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Get Started Free →Campaign: Forward-looking failure analysis combining pre-mortem rapid screening with systematic FMEA deep-dive. Core question: If this artifact fails, how will it fail? Methods: Klein Pre-Mortem 2007, AIAG-VDA FMEA 2019, IEC 60812.
.claude/skills/yogsoth-ai-failure-anticipation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 83% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -6% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 42% | 0% |
Core question: If this artifact fails, how will it fail?
| Artifact Type | Primary Strategy | Fallback Strategy | |---|---|---| | hypothesis, claim | prospective-hindsight | design-fmea | | research-question | design-fmea | process-fmea | | idea, approach | design-fmea | prospective-hindsight | | experiment-design | process-fmea | design-fmea | | gap | risk-prioritization | prospective-hindsight |
| Parameter | S (Quick) | M (Standard) | L (Deep) | |---|---|---|---| | Failure modes | 8 | 20 | 40 | | Failure chain depth | 2 | 4 | 6 | | Mitigation measures | 3 | 8 | 15 | | Re-scoring rounds | 1 | 2 | 3 |
Each subagent operates in isolated context. Pre-mortem facilitation runs first to generate rapid failure scenarios. High-severity items are passed to FMEA subagents for structured analysis. Severity/Occurrence/Detection scores flow through action-priority-matrix for classification. Mitigations are validated via re-scoring loop.
Produces FailureAnticipationReport containing: failure mode catalog, cause-effect chains, S/O/D scores, action priority classification (H/M/L), mitigation measures, and post-mitigation re-scores.
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Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use | | --- | --- | | design-fmea | Strategy: Research design-level FMEA — function analysis, failure mode identification, severity/occurrence/detection scoring per AIAG-VDA 2019. | | mitigation-design | Strategy: Design prevention, detection, and response measures for high-priority failure modes. Produces actionable countermeasures validated via re-scoring. | | process-fmea | Strategy: Research execution process FMEA — analyzes how the research process itself can fail during execution, distinct from design-level failures. | | prospective-hindsight | Strategy: Klein pre-mortem — assume the artifact has failed, then retrospect plausible causes. Generates rapid failure scenario catalog. | | risk-prioritization | Strategy: Action Priority matrix — classifies failure modes into H/M/L priority using severity-weighted scoring per AIAG-VDA 2019 Action Priority tables. |
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | failure-chain-tracing | Tactic: Trace upstream causes and downstream effects of each failure mode. Builds multi-level cause-mode-effect chains for systemic understanding. | | mitigation-validation | Tactic: Run mini-FMEA on proposed mitigations to verify they do not introduce new failure modes. Prevents mitigation-induced risks. | | premortem-to-fmea-pipeline | Tactic: Pre-mortem rapid screening feeds high-risk items into full FMEA analysis. Bridges fast intuitive generation with systematic structured analysis. |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. | | context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. | | mitigation-proposal | Proposes concrete mitigation strategies for identified weaknesses. Generates prevention, detection, and response measures with feasibility assessment. | | stress-test-saturation-detection | Determines whether validation has reached saturation — no new weaknesses or failure modes being discovered. Used by all 5 campaigns as termination signal. | | verdict-synthesis | Synthesizes findings from a completed campaign into typed verdict reports. Produces DebateVerdict, RedTeamReport, FailureAnticipationReport, CounterfactualMap, or AdversarialStressReport depending on campaign. Also supports cross-campaign StressTestSummary. |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→fail | 20,581 | 22,682 | +10% | 1 | 1 | 0% | 3,142 | 4,753 | +51% | 0 | 0 | — |
case-01 | fail→pass | 29,917 | 28,141 | -6% | 1 | 1 | 0% | 4,996 | 5,122 | +3% | 0 | 0 | — |
case-02 | fail→pass | 26,283 | 33,537 | +28% | 1 | 1 | 0% | 4,442 | 6,211 | +40% | 0 | 0 | — |
case-03 | fail→fail | 18,563 | 6,509 | -65% | 1 | 1 | 0% | 2,959 | 1,525 | -48% | 0 | 0 | — |
case-04 | fail→fail | 18,559 | 29,336 | +58% | 1 | 1 | 0% | 3,090 | 5,364 | +74% | 0 | 0 | — |
case-06 | fail→pass | 16,363 | 21,791 | +33% | 1 | 1 | 0% | 2,537 | 4,639 | +83% | 0 | 0 | — |
case-07 | fail→fail | 14,351 | 34,957 | +144% | 1 | 1 | 0% | 2,183 | 7,283 | +234% | 0 | 0 | — |
case-08 | fail→fail | 23,029 | 46,630 | +102% | 1 | 1 | 0% | 3,637 | 7,994 | +120% | 0 | 0 | — |
case-09 | fail→pass | 14,782 | 6,274 | -58% | 1 | 1 | 0% | 2,346 | 2,205 | -6% | 0 | 0 | — |
case-10 | pass→pass | 16,132 | 9,154 | -43% | 1 | 1 | 0% | 2,429 | 2,558 | +5% | 0 | 0 | — |
case-11 | pass→pass | 11,685 | 4,836 | -59% | 1 | 1 | 0% | 1,788 | 1,937 | +8% | 0 | 0 | — |
case-12 | pass→pass | 19,039 | 20,172 | +6% | 1 | 1 | 0% | 3,348 | 4,608 | +38% | 0 | 0 | — |
case-13 | pass→pass | 7,831 | 3,325 | -58% | 1 | 1 | 0% | 1,285 | 1,602 | +25% | 0 | 0 | — |
case-14 | fail→fail | 8,918 | 2,146 | -76% | 1 | 1 | 0% | 1,282 | 1,441 | +12% | 0 | 0 | — |
case-15 | fail→fail | 10,307 | 2,377 | -77% | 1 | 1 | 0% | 1,610 | 1,526 | -5% | 0 | 0 | — |
case-20 | fail→fail | 6,262 | 6,606 | +5% | 1 | 1 | 0% | 627 | 1,692 | +170% | 0 | 0 | — |
case-16 | pass→fail | 11,078 | 2,954 | -73% | 1 | 1 | 0% | 1,646 | 1,618 | -2% | 0 | 0 | — |
case-17 | fail→pass | 16,626 | 15,347 | -8% | 1 | 1 | 0% | 2,415 | 3,438 | +42% | 0 | 0 | — |
case-18 | fail→fail | 12,576 | 3,511 | -72% | 1 | 1 | 0% | 2,149 | 1,770 | -18% | 0 | 0 | — |
case-19 | fail→pass | 14,420 | 3,258 | -77% | 1 | 1 | 0% | 2,199 | 1,663 | -24% | 0 | 0 | — |
case-21 | fail→fail | 27,638 | 36,351 | +32% | 1 | 1 | 0% | 4,768 | 7,290 | +53% | 0 | 0 | — |
case-22 | fail→fail | 31,270 | 37,170 | +19% | 1 | 1 | 0% | 4,847 | 7,310 | +51% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +23 percentage points is the difference between those two pass rates over the 21 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.