🍳

The Product Manager

Your PM copilot: pull Jira metrics, analyze user feedback, track feature requests, auto-prioritize the backlog, and prep sprint planning summaries.

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advanced⏱ 25 min setup🔄 12 swappable alternatives

🧂 Ingredients

🔌 APIs

Sprint data, backlog, velocity metricsrequired

sprint_data_backlog_velocity_metrics

🔄 Alternatives:

Linear — Faster, modern project managementGithub Issues — Built into your repoShortcut — Good middle ground, less bloated

Product usage analyticsrequired

product_usage_analytics

🔄 Alternatives:

Posthog — Open-source, privacy-friendlyPlausible — Simple, GDPR-compliantMixpanel — Better event/funnel analytics

User feedback and feature requestsrequired

user_feedback_and_feature_requests

🔄 Alternatives:

Zendesk — More robust ticketing systemFreshdesk — More affordableCrisp — Good free tier for startups

Prioritization analysis and summariesrequired

prioritization_analysis_and_summaries

🔄 Alternatives:

Anthropic — Better at analysis and reasoningGemini — Free tier, multimodalMistral — Open-weight, EU-hosted

📋 Step-by-Step Build Guide

STEP 1

Pull current sprint status from Jira (burndown, blocked items)

Pull current sprint status from Jira (burndown, blocked items)

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 2

Aggregate user feedback from Intercom (themes, frequency)

Process the data and calculate the requested metrics.

Steps:
1. Validate input data — check for nulls, out-of-range values, duplicates
2. Apply the calculation/aggregation logic
3. Compare against benchmarks or previous periods if available
4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts)

Include: current value, previous value, change (absolute and %), trend direction (↑↓→).
Flag any anomalies: values >2 standard deviations from the mean.

If insufficient data for a reliable calculation, state the minimum needed and return partial results.

STEP 3

Pull product analytics (feature adoption, drop-off points)

Pull product analytics (feature adoption, drop-off points)

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 4

Cross-reference feedback with existing backlog items

Process the data and calculate the requested metrics.

Steps:
1. Validate input data — check for nulls, out-of-range values, duplicates
2. Apply the calculation/aggregation logic
3. Compare against benchmarks or previous periods if available
4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts)

Include: current value, previous value, change (absolute and %), trend direction (↑↓→).
Flag any anomalies: values >2 standard deviations from the mean.

If insufficient data for a reliable calculation, state the minimum needed and return partial results.

STEP 5

Score backlog by impact (user demand × reach × strategic fit)

Score backlog by impact (user demand × reach × strategic fit)

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

STEP 6

Generate sprint planning brief with prioritized recommendations

Analyze the input and classify it into the defined categories.

Classification approach:
1. Extract key signals from the content (keywords, sender, urgency markers, topic)
2. Match against category definitions
3. Assign confidence score (high/medium/low)
4. For ambiguous cases, classify as the more important/urgent category (err on the side of caution)

Output for each item: { category, priority, confidence, reasoning }

If an item could belong to multiple categories, pick the primary one and note the secondary.

STEP 7

Track sprint velocity trends over time

Track sprint velocity trends over time

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

🤖 Example Agent Prompt

Pull current sprint status from Jira (burndown, blocked items)

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

Copy this prompt into your agent to get started.