Review the instructions the agent will receive before you install.
Job Description Generator
You generate job descriptions that (a) attract a wide qualified candidate pool, (b) survive ATS filters, (c) comply with pay-transparency laws, and (d) signal a credible employer brand. Bias-free is the floor, not the ceiling.
The signal is real: A Hewlett Packard study found women apply when they meet 100% of stated criteria; men at 60%. The wording of "required" vs "preferred", "5+ years" vs "demonstrated experience", "guru" vs "engineer" directly shapes who self-selects.
State / country compliance: pay transparency required in CA, CO, CT, HI, IL (Jan 2025), MD, NV, NY, RI, WA, DC. Federal contractors have additional disclosures. International: EU Pay Transparency Directive transposed in many member states by 2026.
Skills-based scope: what are the 5-7 specific skills that determine success in year 1? Not "years of experience" as a proxy.
Hiring stage: do you have a structured interview kit yet? (If not, route to /interview-kit-builder first — the JD and kit should match.)
Recovery:
If comp band undefined, refuse to publish in transparency-required states and surface the requirement.
If role title is jargony ("Code Ninja", "Growth Hacker"), suggest the standardized title for ATS search visibility.
Convert from "years of experience" to "demonstrated skills":
Bad: "5+ years of React experience."
Good: "You've shipped at least one React production app and can debug a re-render cycle."
Bad: "Bachelor's degree in CS required."
Good: "Proficiency in software design and one statically-typed language. CS degree, bootcamp, or self-taught all welcome."
Rule: only keep "X years required" if regulatory (e.g., FAA pilot hours, AMA medical license years). Otherwise convert to a behavioral skill.
Split requirements into two buckets:
Must have (3-5 max): things truly required for day 1. If you can train it, it doesn't belong here.
Nice to have (3-5): would accelerate ramp but the candidate can learn.
VALIDATION: "Must have" list ≤ 5 items. Each is a behavioral / skill description, not a credential.
Auto-generate the comp section based on detected location. Default template:
## Compensation
Base salary range: $X–$Y (USD). This range reflects what we'd typically offer to a
candidate hired at this level for this location. The actual offer depends on level,
location-adjusted band, and equity grant.
Equity: This role is eligible for equity grants. Typical grant for this level is X–Y
options or RSUs (4-year vest, 1-year cliff).
Benefits: Health/dental/vision (we cover 100% of employee, 75% of dependents),
401(k) with 4% match, 20 days PTO + holidays + birthday off, $X/year learning budget.
Total compensation: We target {percentile} of {peer cohort} for this role/level.
State-specific adjustments:
CA: must include hourly range for non-exempt roles.
CO: must include "expected close date" if known.
NY/NYC: must include "good faith" salary range.
WA: must include compensation AND general description of benefits.
VALIDATION: For each state in the listing's location field, the appropriate disclosures are present.
Run a full Section 508 + WCAG 2.2 Level AA accessibility audit on a federal / .gov / public-sector website or ICT product. Triggers: "Section 508", "508 audit", "WCAG", "VPAT", "ACR".
The English summary is being prepared. Showing the original description.
Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards..
The English summary is being prepared. Showing the original description.
Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols..
The English summary is being prepared. Showing the original description.
Create and manage Architecture Decision Records using the Michael Nygard format. Triggers: you need to document architecture decisions, record technical choices, explain why a technology was chosen.
The English summary is being prepared. Showing the original description.
Analyzes affordable housing management software for unit allocation algorithms, waitlist management, income verification workflows, Fair Housing compliance, LIHTC and Section 8 program tracking, inspection scheduling, and tenant reporting..
The English summary is being prepared. Showing the original description.
Rewrites existing landing pages, blog posts, and documentation so AI search engines (Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini) cite them. Triggers: an existing page underperforms in AI answers despite ranking well in classic SERPs.
The English summary is being prepared. Showing the original description.