PromptsEdge
FreeGitHubResearch

Evidence-First Research

Your AI conducts current-state research combining fresh public data and your local context. It integrates web search, multi-source synthesis, market analysis, and lead intelligence for fact-based comparisons and recommendations.

affaan-m on GitHub

Curated by PromptsEdge from a public repo · MIT license. All credit goes to the author.

What is this skill?

Use this when the user asks to research something current, compare options, enrich people or companies, or turn repeated lookups into a monitored workflow.

When to use

  • user says "research", "look up", "compare", "who should I talk to", or "what's the latest"
  • the answer depends on current public information
  • the user already supplied evidence and wants it factored into a fresh recommendation
  • the task may be recurring enough that it should become a monitor instead of a one-off lookup

This is the operator wrapper around the repo's research stack. It is not a replacement for deep-research, exa-search, or market-research; it tells you when and how to use them together.

Skill Stack

Pull these ECC-native skills into the workflow when relevant:

  • exa-search for fast current-web discovery
  • deep-research for multi-source synthesis with citations
  • market-research when the end result should be a recommendation or ranked decision
  • lead-intelligence when the task is people/company targeting instead of generic research
  • knowledge-ops when the result should be stored in durable context afterward

Guardrails

  • do not answer current questions from stale memory when fresh search is cheap
  • separate:
    • sourced fact
    • user-provided evidence
    • inference
    • recommendation
  • do not spin up a heavyweight research pass if the answer is already in local code or docs

Workflow

1. Start from what the user already gave you

Normalize any supplied material into:

  • already-evidenced facts
  • needs verification
  • open questions

Do not restart the analysis from zero if the user already built part of the model.

2. Classify the ask

Choose the right lane before searching:

  • quick factual answer
  • comparison or decision memo
  • lead/enrichment pass
  • recurring monitoring candidate

3. Take the lightest useful evidence path first

  • use exa-search for fast discovery
  • escalate to deep-research when synthesis or multiple sources matter
  • use market-research when the outcome should end in a recommendation
  • hand off to lead-intelligence when the real ask is target ranking or warm-path discovery

4. Report with explicit evidence boundaries

For important claims, say whether they are:

  • sourced facts
  • user-supplied context
  • inference
  • recommendation

Freshness-sensitive answers should include concrete dates.

5. Decide whether the task should stay manual

If the user is likely to ask the same research question repeatedly, say so explicitly and recommend a monitoring or workflow layer instead of repeating the same manual search forever.

Output Format

text
QUESTION TYPE
- factual / comparison / enrichment / monitoring

EVIDENCE
- sourced facts
- user-provided context

INFERENCE
- what follows from the evidence

RECOMMENDATION
- answer or next move
- whether this should become a monitor

Pitfalls

  • do not mix inference into sourced facts without labeling it
  • do not ignore user-provided evidence
  • do not use a heavy research lane for a question local repo context can answer
  • do not give freshness-sensitive answers without dates

Verification

  • important claims are labeled by evidence type
  • freshness-sensitive outputs include dates
  • the final recommendation matches the actual research mode used

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