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 human-avatar/skills-for-humanity --skill s4h-decision-premortem-analysisgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis/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/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.01210 |
| Opus 5 | $0.00036 | $0.00605 |
| Sonnet 5 | $0.00015 | $0.00242 |
| Haiku 4.5 | $0.00007 | $0.00121 |
Grade A, and why
s4h-decision-premortem-analysis 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 12d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Premortem Analysis
Once a direction is chosen, commitment bias makes honest risk assessment nearly impossible — the mind starts defending the decision rather than evaluating it. This skill breaks that by mandating a specific fiction: assume the project has already failed. Then ask why. The pessimism is not optional — it is the mechanism.
Your Process
Step 1: State the Decision and Intended Outcome Write the decision clearly and the specific outcome it is supposed to produce. Include the timeline and the measurable definition of success.
Framing check: Confirm the specific decision before continuing. State what you've identified — the actual decision being stress-tested and its intended outcome — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the decision and its intended outcome]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Project to Failure Enter the failure frame. The statement is: "[Project name] launched on [date] and failed to achieve [outcome]. Here is what went wrong." Write this as if reporting a post-mortem, not brainstorming risks. The past-tense fiction reduces defensive filtering.
Step 3: Brainstorm All Failure Modes Generate failure modes without filtering for probability. Encourage pessimism. For each failure mode, ask: how would this actually unfold? What would be the first sign? What would make it worse?
Step 4: Group Failures by Type
- Execution failures: we had the right model of the world but did it wrong — timing, resourcing, coordination, quality.
- Assumption failures: we did it right but our model of the world was wrong — the market, the users, the technology, the dependencies.
- Unknown failures: we didn't anticipate this category of problem at all.
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.
- 12d ago First seen · 118 lines · 73 tokens per session scan A dd80d841badc
s4h-decision-premortem-analysis is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 1,210 once invoked, about $0.0004 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 2026-08-30.
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