Ad-Hoc Analysis

Ad-hoc analyses are deep-dive investigations that cross-reference multiple data sources and produce a written report with findings. The durable user-facing result is a dashboard. Answer in chat first unless the user explicitly asks to save the result, create a reusable artifact, or re-run/update an existing saved analysis.

When to Use

Use the ad-hoc analysis workflow when:

• The user asks a complex question that requires data from multiple sources
• The investigation involves cross-referencing (e.g., CRM deals matched against call recordings)
• The user explicitly asks for an "analysis" or "deep dive"

Save a reusable dashboard artifact when:

• The user explicitly asks to save, create, publish, or re-run a saved analysis
• The user is already viewing or re-running an existing saved analysis
• The output needs a durable artifact because it includes generated chart images or a reusable refresh workflow

For one-off questions and exploratory deep dives, query the data and answer in chat. Do not create a dashboard or call the legacy save-analysis action just because the user said "analysis" or "deep dive".

If the user asks for an analysis output that needs a bespoke interactive surface, custom visualization, multi-step workflow, or UI that cannot be faithfully represented by native dashboard panels, create an extension and immediately embed it in the dashboard as one or more chartType: "extension" panels with config.extensionId. Never leave the extension as a standalone Analytics result or direct the user to an Extensions page.

Workflow

Step 1: Understand the Question (catalog-first, clarify-first)

Orient before gathering data. Consult the preloaded <resource scope="analytics-catalog"> references (else one find-data) and data-source status first to see which sources are configured and which one owns each fact, then settle scope:

• What is being analyzed? (deals, users, campaigns, errors, etc.)
• What time range?
• What data sources are relevant? (map each fact to the one source that owns it)
• What output does the user expect? (summary, ranking, comparison, trend)

If the metric definition, date range, or grain is ambiguous and a wrong guess would change the numbers, use the ask-question clarifying tool (multiple-choice) before gathering data. Ask at most once per turn, and skip it when the dictionary or the user already answered.

Step 2: Gather Data from Multiple Sources

Use the available actions to pull data. Read the relevant .agents/skills/<provider>/SKILL.md before querying each source.

Common data source combinations:

Analysis type	Data sources
Deal/account deep dive	account-deep-dive bundle, then targeted HubSpot/Gong follow-up
Sales pipeline analysis	HubSpot deals + Gong calls + Slack mentions
Customer health check	HubSpot deals + Pylon support tickets + BigQuery usage events
Content performance	BigQuery pageviews + GA4 + SEO keywords + HubSpot signups
Engineering velocity	GitHub PRs + Jira tickets + BigQuery deploy events
Churn investigation	Stripe billing + HubSpot deals + Pylon tickets + BigQuery usage

Tips for data gathering:

• Start with the primary source (e.g., HubSpot for deals), then enrich with secondary sources
• For the CRM side of named deal/account deep dives, call account-deep-dive first (ask what was said in Gong calls with run-gong-native-insight) with the account, company, domain, deal, or opportunity name. It returns HubSpot deals, associated companies/contacts/tickets/notes/emails, Gong call detail, compact transcript excerpts, coverage counts, and gaps. Use targeted hubspot-records or gong-calls follow-ups only when that bundle leaves a specific gap.
• Structure named deal/account reports as: executive summary, company/deal overview, key contacts and roles, dated timeline, Gong evidence with call dates/titles, current state, risks/blockers, recommended next steps, and methodology/gaps. Do not answer from an all-deals dump or Gong metadata alone.
• Use action filters such as query, properties, objectType, company, and limit to narrow results before cross-referencing
• For HubSpot deal cohorts, use hubspot-deals structured filters (product, pipeline, closedStatus, closedDateFrom, closedDateTo) for the cohort definition. Do not use query when the user names a specific HubSpot field such as products.
• When any first-class provider action is too narrow, use provider-api-catalog / provider-api-docs and then provider-api-request against the provider's real HTTP API. Do not weaken the analysis just because the convenience action is missing an argument.
• When stitching identities across sources, follow cross-source-analysis: match on BOTH a stable id AND email (ids can be reassigned), de-duplicate, and record match quality. Email/company-name/domain matches alone are low-confidence — flag them as caveats, not headline numbers.
• If a data source is not configured, mention what's missing and work with what's available — never invent rows to fill a gap.

Step 3: Analyze and Synthesize

Don't just dump raw data. Synthesize findings:

• Identify patterns, trends, and outliers
• Calculate key metrics (totals, averages, rates, distributions)
• Rank or categorize items when useful
• Call out surprises or actionable insights
• Compare against benchmarks or prior periods when possible
• Only report figures you actually retrieved from a source — never present a number you did not query. Attribute each figure to its source and time window.
• Make the evidence trail explicit enough to audit: source(s), time window, filters, sample size or row count, join/match method, caveats/gaps, and recommended next action when useful.
• Unstructured source records (Pylon tickets, Jira issues, Gong calls and transcripts, Slack messages) are valid evidence: code them for themes, mention counts, sentiment, and objections, state the inspected sample size, and do not imply unsupported statistical certainty.
• The words all, total, or exact do not by themselves call for cross-source validation when one structured query fully covers the requested source and filters.
• If the user challenges coverage, asks why more records were not included, or asks for the updated answer, rerun the source query or revise from the corrected cohort and put the updated answer in the response. Do not claim a dashboard was revised unless the revised answer is saved with update-dashboard or mutate-dashboard.

Step 4: Generate Charts (when useful)

When the analysis benefits from a visual — trends over time, distributions, or comparisons between categories — query the data first, then use the live /chart embed described in data-querying for an in-chat answer. Do not call generate-chart for a one-off chat result: it produces a static image for saved artifacts, requires pre-stringified JSON, and does not count as a real data query for the final response guard.

For a stacked multi-series bar chart, keep the query in long form and emit a panel with chartType: "bar", config.pivot containing xKey, seriesKey, and valueKey, plus config.stacked: true. The live chart route pivots the rows and renders one stack per x-axis category. See data-querying's "Inline Charts In Chat" section for the exact embed fence and encoding.

Only use generate-chart when a saved analysis artifact explicitly needs a static image. If it returns an error while answering in chat, switch to the live embed instead of retrying reformatted labels or data parameters.

You can include multiple live charts in one analysis. Reach for a chart when it communicates the finding faster than a table - don't force visuals on every analysis. Include the query and embed configuration in saved instructions so re-runs produce fresh charts.

Step 5: Format Results as Markdown

Structure the report clearly:

## Key Findings

- **Finding 1**: Specific insight with supporting numbers
- **Finding 2**: Another insight
- **Finding 3**: Actionable recommendation

## Summary Metrics

| Metric               | Value   |
| -------------------- | ------- |
| Total deals analyzed | 54      |
| Average deal size    | $42,300 |
| Win rate             | 23%     |

## Detailed Analysis

### Category 1

[Detailed breakdown with tables, lists, etc.]

### Category 2

[More detail...]

## Methodology

Data sources: HubSpot (deals, contacts), Gong (calls), Slack (mentions)
Time range: Jan 1 – Mar 31, 2026
Filters: S1+ pipeline, closed-lost only

Step 6: Save the Dashboard Artifact (only when requested)

Call update-dashboard with a complete dashboard config when the user asks for a saved/re-runnable analysis or this turn is creating a durable report. If the requested report needs bespoke UI, create the extension first and include its id in an extension panel.

update-dashboard
  --dashboardId "closed-lost-q1-2026"
  --config '{"name":"Q1 2026 Closed-Lost Analysis","panels":[{"id":"findings","title":"Findings","source":"first-party","chartType":"table","width":2,"sql":"...","config":{"description":"Evidence-backed report for the requested cohort."}}]}'

The dashboard config must preserve compact evidence from the real data-source action results you used: row samples, aggregate metrics, match decisions, call/message IDs, short transcript/message excerpts, coded themes, sentiment labels, and explicit provider errors for any gaps. Do not include full Gong transcripts, full tool outputs, or raw provider payload dumps. If you cannot query a source, do not save guessed dashboard content; report the unavailable/error result instead.

Critical: Write good dashboard definitions. The dashboard description/config is what the agent uses on re-run. Be specific:

• Which actions to call with which parameters
• What filters to apply
• How to match records across sources
• What metrics to calculate
• What structure the output should have
• End with "Update dashboard with id='...'"

Step 7: Navigate to the Result

After saving, navigate the user to see the saved dashboard:

navigate --view=adhoc --dashboardId=closed-lost-q1-2026

Re-Running an Analysis

When a user clicks "Re-run" on a saved analysis, the agent receives:

• The original question
• The saved instructions (step-by-step)
• The analysis ID to update

Follow the instructions to gather fresh data, then call update-dashboard with the same dashboardId to update the panels/report. Existing legacy analysis deep links remain readable, but new durable results must be written to dashboards.

If the user challenges the coverage of a chat answer or saved analysis ("why aren't you pulling more deals?", "where is the updated response?"), rerun the source query or revise from the corrected cohort and include the updated deliverable in the response. Do not summarize that a revision exists without showing it or saving it.

Actions Reference

Action	Purpose
update-dashboard	Save or update the dashboard artifact and its panels
get-sql-dashboard	Retrieve a dashboard by ID
list-sql-dashboards	List dashboard artifacts
navigate	Navigate to a dashboard: --view=adhoc --dashboardId=<id>

Storage

Dashboard artifacts are stored in SQL and respect the normal dashboard access model. Legacy analyses remain in their existing SQL tables for compatibility and should not be used for new artifacts.

API endpoints (for UI consumption):

• GET /api/sql-dashboards — list dashboard artifacts
• GET /api/sql-dashboards/{id} — get one

Best Practices

1. Use descriptive IDs — closed-lost-q1-2026 not analysis-1
2. Include methodology — mention data sources, time ranges, and filters in the report
3. Write self-contained instructions — another agent (or the same agent in a new session) should be able to re-run from the instructions alone
4. Include structured data — pass compact resultData with metrics, rows, IDs, short excerpts, and coded themes so the UI can render richer views in the future
5. Keep reports scannable — lead with key findings, put details below
6. Note data gaps — if a source was unavailable or matching was imperfect, say so
7. Suggest next steps — end with actionable recommendations when appropriate
8. Answer in chat — never deflect — present tables, inline charts, and findings directly. Do not say "check the dashboard" or redirect elsewhere.

Large Fan-Out Analyses (30+ Accounts, Deals, or Calls)

For batch analyses spanning many items, chunk the work instead of trying to hold everything in one pass:

1. Define the cohort — fetch the full list of items (accounts, deals, calls) from the primary source (HubSpot, BigQuery, etc.).
2. Chunk and persist — process 5-10 items per iteration. For each chunk, write a short per-item findings note (key signals, gaps, theme tags) as an intermediate result to the dashboard definition or agent scratch.
3. Synthesize — after all chunks are complete, read the intermediate notes back in and produce the final cross-item synthesis (patterns, rankings, themes, recommendations).

This is the same pattern as a batch document analysis: fetch → process chunk → write intermediate → synthesize. The chunking keeps context manageable and each iteration independent.

After Completing an Analysis — Record Discoveries

After completing a significant analysis, record newly confirmed findings when they will help future work. First check whether a finding applies to the user's setup or to the whole organization. Keep setup-specific findings in personal memory or the current analysis. Ask the user and get approval before writing anything to shared LEARNINGS.md or organization memory.

Record approved findings such as:

• Metric definitions (how a metric is actually calculated in this dataset)
• Provider gotchas discovered during the analysis
• Schema discoveries (table names, column names, join patterns that worked)
• Identity-stitching rules confirmed across sources

resources(action: "read", path: "LEARNINGS.md")
resources(action: "write", path: "LEARNINGS.md", content: "<updated>")

Keep entries short and actionable. This is the learning flywheel — the next analysis benefits from what this one confirmed.