| name | forecast-retention |
| description | Parse YouTube retention-curve coordinates to predict audience drop-off hotspots for a video. Use when DS-Star must forecast where viewers will leave so the Narrative/Retention agents can fix pacing before upload. This is statistical work — compute in Python/pandas, call the model only to interpret (spec §11.3 computation-first). |
| metadata | {"agent":"ds-star","source":"Project Atlas Agent Skills Manifest §7","layer":"executive-strategy","production_host":"railway"} |
forecast-retention
Predict drop-off hotspots from retention data.
When to use
- A video has analytics (or a draft has a comparable-cohort baseline) and you need drop-off risk.
Function signature (manifest contract)
def forecast_retention(video_id: str) -> dict:
"""Parse retention-curve coordinates; predict audience drop-off hotspots."""
Inputs / Outputs
- Input:
video_id.
- Output:
{predicted_curve: [...], hotspots: [{timestamp_s, severity}], confidence}.
Computation-first (spec §11.3)
Curve parsing, smoothing, and hotspot detection are pure pandas at zero LLM cost. The model
is only used to write a one-line interpretation of a flagged hotspot — and only when something
unusual is found.
Implementation
Call the hermes-bridge MCP tool run_judgment_agent(insight_type="retention", query=<ask>, context={"video_id": video_id}). Hits Hermes's POST /v1/agents/retention (the real directory is hermes/agents/retention/, not forecast-retention -- using this skill's own name as the insight_type previously missed it and silently fell back to an empty/generic response). Note: the TS ds-star-scientists Temporal activities (server/src/modules/temporal/activities/ds-star-scientists.activities.ts) don't currently call a forecast-retention/retention scientist at all -- only 8 others are wired there -- so this fix only affects this OpenClaw skill's own path, not a parallel Temporal one.
Backend dependency
- YouTube Analytics API +
story_scores/retention tables (Railway) feed Hermes's own data pipeline, not this skill directly.
- Production host is Railway (colocated with the graph/vector store, spec §11.1), not the VPS.
Model
Mostly none (Python). Optional interpretation: gemini-direct/gemini-2.5-flash on flagged exceptions only.
