s4h-decision-premortem-analysis

s4h-decision-premortem-analysis is a skill for Claude Code from human-avatar/skills-for-humanity. It costs 73 tokens per session (1,210 once invoked), scanned A, original, MIT.

A risk-review technique that imagines a decision has already been made and the project has failed, then works backward to identify the causes. This is called a premortem.

In plain words
What is it for?
Use it to stress-test a decision, identify likely failure modes, examine risks, and improve a plan before committing to it.
Why use it?
Assuming failure makes it easier to discuss risks honestly after a direction has started to feel attractive or committed. It exposes weaknesses before they become real problems.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: names the AskUserQuestion tool.

Part of the skills-for-humanity plugin — 197 skills, 1 hook shipped together

Good fit Use it to stress-test a decision, identify likely failure modes, examine risks, and improve a plan before committing to it.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis
Install

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.

Any agent
npx skills add human-avatar/skills-for-humanity --skill s4h-decision-premortem-analysis
Clone the repo
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity

Made for: Claude Code.

Or install skills-for-humanity, the plugin that ships this one along with the rest of its 197 skills, 1 hook.

Wrote 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.

agentmods badge for s4h-decision-premortem-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis/github.svg)](https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-decision-premortem-analysis)
Your own site
<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.

agentmods 80×15 button for s4h-decision-premortem-analysis

Your own site · 80×15
<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>
Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,210 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash dd80d841badc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/s4h-decision-premortem-analysis/SKILL.md · 118 lines

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.

Read the full file on GitHub · 118 lines

Changes

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.

  1. 12d ago First seen · 118 lines · 73 tokens per session scan A dd80d841badc

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

decision-making-under-pressure

Provides practical habits for making better decisions faster -- reframing problems before solving them, knowing when to stop deliberating, balancing intuition with analysis, resisting 'just this once' compromises, and breaking overthinking cycles. Use when facing a high-stakes choice with incomplete data, sensing that…

fatihguner/foreman · 95 tokens

flow-state

Optimizes individual and team productivity using Csikszentmihalyi's flow state framework, covering the conditions for flow (clear goals, immediate feedback, challenge-skill balance), the four-stage flow cycle, and organizational flow design. Use when auditing personal or team focus time, diagnosing productivity loss…

fatihguner/foreman · 96 tokens

stoic-right-action

Applies Stoic ethics of right action to entrepreneurial decision-making, covering the four cardinal virtues (courage, justice, temperance, wisdom), ethical leadership under pressure, and the discipline of choosing the harder right over the easier wrong. Use when facing ethical dilemmas in business, building a…

fatihguner/foreman · 89 tokens

bakeoff

Turn one decision into a judged tournament of solutions, then pick the best. Given a problem, design choice, or a suggestion you want cross-verified, it generates diverse candidate solutions, auto-derives the evaluation dimensions for THAT problem (so you don't have to know what to score on), judges every candidate…

CoriChui/bakeoff · 330 tokens

balanced-scorecard

Translate strategy into metrics across Financial, Customer, Internal Process, and Learning & Growth perspectives.

tupe12334/instinct · 22 tokens

customer-journey-map

Visualize every touchpoint a customer has with a product or service — stages, actions, emotions, pain points, opportunities.

tupe12334/instinct · 31 tokens