research-company

research-company

A 360° brief on a company × role, grounded in real sources — every claim cites a URL. Hard rules: ../_shared/RULES.md.

Inputs

• company (required) — name or canonical key. Known-info gate (RULES §6): if not given inline, read jobs/current.json for company/role before asking.
• role (optional but strongly recommended) — title at the target level.
• Profile: profile/master-profile.md for the fit_for_user view (read context: for direction only; never echo).

Method (forked context — be liberal with WebSearch / WebFetch)

0. Region pack. Read knowledge/regions/<code>.md — comp norms (USD base+equity vs INR LPA CTC fixed+variable) and which community sources to weight (Glassdoor.com vs Glassdoor.in, r/india for IN-only).

1. Discovery sweep — site-restricted WebSearch queries (run a handful, don't be precious):

   • "<company>" compensation "<role>" levels.fyi OR teamblind OR glassdoor
   • "<company>" "<role>" "day-to-day" OR responsibilities OR "what we do"
   • "<company>" career growth OR promotion OR ladder OR "career path"
   • "<company>" news (last 12 months) — site:news.ycombinator.com OR site:techcrunch.com OR site:bloomberg.com OR site:theinformation.com

2. Community sentiment. Up to 5 results each from:

   • Reddit — r/cscareerquestions, r/ExperiencedDevs, r/csMajors, r/recruitinghell, and r/india / r/developersIndia for IN.
   • Blind (teamblind.com) — when accessible.
   • Glassdoor (regional). Each = { content (≤ 20 words), link, source, date? }. WebFetch the promising ones; quote sparingly.

3. News pass — WebSearch last-90-days news; surface 4–6 items with date + 1-line summary + link.

4. Compose the report per reference/report-schema.md:

   • compensation — salaryBenchmarks.{baseSalary, totalCompensation} (verbatim source strings) + benefitsOverview[].
   • jobSummary.{jobDescriptionSummary[], dayToDayResponsibilities[], careerGrowthOpportunities[]} — 4–6 bullets each, drawn from sources (not invented).
   • news.recentNews[] — 4–6 items.
   • discussions.{reddit[], blind[], glassdoor[]} — what people who work there say. Honest: positive AND negative.
   • red_flags[] — 0–4 specific, sourced concerns. "Glassdoor 2.8 with consistent WLB complaints (link)"; "Two CFOs in 18 months (TechCrunch, 2026-03)". Skip if nothing material.
   • fit_for_user[] — 2–3 bullets each linking a profile fact to a company fact. Reads context: for direction; never echoes.

5. Cite everything. Markdown report has links inline; JSON has explicit link / source fields per item, and a deduped sources[] at the root.

Output

• companies/<slug>.md — human-readable; sections mirror the JSON; "Sources" footer lists every URL.
• companies/<slug>.json — schema-compliant per reference/report-schema.md. Consumed by prep-interview, coach-negotiation.
• Console: 5-line summary — comp band · growth signal · top red flag · top fit · next suggested skill (prep-interview if pre-interview, coach-negotiation if pre-offer).

Anti-patterns

• Compensation ranges without a source. If none found, set to null; don't write "$180k–$220k" with no link backing it.
• News older than 18 months (unless the user is researching a founding / legal / deal story).
• Empty arrays filled with placeholder strings ("No information found"). Use [].
• Echoing context.career_goal / context.additional_info into fit_for_user.
• Pretending small samples are representative ("everyone on Blind says X" when there are 3 posts).
• Emoji.

Rubric

• [ ] Every claim in the markdown has a source URL.
• [ ] salaryBenchmarks are verbatim from sources; if absent, null, not invented.
• [ ] Sentiment is balanced (positive + negative) where both exist.
• [ ] red_flags are specific and sourced (or absent).
• [ ] fit_for_user cites both a profile fact and a company fact per bullet.
• [ ] sources[] at root deduplicates every URL used.

Next steps

Brief saved. Next: /prep-interview (pre-interview) or /coach-negotiation (pre-offer).