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Failure Anticipation Campaign · yogsoth-ai | DecimalAI

researchby yogsoth-ai

Failure Anticipation Campaign

yogsoth-ai/failure-anticipation

Source yogsoth-ai/de-anthropocentric-research-engine ↗License Apache-2.0

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.

SkillScore45Maintained

Automatic

Research pack

Last verified 8/23/2026

What it changes

Passed 10 of 21 test cases with the skill, and 5 of 21 without it.

Without skill23%

With skill45%

Checks it passed that the model alone missed

Executes exactly 1 round of mitigation re-scoring.
Identifies at least 8 failure modes for Tier S analysis.
Generates a catalog containing at least 8 distinct failure modes.

and 8 more in the full report

6 fixed4 passed either way11 still failing1 broke

Pass-rate lift: +23 pp
Tokens: +45%
Agent turns: 0%
Response time: +4%

Model gemini-3.6-flashMeasured 8/23/2026v210 of 22 cases passed with skill · 21 comparable

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/yogsoth-ai-failure-anticipation/SKILL.md https://app.decimal.ai/s/yogsoth-ai-failure-anticipation/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add yogsoth-ai-failure-anticipation, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/yogsoth-ai-failure-anticipation/ and pins it in .decimal/skills.lock

decimalai skills add yogsoth-ai-failure-anticipation

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/yogsoth-ai-failure-anticipation/SKILL.md https://app.decimal.ai/s/yogsoth-ai-failure-anticipation/SKILL.md

Raw file: /s/yogsoth-ai-failure-anticipation/SKILL.md ↗ · machine-readable summary at /s/yogsoth-ai-failure-anticipation.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 6

Prompt

We are evaluating a novel algorithm approach for hyperparameter optimization. If design-level FMEA cannot be executed due to missing structural specs, what strategy serves as the fallback?

What was checked, without and with the skill
Checked	Without skill	With skill
Designates prospective-hindsight as the fallback strategy for an idea or approach.	Missed	Passed

case-19 · gemini-3.6-flash · 8/23/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md80 linessingle file

Failure Anticipation Campaign

Core question: If this artifact fails, how will it fail?

Methodology Sources

Klein (2007) — Pre-Mortem technique: assume failure, retrospect causes
AIAG-VDA FMEA Handbook (2019) — 7-step FMEA with Action Priority
IEC 60812 — Failure modes and effects analysis standard

Strategy Routing

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

Budget Table

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

Tactics

premortem-to-fmea-pipeline — Pre-mortem screens, high-risk items trigger full FMEA
failure-chain-tracing — Trace upstream causes and downstream effects
mitigation-validation — Mini-FMEA on proposed mitigations to verify no new risks

Context Management

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.

Output

Track record

Benchmark

10 / 21

test cases passed, vs 5 / 21 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

yogsoth-ai / de-anthropocentric-research-engine

skills/failure-anticipation/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/10/2026Version v2

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Failure Anticipation Campaign

Methodology Sources

Strategy Routing

Budget Table

Tactics

Context Management

Output

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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