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.
git clone --depth 1 https://github.com/nicoladevera/thinking-stackWrote 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/commands/nicoladevera/thinking-stack/premortem)<a href="https://agentmods.dev/commands/nicoladevera/thinking-stack/premortem"><img src="https://agentmods.dev/badge/commands/nicoladevera/thinking-stack/premortem/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/commands/nicoladevera/thinking-stack/premortem"><img src="https://agentmods.dev/badge/commands/nicoladevera/thinking-stack/premortem.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 | $0.00014 | $0.01497 |
| Opus 5.5 | $0.00006 | $0.00599 |
| Sonnet 5.5 | $0.00003 | $0.00299 |
| Haiku 4.5 | $0.00001 | $0.00150 |
Grade A, and why
premortem 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Premortem — Adversarial Risk Analysis
You are running a premortem: a structured risk analysis that assumes the idea or decision in $ARGUMENTS has already failed, then works backward to identify why. Your role is to surface failure causes before they happen — not to debate the idea's merit, but to stress-test its assumptions and expose its weak points.
The user invoked this with: $ARGUMENTS
Phase 1: Intake & Readiness
Assess whether $ARGUMENTS gives you enough to run a meaningful premortem. You need: (1) what the idea or decision is, and (2) enough context to reason about how it might fail. You do not need a full business plan — a paragraph of context is sufficient.
If the idea is too vague (no clear action, no discernible domain, no hypothesis):
Use AskUserQuestion to ask up to 3 targeted clarifying questions. Only ask what's actually missing — do not ask all 3 if 1-2 are already clear:
- What is the idea or decision you're evaluating?
- What does success look like in 12–18 months?
- Any constraints (budget, team, timeline, compliance) or prior thinking that matters?
If the idea is ready: proceed directly.
Once you have enough context, synthesize a framing statement in this form:
It is [time horizon] later. [Idea/decision] has failed.
Present the framing statement to the user and wait for explicit confirmation before proceeding. If they correct or refine it, update accordingly.
Phase 2: Optional Research
Before generating failure causes, assess whether external data would meaningfully improve the analysis. If so, use WebSearch and WebFetch (2–3 queries maximum) to surface:
- Real-world analogues of similar ideas or bets that failed
- Base rate data for the domain (e.g., "API partnership churn rate", "B2B SaaS integration failure patterns", "fintech regulatory enforcement trends")
Skip this step if the idea is purely internal/strategic with no useful external comparables, or if the domain is highly proprietary.
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 · 167 lines · 14 tokens per session scan A 544a2a59d20e
premortem is a command published in the GitHub repository nicoladevera/thinking-stack (2 stars, last pushed 6mo ago), licensed MIT. It adds 14 tokens to every session and 1,497 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 2026-08-31.
Other commands, from other repositories
change
Entry point for any change request. Orchestrates the planner-routing pipeline (cm-planner → executor → reviewer → conventions-audit → test-author → commit). Use for any work that touches code or docs.
audit-plugin
Audit plugin skills, commands, and agents for structure, size, and naming issues.
tldr
Re-apply TLDR rules for this turn (verdict first, no filler).
lfe-plan-critique
Run a 5-lens pre-build critique of the approved active plan before the Builder starts. Acts as the Architect persona, read-only on src/. Writes .plans/plancritique.md. Use immediately after Brain approves activeplan.md.
scaffold
Analyze repository and generate CLAUDE.md, AGENTS.md, and project-specific skills.
dd-spec-test
Generate test spec (requires confirmed status).