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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
End-to-end JD processing workflow: normalize raw JD, research company, score fit/gap, generate analysis reports, and import to opportunity tracker. One-shot skill that orchestrates the full analysis and opportunity import.
Reads a raw JD and produces a YAML snippet for opportunities.yaml plus optional jd_catalog.csv row. Use when adding a new pipeline opportunity from data/raw, ingesting a JD file, or bootstrapping opportunity id and next_action fields.
Produces next-steps markdown for a single opportunity from opportunities.yaml using opportunity_report_next_steps_one template. Use when the user asks for one-opportunity next actions, 24–72h moves, or snapshot for a given id.
Builds sorted rollup of next actions across all active opportunities using opportunity_report_next_steps_rollup template. Use when user asks for combined backlog, rollup next steps, or sorted pipeline todos by date.
Builds the full opportunity pipeline markdown report from opportunities.yaml using opportunity_report_tracking template. Use when the user asks for tracking list report, pipeline snapshot, or opportunity-tracking markdown output.
Updates one opportunity in opportunities.yaml (stage, next actions, close outcome, history). Use when the user changes pipeline stage, notes, next_action, or closes an opportunity by id.
| 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. |
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:
Target (collect, confirm, or ask):
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.
After company data, compare to master.yaml (no PII):
career.target_titles and career.direction_summary?docs/framework/fit-weights.md — distributed & execution, system design, ownership & cross-team delivery, AI in production (if JD/team), domain, seniority, practical constraints.work_mode, location, deal_breakers (e.g. outsourcing-only/body-shop if signals appear).work_mode in master.yaml?deal_breakersdeal_breakers in master.yamlPick one:
Include:
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.