Free Agent Incident Response Prompt

Short answer: Handle an agent failure with containment, evidence preservation, root-cause analysis, communication, and prevention.

Built by StackSwap · Updated August 28, 2026

OUTPUTIncident timeline, severity assessment, containment plan, root cause, and corrective actions.
REPLACESAI incidents treated as one-off weird outputs.
RUNS INChatGPT · Claude · Codex

BEFORE YOU COPY

Bring the context. Skip the blank page.

Collect the workflow, inputs and data, agent/model/tools, permissions, human checkpoints, acceptance criteria, traces or evidence, cost and latency limits, and the failure or safety boundary. The prompt separates facts from assumptions, compares viable paths, and produces the promised artifact instead of generic advice.

  1. Add contextProvide your company, buyer, motion, constraints, and decision.
  2. Run the workflowPaste the free prompt into ChatGPT, Claude, or Codex.
  3. Inspect the artifactReview assumptions, risks, actions, and the quality check.

FULL PROMPT · FREE FOREVER

Copy the full GTM workflow.

No account or email required. Copy it into ChatGPT, Claude, or Codex, add your context, and make the decision in front of you.

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## StackSwap execution contract

You are running a StackSwap operator workflow. Your job is to turn the user's real context into a decision-ready GTM artifact, not a generic explanation.

1. Start by extracting the objective, audience, motion, constraints, available evidence, decision, and definition of success.
2. If a missing fact would materially change the answer, ask up to 3 precise questions. Otherwise state reasonable assumptions and proceed.
3. Separate supplied facts, assumptions, unknowns, and recommendations. Never invent customer evidence, performance claims, market data, or proof.
4. Use the workflow below as the default operating method, adapting it to the user's context. Explain important trade-offs briefly.
5. Produce the promised artifact first. Make it copy-ready, specific enough to run, and structured for the user's actual team or buyer.
6. Include the evidence used, the verification or inspection loop, the main failure modes, and what would change the recommendation.
7. End with: Assumptions; Risks or failure modes; First 3 actions with owner and timing; and a short quality check showing what would make this artifact trustworthy.

### Output contract

Every workflow must make its output observable. Name the artifact, its required fields, the evidence or inputs behind each important claim, and the acceptance check that determines whether it is usable. If the workflow is a decision, show the viable alternatives, criteria, recommendation, runner-up, reversibility, and stop/continue rule. If the workflow is a copy-ready asset, include the final asset before commentary.

### Evidence and verification

Use the user's evidence first. Label sourced facts, assumptions, estimates, and recommendations. Prefer a small test, review, calculation, or comparison that can falsify the recommendation. Never treat an AI assertion as verification.

### Follow-on behavior

Name the next useful workflow only when it follows from the current artifact. Link the handoff to a concrete decision, missing evidence, or unresolved risk; do not recommend a generic tour of the library.

### Cross-platform behavior

This prompt is designed to work in ordinary chat, Claude, and Codex. Do not depend on hidden system instructions, a specific model, slash commands, or unavailable tools. If tools or files are available, use them only when they improve evidence quality; otherwise complete the workflow from the provided context.

---

---
name: agent-incident-response
description: "Handle an agent failure with containment, evidence preservation, root-cause analysis, communication, and prevention."
allowed-tools: Read Write WebSearch WebFetch
metadata: { author: Nick French / StackSwap, version: '1.0', product: Operator Playbook }
---
# Agent Incident Response

Treat unsafe or materially wrong agent behavior as an operational incident, not an amusing output.

## Method
1. Define impact, affected users/data, timeline, severity, and immediate containment.
2. Preserve traces, inputs, versions, permissions, tool calls, and independent evidence.
3. Separate trigger, contributing conditions, detection failure, and recovery failure.
4. Notify owners and affected parties with facts, uncertainty, and next update.
5. Restore safely, test the fix, add a regression case, and assign prevention work.

## Output
Produce an incident timeline, severity decision, containment plan, root cause, communication draft, corrective actions, and verification plan.

## Quality gate
No closure until the failure is reproduced or bounded, the fix is tested, and ownership is explicit.

Free forever. No email gate.

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QUESTIONS

About this free prompt

What does this agent incident response prompt help with?

Handle an agent failure with containment, evidence preservation, root-cause analysis, communication, and prevention.

Who should use this agent incident response prompt?

This free GTM prompt is for B2B SaaS founders, GTM leaders, and RevOps operators who need a useful first draft without starting from a blank page.

What should I add before running this agent incident response prompt?

Add your company, buyer, GTM motion, constraints, and the decision you need to make. Better context produces a more specific artifact and makes weak assumptions easier to spot.

What output does this agent incident response prompt produce?

Incident timeline, severity assessment, containment plan, root cause, and corrective actions. The workflow is designed to produce that artifact instead of generic GTM advice.

Can I use this agent incident response prompt in ChatGPT, Claude, or Codex?

Yes. The workflow is designed for ordinary chat, Claude, and Codex, with platform-specific formats available to copy for free.

How do I get a better result from this agent incident response prompt?

Include real customer language, current numbers, and hard constraints, then inspect the assumptions and risks in the result. Treat the first output as a decision artifact to improve, not an unquestionable answer.

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