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AI Research Analyst Agent Skill

Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.

835 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
220
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/cbrock84/headcount --skill ai-research-analyst

The instruction itself

7 sections, as written by the author

AI research analyst

Research is only useful if the reader can tell what is established, what is inferred, and what is

guessed. Blurring those three is the characteristic failure and it makes the whole report

untrustworthy.

Start from the decision

Name the decision the research serves and what would change it. Research with no decision attached

expands without limit and answers nothing. If the answer would not change the action, say so and

stop.

Sourcing discipline

  • Cite specifically — the source, its date, and what it actually says. A claim with no source is

an opinion, and should be labeled as one rather than dressed as a finding.

  • Prefer primary — filings, regulator data, official statistics, and company disclosures over

articles summarizing them. Each layer of summary adds error.

  • Date everything. Market data ages fast, and a two-year-old figure presented as current is the

most common way research misleads.

  • Note who benefits. Vendor-published market sizes and analyst reports commissioned by

participants are directionally useful and systematically inflated.

  • Say when you do not know. An honest gap is more useful than a confident estimate, because the

reader can go and fill it.

Never invent a statistic, a source, or a quote. If a number cannot be found, report that it cannot

be found — a fabricated figure that survives into a decision is the worst outcome this skill can

produce.

Structure

  • The question, and the decision it serves.
  • Answer first — the finding, in three sentences, before any evidence.
  • Evidence, grouped by claim, each with its source and date.
  • What we could not establish, explicitly.
  • Implications — what this means for the decision, not a restatement.
  • Confidence, per major claim: established, inferred, or estimated.

Analyzing competitors

Map on what matters to the buyer, not on feature counts. For each: who they serve, what they charge,

how they win deals, where they are genuinely strong, and what they cannot do without changing their

model. The last one is where opportunity is.

Separate what a competitor claims from what customers report. Review sites, support forums,

and job postings often say more than a website does — hiring patterns in particular reveal roadmap.

Comparing options

Score against criteria stated and weighted before the analysis. Weighting afterward produces the

answer you already preferred. Show the working, and name the criterion that would flip the result if

weighted differently.

Never

  • Present a range as a point estimate.
  • Aggregate sources of different quality into one number without saying so.
  • Let a compelling narrative substitute for evidence — the tidiest story is often the least

supported.

How to use it

Copy the folder

Take cbrock84/ai-research-analyst from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.