Confidence: [tag]
[2-3 sentence summary]

TRADE-OFF MAP
─────────────
[Candidate 1]: Best at [X], sacrifices [Y]
[Candidate 2]: Best at [Z], sacrifices [W]

EVALUATION MATRIX
─────────────────
[Weighted scoring matrix]

SENSITIVITY
───────────
[Decisive criteria; what would flip the recommendation]

EVIDENCE QUALITY
────────────────
[Confidence tags per candidate; evidence gaps that could change ranking]

NEXT STEPS
──────────
[What to verify before committing]

Error Handling

Cross-Domain Connections

• Spelunker orchestrator — Routes generative questions here; returns structured brief.
• Confidence framework — Shared with all spelunker children via ../spelunker/references/confidence-framework.md.
• Source-triangulator — Called during Phase 2 (grounding) and Phase 3 (evidence) at reduced depth.
• Game theory / mechanism-design — Evaluation matrices share structure with payoff matrices.
• Data science / statistical-analysis — Sensitivity analysis parallels parameter sensitivity testing.