agentleFS
lithammerlithammer/ai/skills/nemesis/SKILL.md

nemesis

Skill0 starsChanged 52 days agoMITView on GitHub

Challenge a plan, design, or implementation one branch at a time.

The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 6 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep the same adversarial reviewer throughout the review loop, across all challenge-and-response turns.

    line 9
  2. Very specific:

    Read the target from the request and the conversation.

    line 22
  3. Distinctive:

    Start a fresh reviewer each time this skill is invoked.

    line 30
  4. Distinctive:

    If the harness supports only one-shot subagents, carry the prior transcript into each new dispatch.

    line 41
  5. Distinctive:

    Check the reviewer's status marker.

    line 47
  6. Distinctive:

    If the user asks to stop, end the loop and summarize the current state.

    line 58
  7. Distinctive:

    When the review completes, the user stops it, or an error ends the run, preserve the available findings and release every reviewer started …

    line 62

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Nemesis

Keep the same adversarial reviewer throughout the review loop, across all challenge-and-response turns. Release it when the review ends.

Pick the brief

The target picks the brief:

  • A plan, design, or idea: NEMESIS_BRIEF.md.
  • An implementation, such as a diff, branch, or PR: two passes, FIT_BRIEF.md then ECONOMY_BRIEF.md. Fit goes first: trimming a change that turns out to be the wrong one wastes the pass.

Read the target from the request and the conversation; a working-tree diff only supports that reading. An argument of design or implementation decides it outright, and fit or economy runs that one pass alone. When the target spans both, ask the user which. Name the brief to the user before the first challenge.

Start or resume

Start a fresh reviewer each time this skill is invoked. Within that run, reuse it, sending only changes and new evidence. Start a replacement if it has already closed. Otherwise, release it before starting a replacement when the user asks for a reset, the brief changes, or the topic changes enough that prior critique would bias the review. Give a fresh reviewer the conversation context and these instructions:

  • The picked brief: how to challenge and close branches.
  • NEMESIS_REPORT_FORMAT.md: response format and status markers. Read this before driving the loop below.

If the harness supports only one-shot subagents, carry the prior transcript into each new dispatch. Wait for critique before taking a step that depends on it; gather independent context while the reviewer works.

Drive the loop

  1. Check the reviewer's status marker. If it is missing or invalid, ask the reviewer to restate the response with a valid marker before proceeding.
  2. On [CONTINUE], answer the challenge with evidence: defend the choice, or change it and show the result. Read the code when it can settle the point. Send the answer to the same reviewer and repeat.
  3. On [RESOLVED] or [STOPPED], end the loop and present the reviewer's closing summary, preserving the distinction between accepted risks and unreviewed or unresolved branches.
  4. In a two-pass run, a [RESOLVED] fit pass starts the economy pass on the code as it now stands. A [STOPPED] fit pass ends the run.

If the user asks to stop, end the loop and summarize the current state.

Release reviewers

When the review completes, the user stops it, or an error ends the run, preserve the available findings and release every reviewer started by this run before presenting the final summary. Leave unrelated agents alone. Report cleanup failures in the summary.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.