Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yogsoth-ai/stress-test --skill premortem-to-fmea-pipelinegit clone --depth 1 https://github.com/yogsoth-ai/stress-testWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yogsoth-ai/stress-test/premortem-to-fmea-pipeline)<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/premortem-to-fmea-pipeline"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/premortem-to-fmea-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/premortem-to-fmea-pipeline"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/premortem-to-fmea-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00736 |
| Opus 5.5 | $0.00015 | $0.00294 |
| Sonnet 5.5 | $0.00007 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
premortem-to-fmea-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Mortem to FMEA Pipeline Tactic
Rapid pre-mortem screening identifies candidate failures; high-severity items trigger full FMEA deep-dive.
Orchestration
- premortem-facilitation generates failure scenarios (fast, intuitive)
- failure-mode-extraction structures scenarios into failure mode list
- severity-scoring performs rapid severity screen (1-10)
- Items scoring >= threshold pass to FMEA pipeline:
- function-analysis → failure-chain-construction
- occurrence-scoring + detection-scoring
- action-priority-matrix classifies H/M/L
- Low-severity items documented but not analyzed further
Threshold Logic
- Budget S: threshold = 7 (only critical items get FMEA)
- Budget M: threshold = 5 (moderate and above)
- Budget L: threshold = 3 (comprehensive coverage)
Subagents Dispatched
- premortem-facilitation (scenario generation)
- failure-mode-extraction (structuring)
- severity-scoring (screening gate)
- function-analysis, failure-chain-construction (FMEA deep-dive)
- occurrence-scoring, detection-scoring (full scoring)
- action-priority-matrix (classification)
Termination Conditions
- All generated scenarios have been screened
- High-severity items have completed full FMEA cycle
- Action priority assigned to all items above threshold
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| action-priority-matrix | Compute Risk Priority Number (RPN = S x O x D), classify failure modes into H/M/L action priority per AIAG-VDA tables. |
| detection-scoring | Rate detectability 1-10 (inverted: 10 = hardest to detect). Estimates how likely current controls would catch the failure before impact. |
| failure-chain-construction | Build cause-mode-effect chains tracing upstream root causes and downstream cascading effects for each failure mode. |
| failure-mode-extraction | Extract structured failure mode list from raw scenarios or artifact analysis. Produces standardized failure mode records. |
| function-analysis | FMEA Step 3: Decompose artifact into function tree — identify what each component is supposed to do before analyzing how it can fail. |
| occurrence-scoring | Rate failure mode occurrence probability 1-10. Estimates how likely each failure mode is to manifest during research execution. |
| premortem-facilitation | Execute Klein pre-mortem protocol — assume failure has occurred, generate plausible failure scenarios through prospective hindsight. |
| severity-scoring | Rate failure mode severity 1-10 based on end-effect impact. Follows AIAG-VDA severity scale calibrated for research artifacts. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 75 lines · 37 tokens per session scan A aed7546dd4f6
premortem-to-fmea-pipeline is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 736 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
relax-dev-debug
Develop and debug the Relax reinforcement learning project. Use this skill whenever modifying code in the relax/ directory, or running remote training jobs on a Ray cluster for validation. Also use it when the user mentions training, debugging training runs, submitting Ray jobs, or fixing training errors.
access-gevety-health-biomarker-data
Access your Gevety health data - biomarkers, healthspan scores, biological age, supplements, activities, daily actions, 90-day health protocol, and upcoming tests.
aminer-open-academic-search
ACADEMIC PRIORITY: Activate this skill whenever the user's query involves academic, scholarly, or research-related topics — including but not limited to: papers, publications, citations, scholars, researchers, professors, institutions, universities, labs, journals, conferences,…
acorn-theorem-prover-verifier
Verify and write proofs using the Acorn theorem prover for mathematical and cryptographic formalization. Use when working with Acorn proof files (.ac), verifying theorems, formalizing mathematical or cryptographic protocols, or writing proofs in the Acorn language. Triggers on:…
research-ideation
Quant-focused research ideation pipeline: scope selection (3 stages) → anchor-first literature grounding → single-core idea generation → iterative refinement → ELO tournament ranking (Final = N+R+C−D) → update evo-memory → user selects direction → expand into manuscript-quality proposal. Optimized for incremental…
quant-experiment-runtime
Quant research experiment executor: discover an offline source database under the workdir's code-repo, build a panel, run a Research Artifact's entry point to compute research-object values, and evaluate IC/ICIR/RANKIC/coverage metrics. Runtime = Experiment Executor; it runs a Research Artifact via a Python-native…