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Premortem

Community
boshu2
premortem

Find how a rollout plan could fail before committing to it. Use when: asked what could go wrong or to poke holes in a plan.

Overview

Publisherboshu2
Repositoryagentops
Skill namepremortem
Stars
450
Forks
42
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by boshu2 on GitHub. Read the source before you install it.

Installation

Install the Premortem AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/boshu2/agentops/tree/main/plugin/skills/premortem
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/plugin/skills/premortem .claude/skills/premortem
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Premortem in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Premortem on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Premortem is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Premortem

Premortem is an optional plan-challenge strategy. It asks one fresh context to identify concrete ways the resolved bead or caller intent could fail before implementation. It is not part of the required RPI sequence and does not authorize readiness. Plan's shared challenge method owns optional exchange, independence and stopping rules; Premortem owns the three checks below. Neighbours: general advice is Review, acceptance of a finished change is Validate, and several independent views are Council.

Run the checks in this order and report findings in the same order: a finding from the first or second check outranks any single technical risk the third check constructs.

The first check: who verifies, and are they fresh?

Test the plan's evidence shape before any technical risk: for every unit of work, who verifies it, and is the verifying context distinct from the one that authored it? A plan whose closure step is "the implementer runs its own tests and closes" contains no independent judgment anywhere. Self-graded green is the classic false-done, and it ranks first because it silently converts every other failure into a shipped one.

The second check: which steps are one-way doors?

Walk the steps and mark each two-way (the plan can back out of it) or one-way (it cannot). For every one-way step name the exact undo cost, the point of no return, and who holds the handle when it is crossed: the caller, or an agent deciding inside a batch. A two-way failure costs a retry; a one-way failure costs the thing itself. Watch for nineteen reversible steps followed by an irreversible one, where the reflex trained by the first nineteen answers the twentieth.

The named failure mode is reversibility asserted, not traced: a rollback section that says "fully reversible" while one step revokes a credential, force-pushes or publishes. A material irreversible action outside existing caller authority is a finding; trace actual undo cost and authorization with Plan. Prior authorization remains valid: do not demand repeated approval at the crossing or call every uncertain detail irreversible. Stop condition: every step carries a mark, and every one-way mark carries its undo cost.

The third check: construct the failure

Draw candidate failures from what the plan takes for granted, not from a generic risk list. Typical premises: a tool or service behaving as the plan describes, the work holding at volumes far above or below what it expects where volume matters, steps finishing in the stated order and time. Aim first at the decision other steps rest on, and at any one-way step whose deciding premise is inferred rather than cited. Name the fact that would show each premise false; when it can be looked up now, look it up instead of reporting the premise as a risk.

For every candidate failure, attempt a concrete defeat: write the input, command sequence or repository state that would make the plan fail, and run or cite the check that shows whether the plan survives it. When execution is not available, the constructed input or sequence plus a cited fact (file and line, documented behavior, an observed output) counts as the attempt. A failure you could not construct is reported as attempted-and-blocked with the obstacle named, which is itself evidence for the plan. The named failure mode is armchair pessimism: imagined risks with no construction, which reads as diligence while testing nothing. A finding with neither a construction nor a blocking fact is deleted, not softened.

Workflow

  1. Resolve the existing intent source and inspect its acceptance, non-goals, evidence requirements and declared write scope. Its digest is the SHA-256 of the exact intent text as supplied (for example shasum -a 256 plan.md).
  2. Judge from a context that did not write the plan, following the shared challenge method for identity, model selection, authorization and bounds. A plan the caller wrote can be judged here, with the caller as author. If this context wrote the plan and no fresh context can be started, run the checks anyway, state that the independence leg is missing, and return inline findings; never describe them as independent.
  3. Run the three checks, then test acceptance completeness, edge behavior, scope and dependencies against cited repository facts. For integration or extension plans where anchoring on the working design is the risk, add the derivation-diff challenge.
  4. Return one complete, bounded set of concrete findings with checked and not-checked scope.
  5. Stop. The caller decides whether to revise the plan or invoke RPI.

Council or Dueling Idea Genies may be caller-supplied evidence, but Premortem requires neither and cannot turn consensus into approval.

Prompt

text
Premortem this plan before I implement: bead ag-4f21 proposes rewriting
`scripts/regen-all.sh` to call `ao gate check` instead of shelling out to
the Python generators, touching cli/internal/gates/regen.go. Plan and
acceptance are in the bead. Find concrete ways it fails.

It's working if

Observable in the trace, without reading the prose, and the rubric a fresh independent judge scores this skill against:

  • Every unit of work carries a named verifier, and any unit verified by the context that authored it comes back as a finding.
  • Every step carries a two-way or one-way mark, and each one-way mark names its undo cost and its point of no return.
  • Every reported finding cites a defeat attempt (the input, command or repository state constructed) or the fact that blocked the construction.
  • The finding set is bounded: a review that flags every step has reported nothing.

Boundary

  • Emit advisory findings, no verdict of any version, readiness, admission, or permission.
  • Do not implement, validate the candidate, retry, repair, schedule, claim, change acceptance, operate Git, close work, release, or deliver.
  • Any plan edit creates a new subject for a later caller-initiated Premortem.

Output

Return findings inline by default:

text
Findings (in check order, then most consequential first within each check)
1. <step> - <how it fails> - <construction, or the fact that blocked it> - <consequence>
Verifiers: <unit>: <who verifies>; self-verified units are findings
One-way steps: <step> - <undo cost> - <point of no return> - <who holds the handle>
Checked: <what was examined>. Not checked: <what was not>.
Independence: <judge context, distinct from author> or "missing: <reason>"

When the caller requests a durable review, return premortem-plan-review.v1 with the intent digest, author and judge context IDs, findings, evidence references, checked, and not_checked, and check it with this skill's scripts/validate-output.sh. The schema requires distinct author and judge IDs, so a review without an independent judge stays inline. An empty finding set means only that this optional challenge found no concrete defect; it is never a lifecycle gate.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Premortem AI skill do?

Find how a rollout plan could fail before committing to it. Use when: asked what could go wrong or to poke holes in a plan.

Why use Premortem on TypingMind?

Because you install it once and use it with any model. Premortem is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Premortem in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boshu2/agentops/tree/main/plugin/skills/premortem. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Premortem?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Premortem?

As many as you like. As long as a model supports skills, you can use Premortem with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Premortem AI skill free?

Yes. It is published on GitHub by boshu2 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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