event-description-generator

name	event-description-generator
description	Orchestrate event description audits by delegating chunk work to the event-descriptions-worker subagent. Resolve a project name to projectId via get_context when needed, then spawn worker subagents over cursors for a bounded run window and write outputs into run-scoped directories. Use when auditing missing event descriptions at scale without doing per-event analysis directly in this skill.

Event Description Generator

Run this skill as an orchestrator only. Do not perform per-event filtering, repo search, or description-writing logic in this skill body. Delegate chunk processing to the event-descriptions-worker subagent.

Workflow

1. Resolve project input (projectId or project name)
2. Create a run ID with short git SHA (<projectId>-<sha>)
3. Create run directories under runs/
4. Determine cursor plan (cursorStart, maxEvents, chunk size)
5. Spawn event-descriptions-worker subagents for cursor chunks
6. Collect worker summaries + output paths
7. Compress the run into a single CSV
8. Report concise progress and next cursor

Execution Rules

• Keep this skill as a dispatcher; the worker does the heavy lifting.
• Do not call set_event_metadata from this skill.
• Do not manually re-implement worker filtering/search logic here.
• Preserve user control over scope (project, cursor range, chunk size, parallelism).

Prerequisites

The user must provide either:

• projectId, or
• project name to resolve.

If neither is provided, stop and ask.

Phase 1: Resolve Project

1.1 If projectId is provided

Use it directly.

1.2 If project name is provided

Call get_context and resolve the name to a single projectId.

• If exactly one clear match exists, continue with that ID.
• If multiple matches exist, ask the user to choose.
• Do not choose an ambiguous match on behalf of the user.

1.3 If project name is not provided

Prompt the user to input the project name or ID and halt.

Phase 2: Build Cursor Plan

Default values unless the user specifies otherwise:

• cursorStart: 0
• maxEvents: 400
• eventsPerWorker: 50
• maxParallelWorkers: 4

For maxEvents:

• Compute total chunks as ceil(maxEvents / eventsPerWorker).
• Process at most maxEvents events starting at cursorStart.
• For the final chunk, pass a reduced eventsPerWorker if needed to avoid exceeding maxEvents.

For each chunk:

• Worker input cursor = current cursor
• Next planned cursor = current cursor + eventsPerWorker

Phase 3: Initialize Run Directory

Before launching workers:

1. Get the short git SHA by running git rev-parse --short HEAD in the workspace root.
2. Create runId = <projectId>-<sha>.
3. Ensure these directories exist:
   • .agents/skills/event-description-generator/runs/
   • .agents/skills/event-description-generator/runs/<runId>/

Phase 4: Spawn Worker Subagents

For each planned cursor, invoke event-descriptions-worker with:

• projectId (resolved)
• cursor
• runId
• eventsPerWorker (optional per-worker chunk size; pass when not default)

Worker output file convention:

• .agents/skills/event-description-generator/runs/<runId>/event-descriptions-<cursor>.csv

4.1 Parallelization guidance

• If user asks for one cursor, run one worker.
• If user asks for multiple cursors/range, run workers in parallel up to maxParallelWorkers.
• For large ranges, run in waves (bounded concurrency), then continue until limit reached.

Phase 5: Collect Worker Results

From each worker response, capture:

• events fetched
• events written
• output CSV path
• next cursor
• any errors

Phase 6: Compress Run

After all workers have completed, run the compress script to merge all chunk CSVs into a single file, keeping only rows with a suggested_description:

python3 .agents/skills/event-description-generator/scripts/compress-run.py <runId>

Capture the script's stdout to include compression stats in the report. If the script fails, note the error but do not block the report.

Phase 7: User-Facing Report

Return a short summary:

Event description generator dispatch complete for project {projectId}
- Run ID: {runId}
- Workers run: {n}
- Total fetched: {sum_fetched}
- Total written: {sum_written}
- Compressed output: .agents/skills/event-description-generator/runs/{runId}/{runId}-{YYYY-MM-DD}.csv
  - Rows with descriptions: {compress_written}
  - Rows filtered (no description): {compress_filtered}
- Next cursor to continue: {max_next_cursor_or_last_next_cursor}
- Errors: {none_or_list}

If the user never specified writing the descriptions, ask if they would like to write the suggested descriptions to Amplitude.

Phase 8: Write Descriptions to Amplitude

IMPORTANT: Only execute this phase when the user has explicitly asked to write descriptions.

Fixed constants (not configurable):

• eventsPerWriter: 100
• maxParallelWriters: 4

8.1 Locate the compressed CSV

Use the compressed output path reported in Phase 7:

.agents/skills/event-description-generator/runs/<runId>/<runId>-<YYYY-MM-DD>.csv

If the file does not exist (e.g. the user is resuming a previous run), ask the user to provide the path and halt.

8.2 Build writer plan

Run the writer-plan script to count data rows and compute chunks in one step:

python3 .agents/skills/event-description-generator/scripts/writer-plan.py <csvPath>

The script outputs JSON with all the information needed to spawn writers:

{
  "csvPath": "...",
  "totalDataRows": 137,
  "eventsPerWriter": 50,
  "totalChunks": 3,
  "chunks": [
    { "index": 0, "lineStart": 1, "lineEnd": 50 },
    { "index": 1, "lineStart": 51, "lineEnd": 100 },
    { "index": 2, "lineStart": 101, "lineEnd": 137 
  

Parse the JSON output and use the chunks array to drive step 8.4.

8.3 Spawn writer subagents

For each planned chunk, invoke event-descriptions-writer with:

• projectId (resolved in Phase 1)
• csvPath
• lineStart
• lineEnd

Run workers in parallel up to maxParallelWriters. For large files, process in waves (bounded concurrency) until all chunks are complete.

8.4 Collect writer results

From each worker response, capture:

• written: events successfully written
• skippedRows: rows filtered due to missing fields
• success: true/false
• errors: any tool errors

8.5 Write report

Return a concise summary:

Description write complete for project {projectId}
- Source: {csvPath}
- Total data rows: {totalDataRows}
- Writers run: {totalChunks}
- Total written: {sum_written}
- Total skipped: {sum_skipped}
- Errors: {none_or_list}

Common Invocation Patterns

Single chunk

• Inputs: projectId=123, cursor=500
• Run ID generated once for this invocation
• Action: spawn one worker
• Output: one CSV + next cursor

Multi-chunk wave

• Inputs: projectId=123, cursorStart=0, maxEvents=100, eventsPerWorker=20
• Planned cursors: 0, 20, 40, 60, 80
• Action: spawn workers with bounded parallelism
• Output: five CSVs under one run directory, continuation cursor 100

References

• Audit worker definition: .cursor/agents/event-descriptions-worker.md
• Writer worker definition: .cursor/agents/event-descriptions-writer.md
• Compress script: .agents/skills/event-description-generator/scripts/compress-run.py
• Legacy search heuristics (if needed for worker evolution): search-playbook.md