| name | calculate-opportunity-score |
| description | Compute the Project Atlas weighted opportunity score for a topic from raw metrics (search, discussion, video, evergreen, emotional, competition, monetization, regional). Use when the Trend Intelligence agent must rank or classify a topic after signals are fetched. This is arithmetic — compute in Python, not via an LLM (spec §11.3 computation-first). |
| metadata | {"agent":"trend-intelligence","source":"Project Atlas Agent Skills Manifest §2","layer":"L1-trend"} |
calculate-opportunity-score
Turn raw metrics into a single ranked opportunity score + classification.
Formula (manifest §2)
Score = Search(20%) + Discussion(15%) + Video(15%) + Evergreen(15%)
+ Emotional(10%) + Competition(10%) + Monetization(10%) + Regional(5%)
When to use
- After
fetch-signals returns metrics for a topic.
- Backlog ranking / create-now vs create-later classification.
Function signature (manifest contract)
def calculate_opportunity_score(raw_metrics: dict) -> dict:
"""Returns {"topic": str, "final_score": int, "classification": str}."""
Inputs / Outputs
- Input:
raw_metrics dict with the eight weighted components (0–100 each).
- Output:
{topic, final_score, classification}.
Computation-first (spec §11.3)
This is a deterministic weighted sum — run it in Python at zero LLM cost. Only call the
model to write the one-line plain-language reason for a ranked item, not to compute the score.
Implementation
Call the temporal-bridge MCP tool start_workflow("calculateOpportunityScoreWorkflow", "trend-signals", [raw_metrics]), then poll get_workflow_result(workflow_id). Runs the weighted-sum formula above as a new calculateOpportunityScore Temporal activity (server/src/modules/temporal/activities/trend-signals.activities.ts) -- pure TypeScript arithmetic, zero LLM cost, matching this skill's own computation-first requirement.
Backend dependency
- Writes
content_opportunities / story_scores (Railway). Stubbed until wired -- the scoring call itself works today, it just isn't durably stored yet.
Model
No LLM needed for the score. Optional one-line reason: deepseek-direct/deepseek-chat.
