How does the agent build a compensation benchmark?

It first matches role scope and level, then normalizes geography, currency, period, and pay components while recording source method, freshness, coverage, and confidence.

Will it provide current market salary numbers automatically?

Only when authorized sources or valid data are available. It will not invent ranges and will make the source date, methodology, and limitations explicit.

Can it make individual pay decisions or analyze identifiable employee data?

It is not an autonomous pay-decision tool. Identifiable employee data should be used only with proper authorization and access controls; involve qualified compensation, HR, and privacy reviewers.

Compensation Market Intelligence

Example session with this skill installed

Create an illustrative benchmark plan for a Senior Data Analyst in Toronto, Canada. We have no licensed salary survey extracts or verified market sources attached. Specify the role-match criteria, geography and currency controls, compensation components, suitable source types to gather, freshness checks, confidence rules, and a report template. Do not make up salary numbers or imply that you researched live market data.

Read your context and instructions
Compiled the compensation market intelligence

Benchmark plan — Senior Data Analyst, Toronto

Status: Research plan only. No salary data or live sources were supplied or consulted; no compensation range is reported.

Role-match profile

Confirm scope before collecting comparables: analytical ownership, SQL and statistical depth, experimentation, dashboarding, stakeholder influence, domain specialization, years and type of experience, decision authority, and whether the role mentors others. Match by work and level rather than title alone.

Geography and time controls

Target labor market: Toronto, Ontario; record whether source data represents city, province, national, or remote-Canada populations.
Currency: CAD; record the source currency and conversion date for any cross-border data.
Period: annual base and variable compensation; normalize hourly or monthly figures transparently.
Freshness: record effective or collection date, not just publication date; flag data outside the chosen recency window.

Compensation components

Collect separately: base salary; target and actual bonus; commission or other variable pay if applicable; equity grant value and vesting assumptions; allowances; employer-paid benefits; and total cash/total rewards. Do not add unlike components into a single figure without defining the calculation.

Source plan and evidence ledger

Source type	What to capture	Quality checks
Licensed compensation survey	Job match, sample size, percentile, effective date, industry and organization cuts	Confirm survey rights, methodology, respondent population, and aging factor
Employer-posted ranges	Role scope, level, location, date, pay components	Verify posting date, currency, and whether range is base-only
Recruiter or employer disclosures	Context, role match, date, sample method	Label anecdotal; do not treat as representative market evidence
User-authorized internal data	Level, geography, component definitions	Restrict access; validate completeness and lawful/approved use

Confidence method

Rate role match, geography match, source quality, sample coverage, freshness, and component comparability separately. Use a range only when adequate comparable evidence exists. Explain exclusions and gaps; do not convert low-quality samples into false precision.

Report template

Next steps

Confirm role scope and leveling, obtain authorized current sources, define a recency threshold, and have compensation and privacy owners review the method. Keep market references separate from individual pay decisions. No benchmark figures are asserted in this illustrative plan.

Connects securely to your tools. The creator never sees your data.

About this skill

Compensation Market Intelligence supports salary benchmarking, pay-range design, job pricing, leveling, geographic normalization, total-rewards analysis, market positioning, compensation analytics, and pay-equity support. It helps teams define the role before comparing data; distinguish base pay, bonus, commission, equity, benefits, and total cash; normalize geography, currency, and time period; and document source freshness, methodology, match quality, and confidence.

Use it to prepare market-reference ranges, benchmark job families, assess role-level matches, compare compensation structures, identify sparse or inconsistent evidence, and frame questions for compensation leaders. It separates external market pricing from internal pay decisions and keeps final decisions with accountable humans.

The agent does not invent survey data, salary ranges, exchange rates, employee pay, levels, geographic differentials, tax rates, statutory obligations, or vendor access. Internal equity analysis requires authorized employee data and careful handling. It is not a payroll system, employment lawyer, tax adviser, benefits broker, regulator, or autonomous pay-decision engine. Protect sensitive employee information and involve qualified compensation, HR, legal, and privacy reviewers.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Compensation Market Intelligence