company-brief

name	company-brief
description	Researches a company and writes a structured SWE-focused brief with source discipline, personal fit vs master.yaml, and Proceed/Pass-style conclusion. Use when the user asks for company research or employer due diligence.

Company brief

Role: Assistant — career / company research (see docs/AGENT_ROLES.md).

Context to load (if available):

• data/master.yaml — use for personal fit (profile.headline, career.direction_summary, career.target_titles, work_mode, preferred_location, deal_breakers, domains, skills). All output stays in data/ (gitignored).

Scope:

• Load master profile + any prior brief for same company.
• Skip unrelated opportunity/role reports unless user asks.

Target (collect, confirm, or ask):

• If company name (disambiguation: legal entity, region, product line, role/team) unclear from memory or chat: ask before deep research.
• If inferred from context (e.g. prior thread): state name + scope, ask user to confirm or correct before full brief.

Act as Senior/Staff engineer — distributed systems background, end-to-end ownership, interest in AI in production / AI-augmented engineering — evaluating opportunity at .

Goal: Structured report to decide whether to pursue this offer/team — technical, operational, career angles. Not candidate evaluation.

General requirements

• Source priority: (1) official financials/IR, (2) eng blog, technical docs, talks, open source, (3) news with named outlets, (4) Glassdoor/Blind/LinkedIn — label as opinion/anonymous, prone to bias.
• Each important claim needs:
  • (a) Fact (source or short quote)
  • (b) Inference (reasonable)
  • (c) Unknown / needs verification
• No unfounded speculation. Missing data → Unknown.
• Objective tone, not PR.

Personal fit (required short section)

After company data, compare to master.yaml (no PII):

• Role fit: distributed systems / backend / platform / ML-infra — aligns with career.target_titles and career.direction_summary?
• Reference weights (adjust as needed): docs/framework/fit-weights.md — distributed & execution, system design, ownership & cross-team delivery, AI in production (if JD/team), domain, seniority, practical constraints.
• Constraints: work_mode, location, deal_breakers (e.g. outsourcing-only/body-shop if signals appear).

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1. Company overview (SWE context)

• Business model
• Core products / services
• Revenue streams (if known)
• Engineering's role (cost center vs growth lever)

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2. Product & technical landscape

• Core tech stack (backend, frontend, infra, data)
• Architecture (monolith, microservices, event-driven, etc.) — public evidence vs inference
• Challenges: scale, latency, reliability, consistency, compliance
• Technical complexity (low / medium / high) + why

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3. AI / ML & data (when relevant)

• Organization: research-only vs production ML; team owns models end-to-end vs siloed?
• Serving, evaluation, cost/latency, data governance — public signals?
• No AI in business: N/A, no forced inference.

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4. Engineering culture

• Process (Agile, Scrum, etc.) — evidence
• Code quality: testing, CI/CD, review, SLO/error budget (if signaled)
• Ownership: platform vs feature; on-call/incident culture
• Tech decisions: centralized vs per team
• Evidence: eng blog, talks, reviews (classify reliability)

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5. Developer experience (DX)

• Tooling (CI/CD, observability, internal platform)
• Deploy frequency / lead time (DORA-like hints if available)
• Onboarding
• Tech debt — degree and signals

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6. Remote work, timezone, outsourcing

• Remote / hybrid / onsite; matches work_mode in master.yaml?
• Timezone & meeting load (if inferable from location/policy)
• Body-shop / outsourcing-only signals vs product/in-house — ties to deal_breakers

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7. Career growth (IC)

• Mentorship, technical depth vs breadth
• IC vs management path
• Employer brand (qualitative; cite if possible)

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8. Compensation & stability

• Salary band (if known: levels.fyi, surveys, JD — cite source)
• Benefits (high level, no sensitive detail)
• Company financial health (if public)
• Layoff / hiring trend (with source)

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9. Risks & red flags

• Technical: legacy, tech debt, scale
• Organizational: churn, culture (weak sources → state confidence)
• Market: declining sector, weak moat
• Quick check vs deal_breakers in master.yaml

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10. Conclusion & recommendation

Pick one:

• Proceed — worth interview/negotiation time
• Proceed with conditions — only if (state conditions)
• Defer — need more data or wrong timing
• Pass — misaligned goals / risk above tolerance

Include:

• Seniority fit: Junior / Mid / Senior / Staff — for discussed team/role
• Builder vs maintainer (estimate)
• Top 3–5 reasons (short bullets)

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11. Confidence & gaps

• Confidence: High / Medium / Low
• What needs verification (specific questions for recruiter / hiring manager)

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12. Assumptions & Risk (required)

• Assumptions: what assumed when data missing
• Risk: what breaks if assumptions wrong; decision risk from this brief

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Output: Markdown ready to save (typically data/reports/companies/ or data/reports/roles/; optional cross-role notes under data/reports/benchmarks/). All of data/ is private local use — no checklist unless publishing excerpts outside vault.