| name | finder-reactor |
| description | Core autonomous, self-guarded decision loop for opportunity-finder apps. Handles discovery (e.g. X search), analysis (LLM + CV + platform context), prep generation, tracking, and guarded promote with cost/rate/fit/CV mutation guards, human pause points, structured decisions, and logging. Use when designing, implementing, or debugging the agentic heart of a finder platform. Fission for tight loops; fusion for reactor architecture and surplus. |
Finder Reactor — Self-Guarded Opportunity Engine
Load rule: Formal SoT. Fission loops → ai-optimization; architecture/surplus → fusion-sage; CV writes → cv-promote-guard.
CLT: ../rules/clt-dual-load.mdc — HITL pauses stay short+decisive; agent loop may continue in parallel until a human gate fires.
// Mission
Finder ≔ search → analyze → decide → prep → track → (promote?) → surplus
// with guards + HITL pauses on high stakes
// Axioms
A1 Guard ≻ Speed // named check + audit before threshold cross
A2 Pause on stakes/ambiguity // CV promote, low conf, cost/rate, writes
A3 Human owns query // never hide "best" search
A4 Sidecar-first external mutation // promote proposal before master write
A5 Decide outputs structured // conf + guards + rationale (zod/serde)
A6 Surplus after every cycle
A7 Reversible + auditable // JSON state; .bak + git-friendly CV diffs
Reactor Loop (Conceptual State Machine)
Search (tunable query + platform context)
→ Ingest + Classify
→ Analyze (pruned CV packet + platform skill/llms + post → fit, gaps, angle, confidence)
Guard: cost, rate, basic validity
→ Decide (structured: pursue? score? prep now? pause for user? why?)
→ Prep (if cleared): letter, cv-delta, research, outreach (guarded)
→ Present + Track (UI/MCP)
→ Export / Apply (user or approved agent action)
→ Outcome → Learn
→ Promote (if pattern or explicit): guarded path to external CV repo
→ Surplus
Every arrow has a guard or pause hook.
Pause / Intervention Points (UI toasts/dialogs + MCP ask_user tool):
- Before expensive LLM batch.
- On fit 60–85 or "interesting but stretch".
- Before any promote.
- On rate limit approaching or cost budget hit.
- On low confidence decision.
Implementation Patterns
Fission: Prune CV to "CorePacket"; prune platform posts; token budget on every prompt; structured output schemas.
Fusion: Treat as FinderReactor state machine; cross-cutting guards (CVPromoteGuard, CostGuard, RateGuard, DecisionLogger, SurplusGenerator); persist "what worked".
MCP / Skill Exposure: Key entry points as MCP tools (search, analyze, prep, decide, promote). Document in root SKILL.md (agentskills.io style).
Guardrails (Non-Negotiable)
- Never send full raw CV JSON or unpruned threads to LLM unless user explicitly asks for "deep mode" with confirmation.
- Never write to external profile CV without sidecar + preview + two explicit confirms.
- Always surface rate limit headers and estimated spend.
- All autonomous "decide" outputs must include confidence (0–100) and intervention level.
- Reactor dev: follow bdd-strategizer for guard + pause logic.
Activation for Agents
Surplus Generation (Mandatory After Reactor Changes)
⚡ Finder Surplus (Q ≈ X.X)
This change would have [saved Y tokens / avoided Z bad decisions] on recent opportunities.
Concrete future win: [specific example].
Suggested follow-up: [one compounding improvement].
Track in .agents/skills/finder-reactor/surplus-log.md or fusion-state.
Related
x-agent-resources, cv-promote-guard, tauri-agentic, ai-optimization, fusion-sage, agentic-reactor.
Example provenance: battle-tested on collab-finder.
