Discovery Loop

Discovery Loop

Purpose

Verification kills bad ideas; nothing in a kill-oriented process creates new ones. A finished investigation with an honest verdict ("no edge found", "not reproducible", "root cause is X") quietly becomes a wall: future sessions treat the kill-list as the space. The two documented failures this skill exists for: (1) a universal negative read off a finite list — "we tested 9 strategies, all dead" became "no edge exists," until an outsider's success disproved it in one evening; (2) belief moving on vibes — credence climbing through a streak of confirmations with no attack, or a "winner" anointed from a short window that a full-history audit later showed was net-negative. The loop makes generation a standing duty and belief an explicit, attackable number.

When to use this skill

• Standing investigations that survive across sessions: market/edge hunts, recurring incidents, performance regressions, growth experiments, research programs.
• A verdict/kill-list exists and is about to be cited as the reason not to look.
• External evidence contradicts your closed conclusion (a competitor does the "impossible" thing) — that is a lead, not an annoyance.
• You notice every recent hypothesis came from your own head, none from observation.

When NOT to use

• One-shot tasks with a definite end (a bug fixed and verified; a question answered).
• Domains where more hypotheses have no value (the decision is made, the system is being decommissioned).
• As a license to re-litigate a kill WITHOUT new evidence — re-opening requires a new observation, not boredom with the verdict.

The loop (every session the investigation is touched)

1. Observe before theorizing. Spend the first cycle on what the domain is DOING now — who is succeeding, what changed, what the live data shows — via the cheapest real-observation channel (public fills, logs, dashboards, competitor output). Winners' observed behavior outranks your priors as a hypothesis source.
2. Generate ≥1 NEW hypothesis, dated, even half-baked. From the observation, not from re-reading old brainstorms. Log it in the hypothesis ledger with a status (OPEN).
3. Keep a belief-state, not a feeling. Each hypothesis carries: credence (your probability it's real, 0–1), the evidence chain (each test, dated, with the credence delta it caused), and its cheapest next falsifying test. Update the number when evidence lands — both directions.
4. Attack every streak. Track consecutive credence-raising events with no kill-attempt between them. At 3, stop confirming and run the strongest attack you can design (decay slice, capacity, full-history depth, discriminating test). A streak is a signal to attack, never to size up.
5. Depth before anointing. Any "winner" found in a short observation window (a performer, a config, a tactic) gets a full-history audit before it becomes evidence — short windows manufacture winners (verified: a candidate that looked strong over ~8 samples reversed hard at depth; a "most consistent" performer was net-negative over its full history).
6. Kill or promote, in writing. Every hypothesis resolves to a dated verdict with receipts; killed ideas stay on the ledger (they block re-litigation without new evidence). Scope every verdict honestly: "no edge in what we generated" — never "the space is empty."