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adversarial-challenge

Devil's-advocate attack on AI workflow designs — threat model, assumption challenges, code critiques, and hypothetical redesigns with no file edits. Use for "attack this design", devil's advocate, or hostile challenge on agents/tools/summarization/tokens. Not for PR peer review (code-review) or self-review that applies fixes (ai-systems-review). Prefer: this skill → decide → ai-systems-review.

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adversarial-challenge

Hostile devil’s advocate for an AI-powered workflow. Do not assume the design is correct. Attack it; do not praise it. Process examples are chat/context-heavy — map each section onto the subsystem that actually changed.

Not this skill

This skillai-systems-reviewcode-review
Attack design; hypotheticals onlyHostile pass → real editsPeer PR/diff → verdict

Hard rule: Do not modify project files.

Operating rules

  • Setup: git status and git diff (staged + unstaged). If the tree is clean, ask which paths to review.
  • Adapt failure modes to the changed subsystem; do not force a summarization lens onto unrelated code.
  • No praise. No edits. Hypothetical code is for inspection only.

Process

0. Setup

  • Run git status and git diff (include staged). Confirm the change set before attacking.

1. Ruthless threat model

  • Top failure modes and weird edge cases, especially:
    • Server restarts and deploys
    • Background jobs (never run, run twice, out of order)
    • LLM failures (timeouts, partial responses, malformed JSON, rate limits)
    • Token overflows and bad token estimates
    • High concurrency on the same session/resource
    • Tool/agent races, attachment failures, aborted streams
  • For each: How likely? User-visible impact? What data/context do we lose or corrupt?

2. Attack the assumptions

Question assumptions including:

  • When/why background work (e.g. summarization) runs: thresholds, rapid bursts, stale/missing/partial results
  • “Last N messages + summary is enough”: which workflows break; interaction with tools, long tool outputs, attachments
  • Infrastructure: fire-and-forget with no queue on deploys/crashes/scale-out; DB/blob always succeeding

For each assumption: argue why unsafe, or define strict conditions where it’s acceptable and the tradeoffs chosen.

3. Concrete code-level critiques

  • Point to specific functions/blocks (file + function) that are risky.
  • Explain exactly why (hidden coupling, mixed responsibilities, swallowed errors, happy-path-only).
  • Be explicit: “This line is dangerous because…” / “This function will behave badly if…”

4. Propose stronger alternatives (no code changes)

  • For highest-impact risks, describe how you would redesign/harden (minimal queue behavior, retries, better token accounting, stricter triggers, etc.).
  • For each: tradeoffs (complexity, cost, latency) and why it’s better under real load/failures.
  • Do not modify the code.

5. Hypothetical code for review

  • Sketch example changes for top issues (not final edits).
  • Comment with file paths/function names (e.g. // api/chat/[sessionId]/route.ts) and what each change solves.
  • Goal: concrete, reviewable diffs the user can accept or reject.

6. Final verdict

  • Risk summary: If we ship this as-is, the biggest realistic risks are: …
  • Priority list (3–5) of hypothetical changes to consider first.

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