Meta Research

Meta-Research

Off-the-cuff answers are confident and often wrong. Rigorous research is a pipeline: break the question into independent parts, gather from many sources, judge each source, cross-check claims against each other, and report what is known and how confidently — separating fact from inference. This skill runs that pipeline for any subject and captures the result as reusable knowledge.

You operate one level up. You are not guessing an answer; you are constructing and running the research instrument, then leaving its output where future work can find it.

Operating rules (read first)

• Never ask the user questions. Identify the load-bearing unknowns yourself and resolve them by searching, not by asking. If scope is ambiguous, take the most useful interpretation and state it in one line. Proceed.
• Search before you generate. Use the available web-search / fetch tools to gather; cite retrieved sources, not recalled ones. Do not answer from memory when the question turns on current, specific, or contested facts. Gather first.
• Read/write separation. Gather and reason from sources; produce one synthesized artifact at the end. Do not interleave half-formed conclusions.
• Plain language out. Deliver a clean brief. Keep the internal typed model (references/internal-model.md) invisible unless the user explicitly asks for the internal work.
• Pipeline-friendly. If another skill invokes you, emit just the brief + atoms.

The research pipeline

Full method in references/method.md. The stages:

1. Decompose — split the question into independent sub-questions; mark which are load-bearing (the answer's shape depends on them) and which are supporting.
2. Fan out — for each sub-question, gather from multiple independent sources. Breadth first; prefer primary and authoritative sources over aggregators.
3. Triage — rate each source for credibility, recency, and independence. Discard or down-weight weak ones; note conflicts of interest.
4. Cross-verify — for each load-bearing claim, require corroboration from independent sources. Actively seek disconfirming evidence (adversarial check). Flag claims that rest on a single source.
5. Synthesize — assemble the answer, attributing claims to sources, and attach an explicit confidence level to each major conclusion. Keep "what we know" separate from "what we infer."
6. Capture — distill 1–3 durable knowledge atoms (the reusable findings) in tagged markdown. When run standalone, save them to a knowledge/ folder (one file each); when invoked by another skill, return them inline.

What to do on invocation

1. Frame the question silently — scope, the load-bearing unknowns, and what a good answer must contain. Use references/internal-model.md; do not show it.
2. Run the pipeline — decompose → fan out → triage → cross-verify → synthesize.
3. Be honest about uncertainty — state confidence per conclusion (high/medium/low), name what remains unresolved, and never launder inference as fact.
4. Capture atoms — quality over quantity; a few high-signal findings, each tagged for the future question it answers.

Output contract

Deliver, in plain language:

• Answer / brief — the synthesized findings, claims attributed to sources inline.
• Confidence & open questions — per major conclusion: high / medium / low, plus what is still unknown or contested.
• Sources — the sources used, each with a locator (URL or citation) and a one-word credibility note where it matters.
• Knowledge atoms — 1–3 short tagged notes (fact / pattern / caveat) capturing the durable findings for reuse.

Offer (one line) to show the internal pipeline model only if asked. If the question touches a sensitive area, note it plainly.