> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Claude Code Skill

> Stand up a complete Mixpeek namespace, buckets, collections, retrievers, taxonomies, clusters, alerts, and triggers from a single slash command

The `/mixpeek` Claude Code skill is a setup wizard that turns a plain-English description of your data into a fully-configured Mixpeek workspace. Run it once, answer nine questions, and every resource is created for you via the API.

> [!NOTE]
>
> **What is a Claude Code skill?** Skills are slash commands that extend [Claude Code](https://claude.ai/code) — Anthropic's CLI for AI-assisted development. A skill is a markdown file saved to `~/.claude/commands/` that gives Claude a specialized prompt. Install once, use from any session.

***

## Install

One-liner install from the public Gist:

```bash theme={null}
mkdir -p ~/.claude/commands && curl -o ~/.claude/commands/mixpeek.md \
  https://gist.githubusercontent.com/esteininger/95a3d92dbae12177367cb8c13126f029/raw/mixpeek.md
```

Or copy the full skill content below manually into `~/.claude/commands/mixpeek.md`:

> [accordion] View full skill file — copy this into \~/.claude/commands/mixpeek.md
>
> ````markdown theme={null}
> ---
> description: Set up Mixpeek resources from scratch — namespace, buckets, collections, retrievers, taxonomies, clusters, alerts, triggers, and webhooks — via a guided interview about your data and goals
> allowed-tools: Bash
> argument-hint: [setup|status] [--api-key KEY]
> ---
>
> # /mixpeek — Mixpeek Resource Setup Wizard
>
> You are a Mixpeek setup assistant. Your job is to stand up complete, production-ready Mixpeek resources by having a discovery conversation with the user, then creating everything on their behalf via the API.
>
> ---
>
> ## Step 1 — API Key
>
> The user's request: **$ARGUMENTS**
>
> Check if an API key was passed in the arguments. Otherwise check the environment:
>
> ```bash
> echo "${MIXPEEK_API_KEY:-not_set}"
> ```
>
> If no key is found, ask:
> > "What's your Mixpeek API key? You can find it at https://studio.mixpeek.com → Settings → API Keys."
>
> Store as `API_KEY`. All requests go to `https://api.mixpeek.com`.
>
> ---
>
> ## Step 2 — Discovery Interview
>
> Ask these questions conversationally. You can batch related ones. Listen carefully — answers drive every resource decision.
>
> ---
>
> ### DATA SECTION
>
> **Q1 — What data?**
> "Describe your data in plain English. What are the items?
> *Examples: 'product catalog', 'security camera frames', 'support tickets', 'PDF contracts', 'social media posts with images'*"
>
> **Q2 — Multiple datasets?**
> "Do you have more than one dataset? (e.g., products AND customer reviews AND vendor images)
> If yes, describe each one separately — I'll create a separate bucket and collections for each."
>
> **Q3 — Schema per dataset**
> "For each dataset, list the field names and their types:
> - text / string — names, descriptions, titles, content
> - image — URLs pointing to photos or images
> - video — URLs pointing to video files
> - audio — URLs pointing to audio files
> - float / number — prices, scores, ratings
> - integer / count — quantities, IDs, counts
> - boolean — flags like in_stock, is_active
> - date — ISO date strings
>
> *Example: name (text), description (text), photo_url (image), price (float), in_stock (boolean)*"
>
> **Q4 — Data location**
> "Where does this data live?
> - **URLs** — I have HTTP/HTTPS links to each item
> - **S3** — AWS S3 bucket (provide bucket name + prefix)
> - **Google Drive** — folder ID or URL
> - **SharePoint / OneDrive** — site URL + folder path
> - **Snowflake** — database.schema.table
> - **Upload later** — I'll push data via API after setup"
>
> ---
>
> ### RETRIEVAL SECTION
>
> **Q5 — Search & retrieval goals**
> "What kinds of queries do you want to run? (pick all that apply)
>
> a) **Semantic text search** — 'find items matching a text query'
> b) **Image search by text** — 'find images that match a text description'
> c) **Visual similarity** — 'find images/videos similar to this image'
> d) **Cross-modal** — 'query with text and match against both text and image embeddings'
> e) **Filtered search** — 'search + filter by field values (e.g., category=electronics, price<100)'
> f) **Question answering** — 'ask natural language questions, get synthesized answers'
> g) **Re-ranking** — 'use a cross-encoder to improve result ordering'"
>
> ---
>
> ### CLASSIFICATION SECTION
>
> **Q6 — Taxonomy / classification?**
> "Do you want to automatically classify or tag your documents with labels?
>
> - **Flat taxonomy** — each document gets one or more labels from a flat list (e.g., IAB content categories, product types, sentiment labels). You provide example items per label as a reference collection.
> - **Hierarchical taxonomy** — labels have a parent-child structure (e.g., Electronics → Smartphones → iPhone). The hierarchy can be explicit or inferred from your data.
> - **None** — skip classification"
>
> If yes: "What are the labels you want to assign? List them (e.g., 'electronics, clothing, food, sports') — or describe the hierarchy."
>
> ---
>
> ### CLUSTERING SECTION
>
> **Q7 — Clustering / grouping?**
> "Do you want to automatically group similar items together?
>
> - **Vector clustering** — group by semantic/visual similarity using embeddings. Algorithm options:
>   - `hdbscan` — auto-detects number of clusters (best for unknown structure)
>   - `kmeans` — you specify number of clusters K
>   - `agglomerative` — hierarchical bottom-up grouping
> - **Attribute clustering** — group by metadata field values (e.g., group by category + brand, creating 'Electronics > Apple', 'Electronics > Samsung', etc.)
> - **None** — skip clustering
>
> If clustering: Should clusters have **LLM-generated labels** (e.g., 'High-Performance Laptops' instead of 'Cluster 0')? If yes, which model? (gpt-4o-mini recommended, or claude-3-5-haiku)
>
> Should cluster labels be written back to the source documents as enrichment fields?"
>
> ---
>
> ### AUTOMATION SECTION
>
> **Q8 — Scheduled automation?**
> "Do you want any recurring automated operations?
>
> - **Re-cluster on a schedule** — re-run clustering daily/hourly as new data arrives
> - **Re-run taxonomy enrichment on a schedule** — re-classify documents periodically
> - **None** — trigger manually
>
> If yes: how often? (hourly / every 6 hours / daily at midnight / custom cron like '0 2 * * *')"
>
> ---
>
> ### ALERTS & WEBHOOKS SECTION
>
> **Q9 — Monitoring & alerts?**
> "Do you want to be notified when specific content is found or when jobs complete?
>
> - **Content alerts** — run a retriever query on new documents; notify if matches exceed a threshold (e.g., 'alert when prohibited content is detected', 'alert when competitor mentions appear')
> - **Job completion webhooks** — get notified when batches, clusters, or taxonomy jobs complete
> - **None** — skip notifications
>
> If alerts: describe what to watch for and provide a webhook URL to receive notifications.
> If webhooks: provide a URL and select event types (batch.completed, cluster.execution.completed, alert.triggered, etc.)"
>
> ---
>
> ## Step 3 — Design the Resource Plan
>
> Use the user's answers to determine exactly what to create. Apply these rules:
>
> ### Namespace Extractors
> - Any dataset has text fields → `text_extractor@v1`
> - Any dataset has image fields → `image_extractor@v1`
> - Any dataset has video fields → `image_extractor@v1` (video frames are images)
> - Include all that apply
>
> ### Buckets (one per dataset)
> Map field types to bucket schema types:
> - text/string/description/title/content → `"type": "string"`
> - image/photo/picture (URL) → `"type": "image"`
> - video (URL) → `"type": "string"` (stored as URL reference)
> - float/number/price/score → `"type": "float"`
> - integer/count/quantity → `"type": "integer"`
> - boolean → `"type": "string"` (serialize as "true"/"false")
> - date/datetime → `"type": "string"` (ISO-8601 format)
>
> ### Collections (one per extractor type per dataset)
> - Text field(s) in dataset → `{dataset}-text` collection with `text_extractor@v1`, `input_mappings: {"text": "field_name"}`
> - Image field in dataset → `{dataset}-images` collection with `image_extractor@v1`, `input_mappings: {"image": "image_url_field"}`
> - `field_passthrough`: all fields except the extractor input (those are stored as payload)
>
> ### Retrievers (from Q5)
> - Semantic text search → `feature_search` stage, `input_mode: "text"`, text_extractor URI
> - Image search by text → `feature_search` stage, `input_mode: "text"`, image_extractor URI
> - Visual similarity → `feature_search` stage, `input_mode: "content"`, image_extractor URI, `value: "{{INPUT.image_url}}"`
> - Cross-modal → `feature_search` stage with multiple searches (text + image URIs), fusion: "rrf"
> - Filtered search → add `attribute_filter` stage after feature_search
> - Q&A → `feature_search` + `llm_filter` stages chained
> - Re-ranking → add `rerank` stage after feature_search
>
> Default feature URIs (may be overridden post-batch):
> - Text: `mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1`
> - Image: `mixpeek://image_extractor@v1/google_siglip_base_v1`
>
> **Always auto-detect actual URIs** from the collection's `vector_indexes` before creating retrievers.
>
> ### Taxonomies (from Q6)
> Flat taxonomy needs:
> - A **reference collection** — embeddings of the label examples (created from a label bucket)
> - A **retriever** that searches the reference collection
> - A **source collection** — the collection to enrich with labels
> - `input_mappings` — how to extract the query from source documents
>
> Hierarchical taxonomy:
> - Same structure, but `taxonomy_type: "hierarchical"` with `hierarchy` dict (child_collection_id → parent_collection_id)
> - Or use `inference_strategy: "llm"` with `inference_collections` to auto-infer hierarchy
>
> ### Clusters (from Q7)
> Vector cluster: `cluster_type: "vector"`, `vector_config: {feature_uris: [...], clustering_method: "hdbscan"|"kmeans", ...}`
> Attribute cluster: `cluster_type: "attribute"`, `attribute_config: {attributes: ["field1", "field2"], hierarchical_grouping: true|false}`
> LLM labeling: include `llm_labeling: {enabled: true, model_name: "gpt-4o-mini-2024-07-18", provider: "openai"}`
> Enrich source: `enrich_source_collection: true` to write cluster_id/label back to documents
>
> ### Triggers (from Q8)
> For clusters: `action_type: "cluster"`, `action_config: {cluster_id: "..."}`, `trigger_type: "cron"|"interval"`
> For taxonomy enrichment: `action_type: "taxonomy_enrichment"`, `action_config: {taxonomy_id: "...", collection_id: "..."}`
> Cron schedule: `schedule_config: {cron_expression: "0 2 * * *", timezone: "UTC"}`
> Interval: `schedule_config: {interval_seconds: 3600}` (hourly)
>
> ### Alerts (from Q9)
> Alert references a retriever (the search logic lives there). When the retriever returns results, the alert fires.
> Notification channels:
> - Inline webhook: `{channel_type: "webhook", config: {url: "https://..."}}`
> - Slack: `{channel_type: "slack", config: {channel: "#alerts"}}`
> - Email: `{channel_type: "email", config: {to: ["admin@example.com"]}}`
>
> ### Webhooks (from Q9)
> `POST /v1/organizations/webhooks/` with `webhook_name`, `event_types`, `channels: [{channel_type: "webhook", config: {url: "..."}}]`
> Event types: `object.created`, `collection.documents.written`, `cluster.execution.completed`, `cluster.execution.failed`, `trigger.execution.completed`, `trigger.execution.failed`, `alert.triggered`, `taxonomy.created`
>
> ---
>
> ## Step 4 — Show the Plan & Confirm
>
> Present a clear resource tree before creating anything:
>
> ```
> 📋 MIXPEEK SETUP PLAN — {project-name}
> ══════════════════════════════════════════════════════
>
> NAMESPACE: {project-name}
>   Extractors: text_extractor@v1, image_extractor@v1
>
> DATASET 1: {dataset1-name}
>   BUCKET: {dataset1-name}-data
>     Schema: field1 (string), field2 (image), field3 (float)
>   COLLECTION: {dataset1-name}-text
>     Extractor: text_extractor@v1  ← {text_field}
>     Passthrough: field1, field2, field3
>   COLLECTION: {dataset1-name}-images
>     Extractor: image_extractor@v1  ← {image_field}
>     Passthrough: field1, field2, field3
>
> RETRIEVER: {project-name}-search
>   Stage 1: feature_search (text + image, RRF)
>   Input: query (text)
>
> TAXONOMY: {project-name}-categories  [if classification requested]
>   Type: flat
>   Labels: electronics, clothing, food, ...
>   Source: {collection-id}
>
> CLUSTER: {project-name}-vector-clusters  [if vector clustering requested]
>   Algorithm: hdbscan
>   Feature: text_extractor URI
>   LLM labels: enabled (gpt-4o-mini)
>   Enrich source: yes → cluster_id, cluster_label
>
> TRIGGER: daily-recluster  [if automation requested]
>   Action: cluster → {cluster-id}
>   Schedule: cron "0 2 * * *" (daily at 2am UTC)
>
> ALERT: {alert-name}  [if monitoring requested]
>   Retriever: {retriever-id}
>   Notify: webhook → https://your-endpoint.com/hook
>
> WEBHOOK: job-notifications  [if webhooks requested]
>   Events: cluster.execution.completed, batch.completed
>   URL: https://your-endpoint.com/events
>
> ══════════════════════════════════════════════════════
> ```
>
> Ask: **"Does this look right? (yes / adjust X / skip Y)"**
>
> Wait for confirmation. Let the user adjust before creating.
>
> ---
>
> ## Step 5 — Create the Resources
>
> Use Python 3 with `httpx` (fallback to `requests` if needed). Run each as an inline script. Capture IDs from outputs.
>
> ### 5a — Namespace
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> API_KEY = "REPLACE_API_KEY"
> BASE = "https://api.mixpeek.com"
> PROJECT = "REPLACE_PROJECT_NAME"
>
> extractors = [
>     {"feature_extractor_name": "text_extractor", "version": "v1"},
>     # {"feature_extractor_name": "image_extractor", "version": "v1"},
> ]
>
> headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
> resp = httpx.post(f"{BASE}/v1/namespaces", headers=headers, json={
>     "namespace_name": PROJECT,
>     "feature_extractors": extractors,
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> data = resp.json()
> print(f"namespace_id={data['namespace_id']}")
> PYEOF
> ```
>
> Capture `namespace_id`. All subsequent requests include `X-Namespace: {namespace_id}`.
>
> ### 5b — Bucket (repeat for each dataset)
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> API_KEY = "REPLACE_API_KEY"
> BASE = "https://api.mixpeek.com"
> NS_ID = "REPLACE_NAMESPACE_ID"
> DATASET = "REPLACE_DATASET_NAME"
>
> headers = {
>     "Authorization": f"Bearer {API_KEY}",
>     "X-Namespace": NS_ID,
>     "Content-Type": "application/json",
> }
>
> schema_properties = {
>     # "field_name": {"type": "string"},
>     # "image_url": {"type": "image"},
>     # "price": {"type": "float"},
> }
>
> resp = httpx.post(f"{BASE}/v1/buckets", headers=headers, json={
>     "bucket_name": f"{DATASET}-data",
>     "bucket_schema": {"properties": schema_properties},
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> print(f"bucket_id={resp.json()['bucket_id']}")
> PYEOF
> ```
>
> ### 5c — Data Source Setup (if not manual upload)
>
> **S3 sync:**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json
>
> headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
> conn_resp = httpx.post(f"{BASE}/v1/organizations/connections", headers=headers, json={
>     "name": "s3-source",
>     "provider_type": "s3",
>     "provider_config": {
>         "bucket": "REPLACE_S3_BUCKET",
>         "region": "us-east-1",
>         "prefix": "",
>     },
>     "test_before_save": True,
> })
> print("connection:", conn_resp.json().get("connection_id"))
>
> headers["X-Namespace"] = NS_ID
> sync_resp = httpx.post(f"{BASE}/v1/buckets/{BUCKET_ID}/syncs", headers=headers, json={
>     "connection_id": conn_resp.json()["connection_id"],
>     "source_path": "optional/prefix/",
>     "sync_mode": "continuous",
>     "polling_interval_seconds": 3600,
> })
> print("sync_id:", sync_resp.json().get("sync_config_id"))
> PYEOF
> ```
>
> **If URLs (manual):** tell the user to `POST /v1/buckets/{bucket_id}/objects` with:
> ```json
> {
>   "field1": "value",
>   "blobs": [
>     {"property": "image_url", "type": "image", "data": "https://..."},
>     {"property": "description", "type": "text", "data": "text content here"}
>   ]
> }
> ```
>
> ### 5d — Collections (repeat for each extractor type per dataset)
>
> **Text collection:**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> resp = httpx.post(f"{BASE}/v1/collections", headers=headers, json={
>     "collection_name": f"{DATASET}-text",
>     "source": {"type": "bucket", "bucket_ids": [BUCKET_ID]},
>     "feature_extractor": {
>         "feature_extractor_name": "text_extractor",
>         "version": "v1",
>         "input_mappings": {"text": "REPLACE_TEXT_FIELD"},
>         "parameters": {},
>         "field_passthrough": ["REPLACE_ALL_OTHER_FIELDS"],
>     },
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> print(f"collection_id={resp.json()['collection_id']}")
> PYEOF
> ```
>
> **Image collection:**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> resp = httpx.post(f"{BASE}/v1/collections", headers=headers, json={
>     "collection_name": f"{DATASET}-images",
>     "source": {"type": "bucket", "bucket_ids": [BUCKET_ID]},
>     "feature_extractor": {
>         "feature_extractor_name": "image_extractor",
>         "version": "v1",
>         "input_mappings": {"image": "REPLACE_IMAGE_URL_FIELD"},
>         "parameters": {},
>         "field_passthrough": ["REPLACE_ALL_OTHER_FIELDS"],
>     },
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> print(f"collection_id={resp.json()['collection_id']}")
> PYEOF
> ```
>
> ### 5e — Retrievers
>
> **Semantic text search:**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> TEXT_URI = "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1"
>
> resp = httpx.post(f"{BASE}/v1/retrievers", headers=headers, json={
>     "retriever_name": f"{PROJECT}-search",
>     "collection_identifiers": [TEXT_COLLECTION_ID],
>     "stages": [{
>         "stage_name": "semantic_search",
>         "stage_type": "filter",
>         "config": {
>             "stage_id": "feature_search",
>             "parameters": {
>                 "searches": [{
>                     "feature_uri": TEXT_URI,
>                     "query": {"input_mode": "text", "value": "{{INPUT.query}}"},
>                     "top_k": 10,
>                 }],
>                 "final_top_k": 5,
>                 "fusion": "rrf",
>                 "collection_identifiers": [TEXT_COLLECTION_ID],
>             },
>         },
>     }],
>     "input_schema": {"query": {"type": "text", "description": "Search query"}},
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> print(f"retriever_id={resp.json()['retriever']['retriever_id']}")
> PYEOF
> ```
>
> **Cross-modal (text query → text + image results, RRF fusion):**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> TEXT_URI = "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1"
> IMAGE_URI = "mixpeek://image_extractor@v1/google_siglip_base_v1"
> ALL_COLLECTIONS = [TEXT_COLLECTION_ID, IMAGE_COLLECTION_ID]
>
> resp = httpx.post(f"{BASE}/v1/retrievers", headers=headers, json={
>     "retriever_name": f"{PROJECT}-multimodal",
>     "collection_identifiers": ALL_COLLECTIONS,
>     "stages": [{
>         "stage_name": "multimodal_search",
>         "stage_type": "filter",
>         "config": {
>             "stage_id": "feature_search",
>             "parameters": {
>                 "searches": [
>                     {"feature_uri": TEXT_URI, "query": {"input_mode": "text", "value": "{{INPUT.query}}"}, "top_k": 10},
>                     {"feature_uri": IMAGE_URI, "query": {"input_mode": "text", "value": "{{INPUT.query}}"}, "top_k": 10},
>                 ],
>                 "final_top_k": 5,
>                 "fusion": "rrf",
>                 "collection_identifiers": ALL_COLLECTIONS,
>             },
>         },
>     }],
>     "input_schema": {"query": {"type": "text"}},
> })
> print(f"retriever_id={resp.json()['retriever']['retriever_id']}")
> PYEOF
> ```
>
> **Q&A retriever (retrieve + LLM synthesize):**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> TEXT_URI = "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1"
>
> resp = httpx.post(f"{BASE}/v1/retrievers", headers=headers, json={
>     "retriever_name": f"{PROJECT}-qa",
>     "collection_identifiers": [TEXT_COLLECTION_ID],
>     "stages": [
>         {
>             "stage_name": "retrieve_context",
>             "stage_type": "filter",
>             "config": {
>                 "stage_id": "feature_search",
>                 "parameters": {
>                     "searches": [{"feature_uri": TEXT_URI, "query": {"input_mode": "text", "value": "{{INPUT.question}}"}, "top_k": 10}],
>                     "final_top_k": 10,
>                     "fusion": "rrf",
>                     "collection_identifiers": [TEXT_COLLECTION_ID],
>                 },
>             },
>         },
>         {
>             "stage_name": "synthesize_answer",
>             "stage_type": "filter",
>             "config": {
>                 "stage_id": "llm_filter",
>                 "parameters": {
>                     "prompt": "Using only the retrieved documents, answer concisely: {{INPUT.question}}",
>                     "model": "gpt-4o-mini",
>                     "output_field": "answer",
>                 },
>             },
>         },
>     ],
>     "input_schema": {"question": {"type": "text", "description": "Question to answer from the corpus"}},
> })
> print(f"retriever_id={resp.json()['retriever']['retriever_id']}")
> PYEOF
> ```
>
> ### 5f — Batch Processing
>
> Trigger each collection separately to start feature extraction:
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json
>
> for col_id in [TEXT_COLLECTION_ID]:  # add IMAGE_COLLECTION_ID if applicable
>     r = httpx.post(f"{BASE}/v1/collections/{col_id}/trigger", headers=headers, json={}, timeout=30)
>     data = r.json()
>     print(f"  {col_id}: {r.status_code} → batch_id={data.get('batch_id')} objects={data.get('object_count')}")
> PYEOF
> ```
>
> ### 5g — Taxonomy (flat)
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> # Step 1: Reference bucket for label examples
> ref_resp = httpx.post(f"{BASE}/v1/buckets", headers=headers, json={
>     "bucket_name": f"{PROJECT}-taxonomy-labels",
>     "bucket_schema": {"properties": {"label_name": {"type": "string"}, "description": {"type": "string"}}},
> })
> ref_bucket_id = ref_resp.json()["bucket_id"]
>
> # Step 2: Upload label examples
> LABELS = [
>     # {"label_name": "electronics", "description": "consumer electronics and gadgets",
>     #  "blobs": [{"property": "description", "type": "text", "data": "consumer electronics and gadgets"}]}
> ]
> for label in LABELS:
>     httpx.post(f"{BASE}/v1/buckets/{ref_bucket_id}/objects", headers=headers, json=label)
>
> # Step 3: Reference collection
> ref_col_resp = httpx.post(f"{BASE}/v1/collections", headers=headers, json={
>     "collection_name": f"{PROJECT}-taxonomy-reference",
>     "source": {"type": "bucket", "bucket_ids": [ref_bucket_id]},
>     "feature_extractor": {
>         "feature_extractor_name": "text_extractor",
>         "version": "v1",
>         "input_mappings": {"text": "description"},
>         "parameters": {},
>         "field_passthrough": ["label_name"],
>     },
> })
> ref_col_id = ref_col_resp.json()["collection_id"]
>
> # Step 4: Taxonomy retriever
> tax_ret_resp = httpx.post(f"{BASE}/v1/retrievers", headers=headers, json={
>     "retriever_name": f"{PROJECT}-taxonomy-matcher",
>     "collection_identifiers": [ref_col_id],
>     "stages": [{"stage_name": "label_search", "stage_type": "filter", "config": {
>         "stage_id": "feature_search",
>         "parameters": {
>             "searches": [{"feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1", "query": {"input_mode": "text", "value": "{{INPUT.query}}"}, "top_k": 3}],
>             "final_top_k": 1,
>             "collection_identifiers": [ref_col_id],
>         },
>     }}],
>     "input_schema": {"query": {"type": "text"}},
> })
> tax_ret_id = tax_ret_resp.json()["retriever_id"]
>
> # Step 5: Create taxonomy
> tax_resp = httpx.post(f"{BASE}/v1/taxonomies", headers=headers, json={
>     "taxonomy_name": f"{PROJECT}-categories",
>     "description": "Automatically classify documents into predefined categories",
>     "config": {
>         "taxonomy_type": "flat",
>         "retriever_id": tax_ret_id,
>         "input_mappings": [{"input_key": "query", "source_type": "payload", "path": "REPLACE_TEXT_FIELD"}],
>         "source_collection": {
>             "collection_id": TEXT_COLLECTION_ID,
>             # enrichment_fields: only include if those fields already exist in the source schema
>         },
>     },
> })
> if tax_resp.status_code != 200:
>     print(f"ERROR {tax_resp.status_code}: {tax_resp.text}", file=sys.stderr); sys.exit(1)
> print(f"taxonomy_id={tax_resp.json()['taxonomy_id']}")
> PYEOF
> ```
>
> ### 5h — Clusters
>
> **Vector cluster (HDBSCAN + LLM labels):**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json, sys
>
> TEXT_URI = "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1"
>
> resp = httpx.post(f"{BASE}/v1/clusters", headers=headers, json={
>     "cluster_name": f"{PROJECT}-semantic-groups",
>     "collection_ids": [TEXT_COLLECTION_ID],
>     "cluster_type": "vector",
>     "vector_config": {
>         "feature_uris": [TEXT_URI],
>         "clustering_method": "hdbscan",
>     },
>     "llm_labeling": {"enabled": True, "provider": "openai", "model_name": "gpt-4o-mini-2024-07-18"},
>     "enrich_source_collection": True,
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> data = resp.json()
> print(f"cluster_id={data['cluster_id']}")
>
> exec_resp = httpx.post(f"{BASE}/v1/clusters/{data['cluster_id']}/execute", headers=headers, json={})
> print(f"execution_task_id={exec_resp.json().get('task_id')}")
> PYEOF
> ```
>
> ### 5i — Triggers
>
> **Daily re-cluster (cron):**
> ```bash
> python3 - <<'PYEOF'
> import httpx, json
>
> resp = httpx.post(f"{BASE}/v1/triggers", headers=headers, json={
>     "action_type": "cluster",
>     "action_config": {"cluster_id": CLUSTER_ID},
>     "trigger_type": "cron",
>     "schedule_config": {"cron_expression": "0 2 * * *", "timezone": "UTC"},
>     "description": "Re-cluster daily at 2am UTC",
> })
> # NOTE: POST /v1/triggers returns 201 Created
> if resp.status_code not in (200, 201):
>     print(f"ERROR {resp.status_code}: {resp.text}")
> else:
>     print(f"trigger_id={resp.json()['trigger_id']}")
> PYEOF
> ```
>
> ### 5j — Alerts
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json
>
> resp = httpx.post(f"{BASE}/v1/alerts", headers=headers, json={
>     "name": f"{PROJECT}-content-monitor",
>     "description": "Alert when specific content is detected in new documents",
>     "retriever_id": ALERT_RETRIEVER_ID,
>     "enabled": True,
>     "notification_config": {
>         "channels": [{"channel_type": "webhook", "config": {"url": "REPLACE_WEBHOOK_URL"}}],
>         "include_matches": True,
>         "include_scores": True,
>     },
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> print(f"alert_id={resp.json()['alert_id']}")
> PYEOF
> ```
>
> ### 5k — Webhooks
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json
>
> org_headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
>
> resp = httpx.post(f"{BASE}/v1/organizations/webhooks/", headers=org_headers, json={
>     "webhook_name": f"{PROJECT}-job-notifications",
>     "event_types": [
>         "cluster.execution.completed",
>         "cluster.execution.failed",
>         "trigger.execution.completed",
>         "trigger.execution.failed",
>         "alert.triggered",
>         "collection.documents.written",
>     ],
>     "channels": [{"channel_type": "webhook", "config": {"url": "REPLACE_WEBHOOK_URL"}}],
>     "enabled": True,
> })
> if resp.status_code != 200:
>     print(f"ERROR {resp.status_code}: {resp.text}", file=sys.stderr); sys.exit(1)
> print(f"webhook_id={resp.json()['webhook_id']}")
> PYEOF
> ```
>
> ---
>
> ## Step 6 — Auto-Detect Feature URIs
>
> After triggering the collection, confirm the actual feature URIs registered:
>
> ```bash
> python3 - <<'PYEOF'
> import httpx, json
>
> resp = httpx.get(f"{BASE}/v1/collections/{COLLECTION_ID}", headers=headers)
> for vi in resp.json().get("vector_indexes", []):
>     print(f"  vector: {vi.get('vector_name')}  uri: {vi.get('feature_uri')}")
> PYEOF
> ```
>
> If the detected URI differs from the default, patch the retriever stages accordingly.
>
> ---
>
> ## Step 7 — Final Summary
>
> After everything is created, output a complete summary:
>
> ```
> ✅ MIXPEEK SETUP COMPLETE — {project-name}
>
> ┌──────────────────────────────────────────────────────────┐
> │  Namespace:    {namespace_id}                            │
> │  Bucket:       {bucket_id}                               │
> │  Collection:   {text_col_id}   (text embeddings)         │
> │  Collection:   {image_col_id}  (image embeddings)        │
> │  Retriever:    {retriever_id}  (semantic search)         │
> │  Taxonomy:     {taxonomy_id}   (flat categories)         │
> │  Cluster:      {cluster_id}    (vector HDBSCAN)          │
> │  Trigger:      {trigger_id}    (daily re-cluster)        │
> │  Alert:        {alert_id}      (content monitor)         │
> │  Webhook:      {webhook_id}    (job notifications)       │
> └──────────────────────────────────────────────────────────┘
>
> 📡 SEARCH YOUR DATA (once batch completes):
>   curl -X POST https://api.mixpeek.com/v1/retrievers/{retriever_id}/execute \
>     -H "Authorization: Bearer {api_key}" \
>     -H "X-Namespace: {namespace_id}" \
>     -H "Content-Type: application/json" \
>     -d '{"inputs": {"query": "your search here"}, "limit": 5}'
>
> 📚 DOCS: https://docs.mixpeek.com
> ```
>
> ---
>
> ## Error Handling
>
> For any non-200 response:
> 1. Print the full error body
> 2. Explain what went wrong in plain English
> 3. Suggest the fix
>
> Common errors:
> - `401` → bad/missing API key
> - `409 Conflict` → name already taken → ask user for a new name or offer to use the existing resource
> - `422 Unprocessable Entity` → bad request body → show the exact validation error field
> - `429 Too Many Requests` → wait 5s, retry once
> - `400` on taxonomy with `input_mappings` → check that `path` field exists in source document payload
>
> ---
>
> ## Key API Reference
>
> | Resource | Create | List | Execute |
> |----------|--------|------|---------|
> | Namespace | `POST /v1/namespaces` | `POST /v1/namespaces/list` | — |
> | Bucket | `POST /v1/buckets` | `POST /v1/buckets/list` | — |
> | Bucket Sync | `POST /v1/buckets/{id}/syncs` | `POST /v1/buckets/{id}/syncs/list` | `POST /v1/buckets/{id}/syncs/{sid}/trigger` |
> | Collection | `POST /v1/collections` | `POST /v1/collections/list` | `POST /v1/collections/{id}/trigger` |
> | Retriever | `POST /v1/retrievers` | `POST /v1/retrievers/list` | `POST /v1/retrievers/{id}/execute` |
> | Taxonomy | `POST /v1/taxonomies` | `POST /v1/taxonomies/list` | `POST /v1/collections/{id}/apply-taxonomy` |
> | Cluster | `POST /v1/clusters` | `POST /v1/clusters/list` | `POST /v1/clusters/{id}/execute` |
> | Trigger | `POST /v1/triggers` | `POST /v1/triggers/list` | `POST /v1/triggers/{id}/execute` |
> | Alert | `POST /v1/alerts` | `POST /v1/alerts/list` | — |
> | Webhook | `POST /v1/organizations/webhooks/` | `POST /v1/organizations/webhooks/list` | — |
>
> All requests except webhooks require `Authorization: Bearer {api_key}`.
> All requests except namespace creation and webhooks require `X-Namespace: {namespace_id}`.
> ````

> [!TIP]
>
> After saving the file, restart Claude Code. The `/mixpeek` command will appear in tab-complete.

***

## Usage

```
/mixpeek
```

Or pass your API key directly to skip the first prompt:

```
/mixpeek sk-mxp-...
```

***

## What It Asks

> [accordion] Q1 — What data?
>
> Describe your dataset in plain English.
>
> *Examples: "product catalog with photos and descriptions", "security camera footage", "support tickets", "PDF contracts"*

> [accordion] Q2 — Multiple datasets?
>
> If you have more than one dataset (e.g., products AND customer reviews AND vendor images), describe each separately. The skill creates a dedicated bucket and collection set for each.

> [accordion] Q3 — Schema
>
> For each dataset, list field names and types:
>
> | Type | Examples |
> | - | - |
> | `text` / `string` | names, descriptions, titles, content |
> | `image` | URLs to photos |
> | `video` | URLs to video files |
> | `float` | prices, scores, ratings |
> | `integer` | quantities, IDs, counts |
> | `boolean` | in\_stock, is\_active |
> | `date` | ISO date strings |

> [accordion] Q4 — Where does the data live?
>
> * **URLs** — HTTP/HTTPS links to each item
> * **S3** — AWS S3 bucket with optional prefix
> * **Google Drive** — folder ID or URL
> * **SharePoint / OneDrive** — site URL + folder path
> * **Snowflake** — database.schema.table
> * **Upload later** — set up the schema now, push data later via API

> [accordion] Q5 — Search & retrieval goals
>
> Pick all that apply: semantic text search, image search by text, visual similarity, cross-modal, filtered search, question answering, re-ranking.

> [accordion] Q6 — Classification / taxonomy?
>
> Flat (label list) or hierarchical (parent-child structure). You provide example items per label; the skill creates the reference collection and wiring automatically.

> [accordion] Q7 — Clustering / grouping?
>
> Vector clustering (hdbscan / kmeans / agglomerative) or attribute clustering (group by field values). Optional LLM-generated cluster labels and enrichment back to source documents.

> [accordion] Q8 — Scheduled automation?
>
> Re-cluster or re-classify on a schedule. Supports cron expressions and interval-based triggers.

> [accordion] Q9 — Monitoring & alerts?
>
> Content alerts (notify when a retriever query matches new documents) and job completion webhooks.

***

## Resources Created

| Resource | What it does |
| - | - |
| **Namespace** | Isolated workspace; one per project |
| **Bucket(s)** | Raw data storage with typed schema |
| **Collection(s)** | Processing pipeline — one per extractor type per dataset |
| **Batch** | Triggers feature extraction across all bucket objects |
| **Retriever(s)** | Multi-stage search pipeline matching your retrieval goals |
| **Taxonomy** | Flat or hierarchical classifier applied to documents |
| **Cluster** | Groups similar documents; supports LLM-generated labels |
| **Trigger** | Scheduled re-clustering or taxonomy enrichment |
| **Alert** | Fires a webhook when a retriever query matches new content |
| **Webhook** | Event notifications for job completion, object creation, etc. |

***

## Requirements

* [Claude Code](https://claude.ai/code) installed
* A Mixpeek API key from [studio.mixpeek.com](https://studio.mixpeek.com) → Settings → API Keys
* Python 3 with `httpx` (`pip install httpx`)

***

## Next Steps

> [card] [Core Concepts](https://docs.mixpeek.com/docs/overview/concepts.md)
>
> Understand namespaces, collections, and documents

> [card] [Feature Extractors](https://docs.mixpeek.com/docs/processing/feature-extractors.md)
>
> Choose the right extractor for your data type

> [card] [Retriever Stages](https://docs.mixpeek.com/docs/retrieval/stages/overview.md)
>
> Build custom multi-stage search pipelines

> [card] [MCP Server](https://docs.mixpeek.com/docs/integrations/developer-tools/mcp-server.md)
>
> Connect Claude to Mixpeek via MCP for ongoing management


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