Expected impact
Potential effect on indexation, visibility, qualified clicks, conversion intent, or risk reduction.
SearchSignal methodology
The opportunity score should explain the work—not hide the logic.
A useful queue balances expected upside, delivery effort, evidence quality, freshness, and project context. SearchSignal uses that structure to help an agent and a reviewer agree on what belongs first.
01 · Source-awareKnow where the evidence came from.Measured performance, crawl findings, licensed metrics, and saved research remain distinguishable.
02 · Project-scopedKeep context attached to the workspace.Domains, audits, lists, priorities, and history stay connected instead of becoming another export.
03 · Agent-deliveredCall the workspace through MCP or HTTP.Use the same authorized evidence in Cursor, Claude, ChatGPT, Windsurf, VS Code, and custom agents.
04 · Human-reviewableInspect the decision before it ships.The interface remains available while the agent handles retrieval, synthesis, and handoff.
Four core inputs
High impact is not enough by itself.
The best next action balances upside with delivery reality and evidence quality. That prevents a large audit, keyword set, or backlink export from becoming another unranked backlog.
Effort
Estimated implementation cost, affected templates, coordination, and verification complexity.
Confidence
Strength, directness, consistency, and source quality behind the recommendation.
Freshness + fit
How current the signal is and how well the work aligns with the active project goal.
Illustrative model
Turn a finding into a defensible decision.
The example below is presentation data. It shows how reusable delivery, current evidence, and pages already close to page one can raise a technical fix above a broader but less certain idea.
Guardrails
The score guides review. It does not replace judgment.
Prioritization is credible only when the evidence, assumptions, and business context remain inspectable and overridable.
| Guardrail | Why it matters | Expected behavior |
|---|---|---|
| Expose supporting evidence | A score without evidence is not defensible. | Attach affected URLs, query movement, source dates, and assumptions. |
| Separate observations from estimates | Search-volume estimates are not direct performance. | Label source type, freshness, and confidence. |
| Allow business overrides | Launches, risk, revenue, and roadmaps can change priority. | Let a person change status and order. |
| Verify after implementation | A completed task is not automatically a successful outcome. | Re-crawl and monitor measured performance. |
Evidence → decision → delivery → proof
Put an explainable work queue inside the agent you already use.
Create a workspace, connect one authorized credential, and let the AI retrieve the evidence behind the next action.