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 | $0.00018 | $0.00368 |
| Sonnet 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 8d 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.
- 8d 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 2mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 736 once invoked, about $0.0002 per session on Opus 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.
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…
local-paper-navigator
Find and read papers from the local papers library (repo papers/, mounted at /papers/). Three native tools form a reading funnel: papersearch (one line per paper), paperread (card + section outline), papersection (one verbatim section — the only full-text access). Use when: find papers in the local library, read a…
experiment-pipeline
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and…
paper-review
Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: 5-aspect checklist (contribution sufficiency, writing clarity, results quality, testing completeness, method design), counterintuitive protocol (reject-first simulation, delete unsupported claims, score trust…