Battle-Tested

During algorithm development, a tracking approach failed completely on real figure skating videos — detecting 6.47s of air time on a jump that lasted 0.7s. The same approach was retried with small tweaks.

Blindspot Interception — after 2 identical failures, force a root cause analysis. After 30 minutes on a dead-end approach, force a strategy switch.

A data pipeline was accidentally executed in the wrong project directory. Cross-project file contamination was a real risk.

Environment Pollution Guard — any cross-project reference triggers an immediate stop-and-confirm before proceeding.

A new rule for automatic case study collection was added to the project-level memory — visible only in the current project. But case studies are mainly generated during development of other products.

No existing rule covered this — it was a blindspot in the SOP creation process itself.

A new protocol was written to project-level memory instead of the global config. The rule explicitly stating 'check if cross-project rules are globally visible' was already in place — but the AI skipped the self-check.

Blindspot Interception — writing new rules must include a scope self-check.

The Challenge Protocol — requiring the AI to question its own output — was just added to the global config. Minutes later, the AI proactively flagged a limitation in a hook it had just built: the keyword detection only covered Chinese terms, missing English edge cases.

Challenge Protocol — during execution, if you notice a potential issue, raise it instead of silently continuing.

During a session wrap-up, the AI reported 'next step: rewrite algorithm v2' — but v3 had already been completed in a previous session. The stale recommendation came from a data copy that wasn't updated.

No rule covered this — the data copy itself was the architectural flaw.

JumpOnion had reached Phase 4 with 166 tests and 9/11 calibration videos passing. But it had never created a unified rules document for AI agent collaboration — critical lessons were scattered across memory files.

New Project Coverage Detection — on session start, check if the project has a unified agent rules document.

During golden file generation, 3 out of 10 calibration videos downloaded 0 frames from the database. The other 7 worked fine. The temptation was to debug network or permissions.

Data Provenance — after cleaning data, all upstream references must be synced. Blindspot Interception — stop chasing symptoms after 30 minutes.

During E2E validation, a metric showed real data at ~1.0 but the threshold was set at max 0.15. The quick fix: just raise the threshold to 1.0 and everything goes green.

Zero Misdiagnosis Principle — fix the definition first, then adjust the numbers. Verification exists to be meaningful, not to be green.

During calibration with 11 real figure skating videos, the system diagnosed a world champion's textbook triple Axel as 'high under-rotation risk.' The confidence was 0.61 — just barely above the 0.60 threshold.

Zero Misdiagnosis Principle — better to say nothing than to say something wrong. Confidence gating — suppress diagnosis below reliability thresholds.

The diagnosis pipeline was connected to the production route. 732 tests passed, 0 failures. Everything looked ready to ship. But my AI partner remembered something.

Challenge Protocol + Verification Before Completion — eval scripts passing does not equal production verification. The AI cited a previous incident where tests passed but production broke.

Three cross-tool session handoffs occurred in a single day: Claude Code to Cursor, Cursor back to Claude Code, then Claude Code to Cursor again. Each switch risked losing context about what was tested, what was blocked, and what came next.

Cross-Tool Handoff Protocol — every session exit must produce a handoff note. Every session start must read the previous handoff note and cross-validate against the source of truth.

While fixing an export feature, a database query used SELECT * but the manual field mapping missed a column. After fixing it, another missing column was found — the exact same bug pattern.

Bug Confession Protocol — when the AI fixes its own bug, it must self-report the pattern, not just the fix.