Temporal Pipelines

Temporal Workflow Pipelines

Thesis: A custom pipeline is a Python Temporal @workflow.defn authored with pipeline_sdk and driven entirely through 19 MCP tools. The path through this skill:

• §1–§2 — understand the model and the tools
• §3 — design within the 4 GiB / 2-core / 4-activity pod budget
• §4 — author from scratch through a strict validate-before-upload loop
• §5 — PATCH an existing pipeline (never regenerate)
• §6 — debug a failing run (code examples live in the cookbook)
• §7 — finish with a skeptic-verify + visualize pass

Pipeline lifecycle:

graph LR
    Scope["Scope §4.1"] --> Author["Author §4"]
    Scope --> Edit["Edit §5"]
    Author --> Gate["Validate gate §4.5"]
    Edit --> Gate
    Gate --> Run["Run §4.6"]
    Run --> Finish["Finish: verify + visualize §7"]

Route first: a new pipeline (or a full rebuild) → start at §4 (Scope). Editing one that already exists → jump straight to §5 (pull the live code first, PATCH, never regenerate from memory).

Read the matching reference file BEFORE writing code. The references/ files are part of this skill — when a row matches the task at hand, Read the file and work from it, not from memory of it:

1.0 What it is & when to use it

A pipeline is a Python Temporal workflow (@workflow.defn class) that orchestrates reusable activities with Temporal's full power: deterministic replay, fan-out/fan-in, child workflows, signals, heartbeats, retries, durable scheduling. Code is stored in Postgres, shipped to S3 by the Workflow Manager API, run by a per-tenant K8s worker pod; each run is a first-class, observable, recoverable Temporal workflow.

It can do practically any pipeline. Lead with the marketing outcome, technology second — Improvado customers are media buyers and marketing ops. When the user is vague or just exploring ("what can this do?", "что можно сделать?", a half-formed idea), don't make them spec it cold — proactively pitch 3-5 concrete options from §1.1, rephrased to their business, and let them pick one to build.

1.1 Marketing use-cases to suggest (pick 3-5, rephrase naturally)

• Long-running backfills with resumability — reprocess 12 months of Meta Ads spend by day; survives pod restarts with zero lost work.
• Cross-platform audience sync — pull a CDP audience, fan-out to Meta / Google / TikTok / LinkedIn in parallel, aggregate per-platform success.
• Durable ETL with cursors — nightly incremental extract via a PipelineState cursor; any step fails → Temporal retries from the last checkpoint.
• Multi-step campaign launches — read a brief from Google Sheets → validate UTM/naming → upload creatives → create campaigns → verify; each step isolated, one failure doesn't cascade.
• Reverse ETL with fan-out — push attribution into HubSpot / Salesforce per workspace using child workflows.
• Data-quality watchdogs on cron — scan tables for freshness every 30 min, alert on stale data.
• Budget pacing with signals — watch daily spend vs. plan, receive pause_campaign signals, call ad-platform APIs.
• Creative-intake with heartbeat — poll SFTP for new assets every few minutes, process in batches, PipelineState dedupes already-seen files.

Drop into generic ETL phrasing only after the user signals a technical integration.

Scope boundary — hand off, don't stretch. Pure DTS-UI extraction → /discovery-api, /field-mapping. UI/React → /react-best-practices. General Python unrelated to @workflow.defn → just write it, no skill.

2.0 Architecture & tools

Everything — run, inspect, debug — goes through the MCP tools below. agency_uuid/workspace_id always come from tenant context, never from agent args.

System components:

graph TD
    Agent["AI Agent (chat)"] --> MCP["19 MCP tools"]
    MCP --> WM["Workflow Manager API"]
    WM --> S3["S3 code store"]
    WM --> Temporal["Temporal Server"]
    Temporal --> Pod["Per-tenant worker pod"]
    Pod --> S3
    Pod --> Act["prepare_credentials → your activity → cleanup_credentials"]

The Workflow Manager AST-validates code (blocks os, subprocess, socket), uploads .py to S3, starts the workflow on task queue tenant-{agency_uuid}-{workspace_id}; the pod downloads the .py and loads it as the synthetic module _s3_pipeline_<file> — this is what drives the sandboxed=False rule (references/code-conventions.md, T1).

2.1 Authoring & execution tools

2.2 Runs, results & schedules

Schedules: pauseTemporalPipelineScheduleTool, resumeTemporalPipelineScheduleTool, triggerTemporalPipelineScheduleTool (manual run now; under SKIP overlap silently dropped if a run is still RUNNING), listTemporalPipelineSchedulesTool (live from Temporal). Policy table: §4.6.

Run files: listTemporalPipelineRunArtifactsTool lists every file the run wrote under secret.s3.prefix — no publish step (ADR-011); prefix/cursor/page_size to filter/page → getTemporalPipelineRunArtifactUrlTool (presigned GET ~15 min). Never echo the presigned URL — fetch once, drop it. Empty [] = run wrote nothing or wrong run_id.

2.3 Observability — read-only diagnostics (used by the §6.1 debug loop)

3.0 Runtime limits — write efficient code or the pod dies

Every activity runs on a small per-tenant pod; up to 4 activities run concurrently on one pod, sharing the budget below. Design as if 3 identical siblings share the pod with you.

How to stay inside this budget — the efficiency rules (E1–E7), the logging rules (L1–L5), and the list of pre-installed libraries live in references/code-conventions.md, read in full at §4.4. They are distilled from real production pipelines; keeping peak memory inside 4 GiB is what keeps a pipeline alive at production volumes.

4.0 Author a new pipeline

Work chronologically and catch every error before upload — each post-upload mistake costs an S3 round-trip + worker reload. Work in a local sandbox file with the SDK installed. Editing an existing pipeline instead? → §5.

graph LR
    A["Scope §4.1"] --> B["SDK §4.2"]
    B --> C["Recon §4.3"]
    C --> D["Write §4.4"]
    D --> E["Validate §4.5"]
    E --> F["Run §4.6"]
    F --> G["Finish §7"]

4.1 Scope it first

The pipeline's type, data volume, and cadence decide the whole design — guess them wrong and you re-architect after the first slow / OOM run.

PROBE the real source — don't guess. List the SFTP dir (file count + sizes), SELECT count() a CH table, HEAD an S3 prefix, page a Discovery endpoint. For sources reachable only with the pipeline's own creds (SFTP / S3 / destination DB), run a tiny throwaway runTemporalPipelineCodeTool that lists+counts and returns a summary — doubles as a connectivity + credentials smoke test.

4.2 Update & read the SDK

The SDK is the contract; sandboxes drift behind the worker by days. Update it, then read the source — don't guess signatures:

pip install --upgrade improvado-pipeline-sdk
python -c "import pipeline_sdk, os; print(os.path.dirname(pipeline_sdk.__file__))"
cat .../pipeline_sdk/runtime/proxy.py   # call_datasource_proxy / call_mcp_tool
cat .../pipeline_sdk/types.py           # every dataclass crossing workflow ↔ activity

If a docstring contradicts this skill, trust the docstring and flag it. Import map:

4.3 Recon before code

Never invent table/column names or HTTP shapes — verify live:

• ClickHouse (recipe_*, extract_*, load_*, mdg_*, flat_data_*): listDataTablesTool({search}) → clickhouseTool({query:"DESCRIBE TABLE …"}) + a LIMIT 3, then hard-code what DESCRIBE returned. Skip if the pipeline never touches CH.
• Data-source HTTP: prototype the exact request in chat with discoveryRequestTool (or mcpListToolsTool+mcpCallToolTool) until a 2xx with the data you want, then copy the args verbatim. Same backend as the SDK proxy → a 200 in chat is a 200 from the activity. Load /discovery-api first.

4.4 Write the workflow

Read references/code-conventions.md now — in full, before any code. It is the single source of truth for pipeline code, and the §7.1 verifier will judge your file against every rule in it: the three upload-traps T1–T3 (sandbox + imports, string-name activities with result_type=, O(1) activities), correctness C1–C6 (determinism, credentials, tenant/secrets, connection routing, errors/retries, cancellation), efficiency E1–E7 (the 4 GiB survival kit), logging L1–L5. Each post-upload mistake costs an S3 round-trip + worker reload — the rules exist to catch all of it before upload.

Write from the closest cookbook recipe, then check the draft against the rules file section by section.

4.5 Validate locally — the gate before any MCP write

All three MUST pass before create/update/run (and before execute if you edited code this session). If any fails, fix and re-run all three — only a clean sweep earns the MCP call.

ruff check <file.py>
pyright <file.py>
python -m pipeline_sdk validate <file.py> --name <WorkflowName> --json

--name is the @workflow.defn(name=…) value (= workflow_name you pass the tool), NOT the class name. Exit 0=valid / 1=errors / 2=not found. Error codes: SYNTAX_ERROR, FORBIDDEN_IMPORT, SANDBOXED_NOT_FALSE, MISSING_WORKFLOW_RUN, DUPLICATE_NAME, WORKFLOW_NAME_NOT_FOUND, ACTIVITY_NOT_ASYNC, IMPORT_ERROR, SMOKE_FAILURE, SMOKE_TIMEOUT. No isolated sandbox available → add --no-smoke (static checks only).

4.6 Run it

Pick the path by intent, not size: a one-off answer to a task → adhoc; a pipeline the user will keep, re-run, or schedule → persist it in the DB (even with no schedule) and iterate via the upload-URL, so the code never re-enters the token stream.

• Ad-hoc / one-off (you need the result once, nothing to keep) → runTemporalPipelineCodeTool({…, wait_for_result:false}) → poll getTemporalPipelineRunResultTool. The code rides inside the tool call — fine for a single shot, wasteful the moment you iterate.
• Real pipeline → persist, then iterate out-of-band (the default once it's more than a one-off — even with NO schedule): createTemporalPipelineTool once (code crosses the wire one time) → per change generateTemporalPipelineUploadUrlTool → curl -X PUT "<url>" -H "Content-Type: text/x-python" --data-binary @file.py → executeTemporalPipelineTool (re-syncs S3). Why: the PUT ships the .py straight to S3, so the code never goes back through a tool call — you stop re-paying tokens for the whole file on every iteration. Keep is_scheduled off until a cron is actually wanted; a persisted unscheduled pipeline runs on demand via executeTemporalPipelineTool.
• First run on a small slice — one day / one file / a LIMIT — and watch it through §6.1; go full-volume only once the slice run is clean. Cheaper than debugging an hour-long OOM.
• Scheduling (add later via update, or set at create): is_scheduled:true + cron_expression (UTC) + overlap_policy + catchup_window_seconds. New code doesn't hot-swap into a RUNNING instance — the next execute/trigger picks it up once the worker reloads (~20–60 s, §6.1). Turn a schedule on only after the §7.1 verify pass — once cron is live a missed bug fires on every tick, and under SKIP a wedged RUNNING run silently blocks all future ticks. After enabling, triggerTemporalPipelineScheduleTool once to prove the schedule itself fires.

Overlap policy — pick deliberately:

catchup_window_seconds: triggers missed while the worker was down are backfilled within this window (default 60). Hourly+ jobs → 3600 so one missed tick replays; frequent jobs → keep it small to avoid trigger storms.

5.0 Edit an existing pipeline

PATCH, never rewrite — start from the live code and apply a minimal diff; another agent or the customer may have edited it since you last saw it. Never call createTemporalPipelineTool again (it duplicates).

graph LR
    L["Locate"] --> P["Pull live code"]
    P --> U["Understand"]
    U --> D["Minimal diff"]
    D --> V["Validate §4.5"]
    V --> Push["Push: update / upload-URL"]
    Push --> T["Test: execute + observe §6.1"]
    T --> Fin["Finish §7"]

1. Locate — have pipeline_id? Skip ahead. Otherwise listTemporalPipelinesTool → pick by name/category.
2. Pull — getTemporalPipelineTool({pipeline_id}) → write response.code to your sandbox file as the baseline; note its connection_ids, schedule, and current metadata.graph_ui/details_ui.
3. Understand — read the code: which activities, connections, PipelineState keys, and schedule exist. Don't touch what you don't understand.
4. Minimal diff — apply ONLY the change the user asked for. Don't rename, reformat, or reorder for tidiness — every unrelated diff is a prod regression risk. Keep activity names, decorators, import order, helper structure. If the diff adds a new CH read or a new data-source request, run §4.3 recon (DESCRIBE / discovery-probe) before writing it — the no-guessing-names rule holds on edits too. New or changed code follows references/code-conventions.md — Read it before diffing, same as §4.4.
5. Validate — §4.5, all three clean on the patched file.
6. Push — pick by what changed:
   • Code-only change → upload-URL flow (generateTemporalPipelineUploadUrlTool → curl -X PUT … --data-binary @file.py, §4.6): ships the .py to S3 without sending workflow_code through a tool call, so you don't re-pay tokens for the whole file.
   • Non-code fields too (workflow_name, cron_expression, params, overlap_policy, schedule toggle) → updateTemporalPipelineTool({pipeline_id, …}).
   • Adding/removing a connection alias → update the pipeline's connection_ids to match prepare_credentials(connection_aliases=…).
   • The worker doesn't hot-swap a RUNNING instance — execute/next trigger picks up fresh code after reload (~20–60 s); schedule changes affect only future triggers.
   • Risky edit to a scheduled pipeline? De-risk before the next tick runs the new code: adhoc-run the patched file once via runTemporalPipelineCodeTool (isolated — never touches the live record), or pauseTemporalPipelineScheduleTool for the edit and resume once verified. The §4.5 gate catches static errors, not a runtime/data failure that would otherwise fire on cron.
7. Test — executeTemporalPipelineTool → confirm via the debug loop (§6.1).
8. Finish — re-run §7 (verify + PATCH the visualization, §7.2).

Wholesale regeneration only when the user asks to redesign, or getTemporalPipelineTool returns no usable code.

6.0 Examples & debugging

All code examples live in references/cookbook.md — runnable, current-API recipes to copy and adapt: minimal smoke test, write a file to S3, durable cursor/state (PipelineState), data-source call via proxy, internal MCP-tool call (email / ClickHouse), large-file streaming multipart upload, native warehouse driver, resumable long activity, parallel fan-out. Start any non-trivial workflow from the closest recipe, adapt, then run the §4.5 gate.

6.1 Debugging a run

Escalate from the cheapest signal: result → workers → logs → run files. Full reference — observability-tool fields and the complete symptom→cause→fix catalog: references/debugging-and-troubleshooting.md.

1. getTemporalPipelineRunResultTool({run_id}) / describeTemporalPipelineRunTool — status + result/error.
2. getTemporalPipelineWorkersTool() — crashed/throttled pod, or a code bug? Check last_error.reason (OOMKilled/CrashLoopBackOff), restart_count, resources.cpu_throttled_pct. Empty workers:[] = scaled to zero (next execute cold-starts 30-60 s).
3. getTemporalPipelineRunLogsTool({run_id, level:'error'}) — narrow with contains/activity_id; tail a RUNNING run with direction:'forward' + cursor.
4. Run files — fetch any file the run wrote to its S3 prefix (§2.2). When stuck, have the failing activity dump its raw payload (no PII) under secret.s3.prefix, re-run, read it back.

Every symptom in that catalog maps back to a numbered rule in references/code-conventions.md — the fix is almost always "apply the rule you skipped".

7.0 Finish — two focused passes

Once the pipeline works and is persisted, close out with two passes. Delegate each to a sub-agent when the runtime has one (fresh context, no authoring bias). No sub-agent tool available? Run the same pass yourself, in full, in this context — read the reference file and work through it item by item before reporting done. Never skip the passes.

7.1 Verify (skeptic)

Inputs: the final .py + references/code-conventions.md (add references/pipeline-state.md when the pipeline uses state — it ends with its own checks block). The verifier protocol is the last section of the rules file: a resource-profile trace first (assume data is LARGE), then every rule — T1–T3, C1–C6, E1–E7, L1–L5, P1–P5 — judged PASS/FAIL against the actual code with quoted lines, not a summary. Fix every real FAIL, re-run §4.5. Don't ship on your own say-so.

7.2 Visualize

Inputs: the pipeline_id + references/visualization.md. Read the live code via getTemporalPipelineTool, map the workflow to 2-5 business-language components, fill references/graph-ui-template.html + references/details-ui-template.html, and save metadata.graph_ui + details_ui + category via updateTemporalPipelineTool. On a pipeline that already has a visualization, PATCH it (minimal diff — keep existing ids, palettes, icons, ordering), never regenerate. Skip for adhoc runTemporalPipelineCodeTool tests (no pipeline_id).

Final checklist (before you call it done)

• §4.1 scoped: type · volume · cadence — probed live, not guessed
• references/code-conventions.md read in full; draft checked rule-by-rule (T1–T3 · C1–C6 · E1–E7 · L1–L5)
• §4.5 gate clean: ruff + pyright + pipeline_sdk validate
• §4.6 ran: persisted unless a one-off; small slice first, observed via §6.1
• §7.1 skeptic verify ran (sub-agent or self) — before any schedule
• §4.6 schedule deliberate: overlap policy · catchup window · UTC cron — enabled only after verify
• §5 (edits): minimal diff off live code; connection_ids synced; no duplicate create
• §7.2 graph_ui + details_ui + category present (PATCHed on update)