Ingest Pipeline

Aggregates CRM/MAP data, cleans records, and prepares forecast-ready pipeline views.

Published by @gtmagents·0 agent reads / 30d·0 saves·Updated June 14, 2026

Command: ingest-pipeline

Inputs

timeframe – forecast period (Q1, Q2, FY, rolling-90).
segments – comma-separated list of segments or regions.
detail – summary | full output verbosity.
filters – optional stage, product, or owner filters.
refresh – optional flag to force data refresh vs cached snapshot.

Workflow

Data Extraction – pull CRM opportunities, bookings actuals, MAP signals, and enrichment fields by timeframe.
Normalization & QA – dedupe, enforce stage definitions, check close dates, align currencies, and flag missing data.
Scoring & Cohorting – compute coverage, velocity, win rate assumptions, and stage-based risk scores.
Segmentation – split pipeline by segment, region, product, partner, and channel.
Packaging – output structured tables + dashboards for forecast architect + finance partner review.

Outputs

Pipeline dataset (CSV/Sheets) with commit/upside/sandbag buckets.
Data quality log detailing issues resolved or pending.
Snapshot summary (coverage %, attainment, risk flags) per segment.

Agent/Skill Invocations

revops-analyst – runs extraction + QA steps.
forecast-modeling skill – provides scoring templates and coverage math.
variance-analysis skill – tags known risk cohorts for later tracking.

More on the bench

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customer-success+2

Strategic Planning

ACTIVATE when developing long-term strategy, setting OKRs, creating vision statements, building SWOT analyses, designing strategic frameworks, defining company direction, planning multi-year initiatives, or aligning organizational strategy with business goals. Executive must think through market positioning, competitive advantage, and measurable strategic objectives.

product-management+2

Sharebench · Public bench

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1Pick your tool

# Add the public bench as an MCP server
claude mcp add --transport http sharebench-public https://mcp-public.sharebench.ai

# Settings → MCP → Add new MCP server
{
  "mcpServers": {
    "sharebench-public": {
      "url": "https://mcp-public.sharebench.ai"
    }
  }
}

# Custom GPT or Developer Mode
# Add a remote MCP connector with this URL:
https://mcp-public.sharebench.ai

# Append to ~/.codex/config.toml
[mcp_servers.sharebench_public]
url = "https://mcp-public.sharebench.ai"

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