agentleFS
preetamnathpreetamnath/agent-skills/skills/best-answer/SKILL.md

best-answer

Skill4 starsChanged 7 months agoMITView on GitHub

Build the single strongest answer to one open question — a diverse panel of subagents attacks it, a clean-room judge maps their disagreement, then you synthesize one grounded answer better than any single take. TRIGGER when: user says 'best answer', 'fan out and synthesize', 'give me the strongest answer'; user has a hard question and no answer yet. SKIP when: validating an answer you already have (validate-answer); surfacing problems in a draft (find-gaps).

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    NOT this if you already have an answer and want it checked → validate-answer.

    line 14
  2. Very specific:

    NOT this if you want a list of problems in a draft, not an answer → find-gaps.

    line 15
  3. Very specific:

    NOT this if you're grading one concrete proposal → second-opinion.

    line 16
  4. Distinctive:

    When all panelists share one model (e.g.

    line 32
  5. Distinctive:

    Write one grounded answer, attributing each load-bearing claim to its evidence.

    line 57

≈ “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

Best Answer

Build the single strongest answer to one open question: a diverse panel attacks it, a clean-room judge maps their disagreement, then you synthesize one grounded answer. The engine is diversity of error — independent attempts fail differently, so you resolve the differences deliberately instead of averaging them.

When to use

You have a hard question and no answer yet, and you want the single strongest one.

  • NOT this if you already have an answer and want it checked → validate-answer.
  • NOT this if you want a list of problems in a draft, not an answer → find-gaps.
  • NOT this if you're grading one concrete proposal → second-opinion.

Instructions

Step 1 — Frame the question and design the panel

Restate the question in a line or two. Pick a panel size (default 3; range 2–4).

Give each panelist a distinct mandate — angles that attack the question from non-overlapping directions:

Task typeExample panel
Research / analysisfirst-principles mechanism · skeptic (what's overstated) · practitioner (how it plays out)
Diagnosiseach panelist pursues a different culprit class
Design choiceeach authors a different approach, then argues its failure modes

When all panelists share one model (e.g. Opus), diversity must be manufactured from the mandates — identical panelists co-vary and the judge's map comes back empty.

Step 2 — Fan out the panel (parallel)

Dispatch all panelists in one message (multiple Agent calls) so they run concurrently. Each gets the same question, its own mandate, and tools (web search, bash, read). Ask each for a structured answer — its position, key claims with evidence, and a confidence — not a raw dump.

Step 3 — Map the disagreement (clean room)

Dispatch one general-purpose subagent as a clean-room judge — give it only the panel outputs (verbatim, each labeled by panelist) and the question, never your reasoning or which panelist you trust. Instruct it to:

  • map the answers into three buckets — contradictions (incompatible positions, with each side), blind_spots (relevant points NO panelist raised), unique_insights (a point only one panelist made);
  • earn each item — only real contradictions and material omissions, no padding;
  • map, don't decide — pick no winner, write no final answer;
  • surface, don't verify — don't run code or chase ground truth (that's Step 4);
  • not spawn its own subagents;
  • return the DisagreementMap verbatim — the structured map is the whole response, no narrative.

Step 4 — Verify and synthesize (you)

You hold the original context, so you write the answer:

  • Resolve each load-bearing contradiction deliberately — check ground truth where it's cheap (read the code, run the check), dispatching a subagent per fork when needed. Don't split the difference.
  • Fold in unique_insights that survive scrutiny.
  • Address blind_spots, or flag them as open.

Write one grounded answer, attributing each load-bearing claim to its evidence.

Rules

  • Recursion guard. Panelists must not fan out their own panels — keep deliberation to one level.
  • Cost discipline. Reserve for hard, open-ended questions; answer a cheap or deterministic one directly.

Output Schema

DisagreementMap {
  contradictions: [ { topic: string, positions: string[] } ],
  blind_spots: string[],          // each: the gap + why it matters, one line
  unique_insights: [ { insight: string, raised_by: string } ]
}

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.