# Analysis Workspace

> How to use Resources-backed workspace files for large-scale multi-source analyses: scratch/ temporary staging, chunked batch processing with per-item memos, run-code aggregation, saveToFile for big API pulls, and synthesizing across files that exceed one context window.

- Skill: `builderio/analysis-workspace` (Agent Skill)
- Install (CLI): `npx skillmds@latest add builderio/analysis-workspace`
- Raw SKILL.md: https://api.skillmd.com/api/skills/builderio/analysis-workspace/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: Builder.io (https://skillmd.com/u/builderio)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/builderio/analysis-workspace

---


# Analysis Workspace

Analysis files live in the same Resources workspace users inspect and manage in
the app. Use normal path conventions:

- `scratch/...` for temporary staging files, per-item memos, raw API pulls, and
  other agent working data. These are hidden from the Resources view by default.
- Descriptive folders such as `analysis/q2-churn/` only for files the user
  should keep, inspect, or manage after the analysis.

The `run-code` helpers (`workspaceRead`, `workspaceWrite`, `workspaceAppend`,
`workspaceList`) and `saveToFile` write through this Resources-backed file
store. Use them to stage intermediate results that would overflow the context
window, then read them back selectively for synthesis.

## When to Use

- **Batch fan-out** with 30+ items (accounts, calls, deals, tickets, messages,
  documents, events): write a
  per-item memo file after each item, then synthesize across all memos in a
  final pass.
- **Large API payloads**: use `saveToFile` on `provider-api-request` or
  `web-request` to write a 20 MB dataset to `scratch/...` instead of returning
  it in context.
- **Provider-wide search/count/classification**: build a durable corpus first,
  then search or aggregate it with `run-code`. This is required when the user
  expects broad recall or when a negative answer such as "no mentions" would be
  misleading if based on a sample.
- **Multi-step analyses** that span multiple conversations or agent turns. Keep
  durable files outside `scratch/` only when the user wants to keep them.
- **run-code aggregation**: call `workspaceRead` / `workspaceWrite` inside a
  `run-code` block to load and process data that's too large to print as output.
- **Long compute**: for aggregation scripts that could exceed ~30 s (big
  cross-source joins, multi-page provider sweeps), run `run-code` with
  `background: true`. It returns `{ executionId, status: "queued" }`
  immediately and executes durably out-of-band (default 10 min budget),
  surviving the hosted run timeout. Continue other work, then poll with
  `run-code` `{ executionId }` for the persisted result. On `failed`/
  `timed_out`, chunk the work or persist intermediate progress with
  `workspaceWrite` and re-run.

## Delegated Analysis Handoff

Background sub-agents start with a clean context. A handoff for a large
analysis must include the exact staged dataset id or workspace path, the
records/chunk range to process, the output path, and the checkpoint or resume
operation. The child must read that staged input before making provider calls.

- If a readable staged file or dataset already exists, process it in place. Do
  not re-page the provider endpoint to recreate the same corpus.
- If the named staged input is missing or unreadable, stop with an explicit
  missing-input error. Do not silently fall back to a full provider sweep.
- For 500 or more Gong records, do not use `gong-calls(exhaustive=true)`, a
  per-call transcript loop, or a single `provider-api-request` `fetchAllPages`
  call as the analysis itself. If the term is a configured keyword tracker,
  stage `/calls/extensive` tracker results and reduce them with a Data Program
  or `query-staged-dataset`. Otherwise use `provider-corpus-job` with a staged
  call-id input and continue raw transcript retrieval in bounded, checkpointed
  batches.
- Write a compact result or progress marker after every completed chunk. A
  final answer written only after the whole corpus is processed is not a
  checkpoint.

## Workspace File Helpers

Inside `run-code`, use the workspace helper functions:

| Helper                                        | Use                                        |
| --------------------------------------------- | ------------------------------------------ |
| `workspaceWrite(path, content, contentType?)` | Create or overwrite a file                 |
| `workspaceAppend(path, content)`              | Append text to a file                      |
| `workspaceRead(path, opts?)`                  | Read content, with optional paging         |
| `workspaceReadMeta(path, opts?)`              | Read content plus metadata/truncation info |
| `workspaceList(prefix?)`                      | List files under a prefix                  |

- `read` supports `{ offset, maxChars }` for paging large files.
- `list` supports a prefix filter, e.g. `workspaceList("scratch/q2/")`.
- Direct writes cap at 2 MB each. `saveToFile` allows up to 20 MB per pull.
- Temporary files belong under `scratch/`; durable user-facing files belong in
  normal Resources folders.
- When the user asked for a file they can keep or download, call
  `show-workspace-file` with the durable path immediately after the write. The
  download must appear in chat; do not answer with only a path or navigation
  instructions.

## Chunked Batch Analysis (30+ items)

For large fan-outs (account deep dives, Gong call reviews, deal cohorts):

1. **Define cohort**: fetch the item list (e.g. `hubspot-records` for accounts).
2. **Chunk**: process 5–10 items per pass to avoid context overflow.
3. **Per-item memo**: for each item, fetch evidence and write a memo file under
   `scratch/` unless the user asked to keep the memos:
   ```javascript
   await workspaceWrite(
     "scratch/analysis/q2-churn/acme-corp.md",
     "## Acme Corp\n\n**ARR**: $120k\n**Risk signals**: ...",
     "text/markdown",
   );
   ```
4. **Synthesize**: after all items are processed, list files and read each memo:
   ```javascript
   const files = await workspaceList("scratch/analysis/q2-churn/");
   ```
   Then read each file and synthesize findings into the final answer or a
   saved analysis.
5. **Promote** (optional): write a durable summary outside `scratch/` if the
   user wants to inspect or keep it in Resources.

For very large cohorts (100+ items), use agent-teams sub-agents to process
chunks in parallel — each sub-agent writes its memos independently, the
orchestrator synthesizes at the end.

## Corpus-First Provider Search

Use this workflow for arbitrary provider questions where a canned action is too
narrow, where records must be joined across systems, or where absence matters:

1. Discover the provider surface with `provider-api-catalog` and
   `provider-api-docs` when endpoint/filter/pagination details are uncertain.
2. For a broad or absence-sensitive corpus, start with the provider's native
   search or indexed tracker surface when one exists. Otherwise use
   `provider-api-request` as the raw ingestion step with `stageAs` or
   `saveToFile`, then reduce the staged data with `query-staged-dataset` or a
   Data Program. Use `provider-corpus-job` for durable paginated or batched
   transcript/body scans so each page or batch is checkpointed durably.
3. Use `run-code` to read staged data, write intermediate files, normalize
   records, join identity fields, and aggregate. For long code, use
   `background: true` and persist progress after each chunk.
4. Validate coverage before synthesis. Track pages fetched, records inspected,
   truncation flags, aborted calls, and records skipped for missing joins.
5. Finalize with the answer plus coverage and caveats. If coverage is partial,
   say so directly; never state "none found", "all records", or an exhaustive
   conclusion from sampled, truncated, or aborted data.

## saveToFile on Provider API Requests

Attach `saveToFile` to `provider-api-request` or `web-request` to write the
full response body to a Resources-backed workspace file instead of returning it
in context. Allows up to 20 MB per call. Use `scratch/...` for temporary raw
payloads.

```
provider-api-request
  provider=<provider-id>
  method=POST
  path=/records/search
  body={ filterGroups: [...], limit: 200 }
  saveToFile="scratch/analysis/provider-records-2026-q2.json"
```

Returns: `{ savedToFile: true, savedTo, status, bytes, contentType, preview }`.

Then use `run-code` to process the saved file:

```javascript
const raw = await workspaceRead(
  "scratch/analysis/provider-records-2026-q2.json",
);
const records = JSON.parse(raw);
// … aggregate, filter, join …
```

## fetchAllPages

Use `fetchAllPages` on `provider-api-request` to automatically paginate
cursor-based APIs. Combine with `saveToFile` to write the full dataset:

```
provider-api-request
  provider=hubspot
  path=/crm/v3/objects/deals
  query={ limit: 100 }
  fetchAllPages={
    cursorPath: "paging.next.after",
    cursorParam: "after",
    itemsPath: "results",
    maxPages: 20
  }
  saveToFile="scratch/analysis/all-deals.json"
```

Common cursor paths:

- HubSpot: `paging.next.after` / `after`
- Gong: `records.cursor` / `cursor`
- Pylon: `nextCursor` / `cursor`
- Slack: `response_metadata.next_cursor` / `cursor`
- PostHog: `next` (full URL — extract token manually if needed)

## run-code + Workspace Integration

Inside a `run-code` block you have access to workspace helpers:

```javascript
// Read a previously saved API response
const raw = await workspaceRead("scratch/analysis/deals.json");
const deals = JSON.parse(raw);

// Process data
const byStage = {};
for (const deal of deals) {
  const stage = deal.properties.dealstage ?? "unknown";
  byStage[stage] = (byStage[stage] ?? 0) + 1;
}

// Write the aggregated result back
await workspaceWrite(
  "scratch/analysis/deals-by-stage.json",
  JSON.stringify(byStage, null, 2),
  "application/json",
);
console.log(JSON.stringify(byStage, null, 2));
```

Workspace helpers: `workspaceRead(path, opts?)`, `workspaceWrite(path, content, contentType?)`,
`workspaceAppend(path, content)`, `workspaceList(prefix?)`.

## Provider API Discovery

When you need an API endpoint that isn't covered by a canned action:

1. `provider-api-catalog` — list available providers and their base URLs, auth,
   and example paths.
2. `provider-api-docs provider=<id> url=<docs-url>` — fetch any public docs or
   OpenAPI spec URL. Works for any `https://` URL.
3. `provider-api-request` — call the endpoint directly.

Use `web-request` to fetch public REST docs pages before registering a custom
provider.

## Registering Custom Providers

For APIs not in the built-in catalog, register them with
`provider-api-register` (dispatch action):

```
provider-api-register
  id="my-internal-api"
  label="My Internal API"
  baseUrl="https://api.mycompany.com"
  auth={ type: "bearer", credentialKey: "MY_API_TOKEN" }
  docsUrls=["https://docs.mycompany.com/api"]
```

Then call it with `provider-api-request provider=my-internal-api ...` and the
agent's credential system handles auth automatically.

## Learnings Flywheel

After any significant batch analysis, record discoveries to `LEARNINGS.md`
via the `resources` tool (`action: "write"`). Capture confirmed schema paths,
cursor fields, identity join keys, and pagination patterns.