Data Informed Planning

by pendo-io340d503c23eeNo licenseListed Oct 8, 2026Updated Oct 8, 2026

Use when planning a product feature, fix, or prioritization task where Pendo usage data, signals, issues, or customer feedback could ground the plan in real product behavior — e.g. "plan a fix for the pricing-page dropoff", "what should I build next", "improve the welcome flow". Skip for pure code refactors, infra, or tooling work.

AI-generated overview

Gathers Pendo and Novus product-usage data to ground feature, fix, or prioritization plans in real behavior.

What it does
This skill acts as a composition layer over an existing planning skill. It runs a precheck on two MCP servers, classifies the request intent, gathers relevant signals, issues, feedback, and metrics in parallel, and condenses them into a short Data Context block with caveats. It then hands the original request plus that block to the planner, or writes the plan itself if the planner is unavailable.
When to use it
Use it when planning a user-facing product feature, fix, launch, deprecation, or prioritization task and you want usage data, signals, issues, or customer feedback to inform the plan. Skip it for pure code refactors, infrastructure, build, CI, tooling, or documentation work, or when the relevant data is already supplied.
Requirements
Requires the Novus MCP server and the Pendo MCP server from the pendo-analytics plugin, each authenticated once via /mcp, plus network access to those services. It optionally delegates to the superpowers:writing-plans skill if the superpowers plugin is installed. It ships no scripts; it is instructions only.

Data-Informed Planning

Overview

A composition layer over /superpowers:writing-plans. Gathers relevant Novus + Pendo MCP data first, then hands the planner a "Data Context" block so the resulting plan is grounded in real usage, not assumptions.

Core principle: Don't reinvent planning. Inject product reality into the planner that already exists.

Prerequisites

This skill requires both MCP servers that ship with the pendo-analytics plugin:

  • Novus MCP (server name novus) — artifact graph, signals, issues, product wiki, opinionated metrics
  • Pendo MCP (server name pendo-external) — Voice of Customer, NPS, PES, session replays, segments/accounts, visitor data, entity discovery

Both are auto-configured when the plugin is installed. Run /mcp once to authenticate each.

The skill delegates to the superpowers:writing-plans skill when available. Install the superpowers plugin alongside this one for best results. If superpowers is not installed, the skill will produce the plan directly using the gathered Data Context instead of delegating.

Step 0: Precheck (REQUIRED before classifying)

Verify both MCPs are reachable with cheap calls:

  • get_pendo_apps (Novus) — confirms Novus auth
  • list_all_applications (Pendo) — confirms Pendo auth

If either fails:

  • Tell the user which MCP is unreachable and how to fix it (run /mcp)
  • Offer to proceed with whichever MCPs are available, clearly noting the gap in the Data Context Caveats
  • Do NOT silently skip — the planner needs to know what data wasn't available

Also check whether /superpowers:writing-plans is available. If not, note it and plan to use the fallback in Step 4.

When to Use

Use when:

  • The request touches user-facing product behavior (feature, UX, fix, launch, deprecation)
  • The request asks "what should I work on" / "what's broken" / "where should I focus"
  • The request names a page, feature, funnel, journey, or guide
  • The user wants a plan but hasn't told you which data should inform it

Do NOT use when:

  • Task is a pure code refactor with no user-visible change
  • Task is infra / build / CI / tooling / docs
  • User already supplied the relevant data inline

If unsure, run the classifier (Step 1). If it returns gather: false, fall through to /superpowers:writing-plans directly.

Workflow

  1. Precheck — confirm both Novus and Pendo MCPs are reachable
  2. Classify intent — decide whether to gather data, and which tools to call (see classifier.md)
  3. Gather — call the selected MCP tools in parallel (single message, multiple tool uses)
  4. Summarize — produce a Data Context block (≤ 250 words + artifact IDs)
  5. Delegate — invoke /superpowers:writing-plans with <original request> + Data Context
  6. Stop — let the planner own the plan. Do not double-plan.

Step 1: Intent Classifier

See classifier.md (alongside this file). Output shape:

json
{  "intent": "feature | fix | prioritization | diagnosis | funnel-journey | non-product",  "gather": true,  "novus_tools": ["get_product_wiki", "list_signals"],  "pendo_tools": ["get_feedback_insights"],  "scope": { "appId": "...", "artifactId": "...", "name": "..." }}

Tool names are bare — matching the convention used by the other skills in this plugin. The source split (novus_tools / pendo_tools) exists for the precheck and the "prefer Novus when overlap" rule, not disambiguation; tool names don't collide between the two MCPs.

If gather: false → stop and call /superpowers:writing-plans directly.

Step 2: Gather

Call the selected MCP tools in parallel. Rules:

  • Always include get_product_wiki (Novus) for feature intent — gives the planner product structure
  • Prefer Novus over Pendo when both expose similar data — Novus responses are pre-processed and tied to the artifact graph the planner can reference. Reach for raw Pendo only when you need a slice Novus doesn't expose (segment/account breakdowns, VoC, sentiment, raw visitor queries)
  • Funnel and journey analysis live only in Novus — there is no Pendo MCP equivalent
  • If a tool returns empty, errors, or fails a product-line check (e.g. NPS, Session Replay, Agent Analytics not in subscription), note it in Caveats and move on — don't retry

Step 3: Data Context Block

Target ≤ 250 words. Use this template:

markdown
## Data Context (Novus + Pendo MCP — fetched <ISO timestamp>)
**Product:** <productName>  ·  **Scope:** <page / feature / funnel + id, or "app-wide">
**Relevant signals (N):**- <title> — <priority> · <one-line takeaway> · `<id>`
**Relevant issues (N):**- <title> — <one-line takeaway> · `<id>`
**Voice of customer (N items):**- <theme> — <count> mentions · <sample quote or sentiment> · `<feedback-id>`
**Engagement / sentiment:**- PES: <score> (<window>)  ·  NPS: <score> (<window>)- <metric>: <value>
**Caveats:**- <e.g. "tagging is misconfigured — page/feature metrics unreliable; see signal b7d4…">- <e.g. "Pendo MCP unreachable; VoC + PES not included">- <e.g. "NPS tool gated — subscription doesn't include NPS product line">

Always populate Caveats when:

  • A list_signals result surfaces a data-quality issue (broken tagging, stale metrics)
  • An MCP was unreachable in the precheck
  • A product-line gate blocked a tool
  • A tool returned empty when you'd expect data

The planner must know when not to trust the numbers.

Step 4: Delegate

If /superpowers:writing-plans is available, delegate:

/superpowers:writing-plans
<original user request>
---
<Data Context block>

The planner writes the plan. This skill ends here.

Fallback (superpowers not installed): If the superpowers plugin is not available, write the plan directly. Use the Data Context block to ground your plan in the gathered data. Structure the plan with: goal, key findings from the data, prioritized action items, success metrics, and risks/caveats. Keep the same data-driven rigor — the only difference is you're writing the plan yourself instead of delegating.

Quick Reference

Tool names are bare. Prefer Novus for overlapping data; pull Pendo for VoC, sentiment, segment/account slicing, or session-level diagnostics.

IntentNovus toolsPendo toolsMust-include
featureget_product_wiki, list_signals, get_related_artifactsget_feedback_insights, get_ideas, productEngagementScorewiki + (insights OR ideas)
fixlist_issues, list_signals, get_page_metrics/get_feature_metricssessionReplayList, devlogEvents, visitorQueryissues
prioritizationlist_signals, list_issues, get_headline_metricsget_feedback_insights, productEngagementScore, npsScoresignals + issues + insights
diagnosisget_page_metrics, get_feature_metrics, get_funnel_analysis, list_signalssessionReplayList, visitorQuery, segmentListmetrics + signals
funnel-journeyget_funnel_analysis, get_journey_analysis, get_related_artifacts(none — Novus owns funnel/journey)funnel/journey analysis
non-product(none)(none)— bail to planner

Common Mistakes

  • Skipping the precheck — silent MCP failure = silently bad plans. Run Step 0.
  • Double-planning — writing a plan yourself after gathering. STOP, hand off.
  • Sequential MCP calls — always parallel; latency compounds.
  • Pulling raw Pendo when Novus has it — Novus responses are linked to artifacts the planner can reference. Pendo raw is for breakdowns Novus doesn't cover.
  • Retrying gated tools — if NPS / Session Replay / Agent Analytics tools return product-line errors, the subscription doesn't include them. Note in Caveats and move on.
  • Ignoring data quality — surface tagging warnings, missing-MCP warnings, and product-line gates in Caveats.
  • Over-gathering — stick to the classifier's tool list; the planner doesn't need raw event dumps.
  • Skipping classifier on "obvious" requests — "refactor the dashboard component" reads product-y but is code work; classifier correctly returns non-product.

Red Flags — STOP

  • About to call >6 MCP tools → over-gathering; narrow scope first
  • About to write the plan yourself → call /superpowers:writing-plans
  • Gathering when intent is non-product → classifier said skip
  • Data Context >250 words → compress; link IDs, don't paste blobs
  • Skipped precheck and one MCP is dead → output will mislead the planner; run Step 0
  • A Novus tool name appears in pendo_tools (or vice versa) → swap to the correct array

Source and attribution

Source:pendo-io/claude-pendo-plugininplugins/pendo-analytics/skills/data-informed-planningat commit340d503

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal