agentleFS
slogsdonslogsdon/skills-workflows/skills/research-sweep/SKILL.md

research-sweep

Skill0 starsChanged 3 months agoMITView on GitHub

Research a list of topics in parallel, adversarially fact-check every load-bearing claim, and merge the survivors into one decision-grade brief. Use when Shane says "deep research on X", "research these topics", "run a research sweep", "I need a brief on X before deciding", or hands over a list of questions to answer with sources. Do NOT use for a single quick lookup (just search), for reading the vault (use /vault-retrieve), or for mining one account or corpus (use /source-mine).

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is 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.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    The counter-position requirement: Every topic must also research the case against itself.

    line 27
  2. Distinctive:

    Run topics in parallel. Each returns findings with sources and a per-claim confidence.

    line 48
  3. Very specific:

    Write one brief. Refuted claims are dropped or replaced by their correctedClaim, never silently kept.

    line 86

≈ “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

Skill: /research-sweep [topic list]

Produce a brief whose every number you would defend in public — by researching each topic independently, then paying a second agent to destroy the first one's claims.

Don't: dispatch a researcher without the preamble from research-preamble.md — an agent researching from memory looks identical to one researching from sources until you check a citation. Don't let the same agent research a topic and verify it; self-verification confirms, it does not test. Don't merge unrefuted and refuted claims into one brief without marking which is which.

Steps

Step nameStageRuns
scopeTopic list + shared contextorchestrator, with Shane
researchOne agent per topic, parallelresearcher subagents
verifyOne agent per load-bearing claimfact-checker subagents
mergeSingle decision-grade brieforchestrator

Stage 1: Scope — step scope

Settle three things before dispatching anything:

  1. The topic list. Each topic gets its own agent. A topic is one question with a stated angle, not a subject area — "which trigger signals empirically correlate with closed-won, and on what evidence" beats "buying signals".
  2. The shared context block. Product, positioning, and audience, pasted identically into every researcher prompt so findings come back usable rather than generic.
  3. The counter-position requirement. Every topic must also research the case against itself. Name it explicitly in the prompt.

Output: a topic list and a context block. Checkpoint: show Shane the topic list before spending N agents on it.

Stage 2: Research — step research — one subagent per topic

Each prompt is, in order: the preamble (with <DATE> substituted), then

You are a deep research analyst. Research one topic exhaustively and return structured findings.

# Topic: <title>
<what to cover — enumerate the specific sub-questions, named frameworks, and
originating sources to find. Be blunt about how much of this literature is
vendor content with no underlying study. Also research the counter-position.>

## <Brand> context (the brand this research serves)
<the shared context block>

Run topics in parallel. Each returns findings with sources and a per-claim confidence.

Output: N sets of findings. Checkpoint: none — flow straight into verification per topic as each lands.

Stage 3: Verify — step verify — one subagent per load-bearing claim

A claim is load-bearing if the brief's recommendation changes when it is false. Verify those; skip the rest.

Each prompt is the preamble, then:

You are an adversarial fact-checker. Your default posture is skepticism. Another
researcher, studying "<topic>", produced this claim:

CLAIM: <claim>
THEIR EVIDENCE: <evidence>
THEIR SOURCES: <urls>
THEIR CONFIDENCE: <confidence>

Try to REFUTE it. Run your own independent searches — do not just re-read their
sources. Check whether: the sources actually say what's claimed; the data is stale;
it's vendor marketing being treated as fact; it generalizes from one anecdote; a
plausible-sounding number was fabricated; the opposite is also documented somewhere
credible.

Set refuted=true if the claim is wrong, materially overstated, or unsupported by
anything you can find. When genuinely uncertain, lean toward refuted=true and
explain. Always fill correctedClaim with the version you would defend.

The uncertainty default is the point: uncertain resolves to refuted, and correctedClaim is always populated, so a survivable version exists even when the original dies.

Output: a verdict per claim. Checkpoint: report the refutation count before merging — a high rate means the research stage was too credulous, not that the brief is thin.

Stage 4: Merge — step merge

Write one brief. Refuted claims are dropped or replaced by their correctedClaim, never silently kept. State what could not be verified and what would change the recommendation.

Output: the brief.

Resume protocol

--resume-from <step> skips earlier steps. Valid steps: scope, research, verify, merge. Required state: research needs the topic list and context block; verify needs the findings; merge needs the verdicts.

Failure handling

A subagent that dies leaves its topic unresearched — say so in the brief rather than merging a silently short sweep.

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.