deep-research
This skill conducts structured investigations into Mixpanel metrics to uncover why data changed or what's driving a trend.
Identity proven by GitHub's verified organisation badge (mixpanel)
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
- License
- Apache-2.0 (permissive)
- Pin
- dfb891bpinned Oct 9, 2026
- Context cost
- about 930 tokenswhen the skill loads
- GitHub stars
- 15on mixpanel/ai-plugins
- Scripts
- None found
What it does
“Conducts a structured metric investigation in Mixpanel. Use when the user asks why a metric changed, what's driving a trend, requests a "deep dive" or "root cause," or wants to understand a phenomenon in their data. Walks through project / event / property scoping, plan confirmation, and an iterative query → interpret → hypothesise loop.”
Generated summary from the skill's own text
This skill conducts structured investigations into Mixpanel metrics to uncover why data changed or what's driving a trend. It guides the agent through scoping the analysis, validating data, running exploratory queries, and iterating through hypothesis testing until the root cause is identified with evidence.
When to use it
- User asks why a metric dropped, spiked, or changed unexpectedly
- User requests a deep dive or root cause analysis of data
- User wants to understand drivers behind a specific trend
- Investigating what caused changes in user behavior or feature adoption
When not to
- Simple one-off metric lookups that need direct answers only
- User lacks Mixpanel access or data unavailable
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add mixpanel/ai-plugins --skill deep-researchInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install mixpanel/ai-plugins deep-research@dfb891bb6f754b5dcab2131b7311e7ccb9bf8c3eInstalls the commit we checked (dfb891b).
Copy by hand
Download the folder at dfb891b and put it in .claude/skills/deep-research for Claude Code and in .agents/skills/deep-research for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "deep-research" by Mixpanel. Source, pinned to commit dfb891bb6f754b5dcab2131b7311e7ccb9bf8c3e: https://github.com/mixpanel/ai-plugins/tree/dfb891bb6f754b5dcab2131b7311e7ccb9bf8c3e/plugins/mixpanel/skills/deep-research 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/deep-research/ for Claude Code and .agents/skills/deep-research/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/mixpanel/deep-research
SKILL.md at the pin
---
name: deep-research
description: Conducts a structured metric investigation in Mixpanel. Use when the user asks why a metric changed, what's driving a trend, requests a "deep dive" or "root cause," or wants to understand a phenomenon in their data. Walks through project / event / property scoping, plan confirmation, and an iterative query → interpret → hypothesise loop.
license: Apache-2.0
metadata:
engine: required
---
# Deep Research / Metric Investigation
> **Engine required** — resolve an engine per [`ENGINE.md`](../../ENGINE.md): one named in the conversation or loaded instructions is mandatory (not set up → offer `/mixpanel:install` for it); otherwise use the Mixpanel MCP server, or offer `/mixpanel:install` if it's unavailable.
This skill is a structured investigation, not a one-shot answer.
## Requirements
- Access to Mixpanel (query schemas, run queries, manage dashboards).
---
## When to use this skill
Trigger when the user wants to understand _why_ something happened in their data. Common phrasings:
- "Why did [metric] drop / spike / change?"
- "Can you do a deep dive on [X]?"
- "What's driving [trend]?"
- "Root cause this for me."
- "Help me understand what happened with [feature / cohort / segment]."
Do **not** trigger for one-off lookups ("what was DAU yesterday?"). Those are direct queries, not investigations.
---
## Workflow
### Phase 1 — Scope
Do not run analysis queries until scope is confirmed.
Do your best to find the following information from the user's question and context. If anything is missing or ambiguous, ask clarifying questions before proceeding.
1. **Project.** Which Mixpanel project? If the user has access to several, ask.
2. **Events.** Which events relate to the question?
3. **Properties.** Which properties are relevant to break down by? (e.g. platform, utm_source, plan_tier)
State your assumptions and ask the user to confirm before continuing. The final answer depends on this being right.
### Phase 2 — Validate and plan
Run small exploratory queries to confirm data exists in the analysis window. Be resilient — try different approaches if your first attempts don't work. If volume is zero, partial, or anomalously low, surface that to the user before going further.
Then present a compact plan:
```
*Investigation Plan*
• *Project:* `project name`
• *Events:* `event_a`, `event_b`, `event_c`
• *Properties:* `platform`, `utm_source`, `plan_tier`
*Initial Queries:*
• Trend of event_a over 30 days to establish baseline
• Breakdown by platform to isolate where the change happened
• ...
Say *yes* to continue the analysis.
```
Wait for explicit confirmation before running the full investigation. If the user revises the plan, restate it and re-confirm before continuing.
### Phase 3 — Investigate
Enter the research loop:
1. **Run** one or more queries from the plan.
2. **Read and interpret** the results — what stands out, what doesn't?
3. **Form a hypothesis.** If the data clearly answers the question, prepare to summarise. If not, return to step 1 with a sharper query.
Continue until you can answer the original question with evidence, or you can clearly articulate what data is missing. Stay creative — every dataset is different, so let the data shape the next query rather than following a fixed sequence.
---
## Guidelines
- **Start broad, then narrow.** Establish the overall trend first; each subsequent query should be informed by the previous one.
- **Break down by dimensions where you'd expect variation given the question.** Don't slice by every property — pick the ones most likely to show a delta.
- **Correlate timing.** If a metric shifted on a specific date, ask what else changed: a deploy, a campaign, a policy, an outage.
---
## Output
When the investigation concludes, present:
1. The **answer** to the original question, in one or two sentences.
2. The **evidence** — create a dashboard using the `create-dashboard` skill to back your findings with live data.
3. **Caveats** — anything the data doesn't tell you, alternative explanations you can't rule out, and follow-up queries the user might want to run.
From mixpanel/ai-plugins at dfb891b, shown as plain text under its license: Apache-2.0 (permissive). The license text is in plugins/mixpanel/skills/deep-research/SKILL.md.
View SKILL.md on GitHub at dfb891bFiles
- SKILL.md4.2 KB · ce44e9a76cbc
1 file, 4.2 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
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