triage-orchestra-pipeline

name

triage-orchestra-pipeline

description

Diagnose a failed Orchestra pipeline, open a fix PR, validate it on a branch run, then present a human-readable triage summary and STOP for user approval before merging. Use this when the user wants to review the fix before it goes to main — not for fully automated fixes. Trigger on phrases like "triage my pipeline", "show me what's broken", "investigate but don't fix yet", "prepare a fix for review", or when the user explicitly wants a review gate before applying changes. Also trigger when the user describes a symptom in a downstream system ("dashboard looks wrong", "chart is stale", "dbt model has bad data") even if no pipeline error exists — the skill will trace the symptom upstream.

Triage Orchestra Pipeline

Diagnose a failed (or symptom-reported) pipeline, open a fix PR, validate it on the branch, then present a compact triage report and stop for human approval. Nothing merges to main until the user says so.

Shared references

Orchestra docs live under references/orchestra/. Index and layout: ../../references/orchestra/README.md. Paths below are relative to this skill folder.

Pipeline

• ../../references/orchestra/pipeline/diagnosis-patterns.md — error classification
• ../../references/orchestra/pipeline/remediation-playbooks.md — fix strategies
• ../../references/orchestra/pipeline/knowledge-store.md — (optional) local fix history; prefer your client's persistent memory

MCP

• ../../references/orchestra/mcp/tools-quick-ref.md — tool arguments and behaviour (use get_pipeline to read a pipeline's full definition)

Workflow

Step 0 — Parse the prompt: error-first or symptom-first?

Before doing anything, classify the input:

Error-first — the pipeline has a red status, or the user gives a run ID/URL/UUID, or says "my pipeline failed". Skip to Step 1.

Symptom-first — the user describes something wrong in a downstream system without mentioning a pipeline error: "Lightdash dashboard looks wrong", "numbers are off", "chart is stale", "dbt model returning bad values". Go to Step 0b.

---

Step 0b — Symptom-first: start at the named layer, traverse upstream

Goal: Find the root cause by starting at the system the user named and walking backwards through the pipeline until something is wrong.

0b-1. Map the symptom to a pipeline task

Extract the system keyword from the prompt (e.g. "Lightdash", "dbt", "Fivetran"). Call list_pipeline_runs for the relevant pipeline to get the most recent run. Call list_task_runs for that run to get all tasks and their execution order.

Match the keyword to a task via the integration field (e.g. LIGHTDASH, DBT_CORE, FIVETRAN). This is the entry task. If no pipeline is named, ask the user which pipeline to check before proceeding.

0b-2. Inspect the entry task

For the entry task in the most recent run, check three things in order:

1. Run status. Was it SUCCEEDED, WARNING, SKIPPED, or FAILED? Note it — even a SUCCEEDED task can produce wrong output if its inputs were bad.

2. Recent code commits. Identify the git repo for this task from runParameters (branch, commit, platformLink). Fetch the last 10 commits on that branch:

   gh api repos/<owner>/<repo>/commits?sha=<branch>&per_page=10
   

   For each commit in the last 7 days, check the diff for changes to files relevant to the symptom. Examples:

   • Lightdash symptom → check .yml metric/dimension definitions, schema.yml joins
   • dbt symptom → check model SQL, schema tests, macros, seeds
   • Fivetran symptom → check connector config, transformation logic Flag any commit whose diff touches the area the user described as wrong.

3. Artifacts/logs. Download available artifacts (e.g. dbt run_results.json) and check for warnings or row count anomalies even on technically passing runs.

Decision:

• Suspicious commit or anomaly found → this is the root cause. Proceed to Step 2.
• Nothing found → move to the next upstream task (Step 0b-3).

0b-3. Traverse upstream one layer at a time

Walk backwards through the pipeline task list by execution order. For each upstream task, repeat Step 0b-2: check run status, recent commits in its repo, and artifacts.

Example traversal order for a Fivetran → dbt → Lightdash pipeline:

Prompt: "Lightdash dashboard looks wrong"

Layer 1: Lightdash task
  → status: SUCCEEDED
  → commits: no changes in last 7 days
  → no issue found → move upstream

Layer 2: dbt task
  → status: SUCCEEDED
  → commits: found commit 3 days ago changing model SQL in affected area
  → STOP — root cause identified

(Layer 3: Fivetran task — not reached)

Stop as soon as a suspicious commit or anomaly is found. Do not keep looking once a cause is identified.

If traversal reaches the source with nothing found across all layers, report: "No issues found in the last 7 days across all layers" with a table of what was checked at each layer (task, status, commits reviewed, conclusion).

0b-4. Treat the suspect commit as the root cause

Once identified, classify it exactly as you would an error-first diagnosis:

• Error category: CODE_ERROR (logic change), DATA_ERROR (bad values), CONFIG_ERROR, etc.
• Root cause: the specific commit, file, and line that caused the symptom

Proceed to Step 2.

If the commit represents an intentional business logic change (not a bug), do not open a revert PR. Instead, present the triage summary with a [User action needed] resolution: explain which commit introduced the change and ask the user to decide whether to revert or adjust the downstream layer to match the new logic.

---

Step 1 — Identify & diagnose (error-first path)

Run the full diagnosis: find the failed run, get task runs, fetch logs/artifacts/operations, classify the error, identify root cause. Read ../../references/orchestra/pipeline/diagnosis-patterns.md; optionally recall similar past fixes from your client's persistent memory (or a local ../../references/orchestra/pipeline/knowledge-store.md if the user keeps one).

Do not present a verbose diagnosis block to the user yet. Collect all findings silently — the output comes at the end in the triage summary.

---

Step 2 — Open fix PR (do NOT merge)

Apply the fix exactly as in fix-orchestra-pipeline Step 5:

• For code fixes: branch off the failing branch, apply changes, push, open PR via gh pr create
• For config fixes: use update_pipeline if Orchestra-backed

Critical difference: Do NOT merge. Do NOT call gh pr merge. Leave the PR open.

Collect:

• PR number and URL
• Branch name
• Exact files and lines changed
• One-sentence reason the change fixes the failure

---

Step 3 — Validate on the fix branch

Trigger a pipeline run against the fix branch (not main) to prove the fix works before anyone merges it:

start_pipeline(
    alias_or_pipeline_id=<pipeline_id>,
    branch=<fix-branch-name>,
    environment=<same env as the failed run>
)

Poll get_pipeline_run_status every 60 s until terminal (SUCCEEDED / FAILED / WARNING).

• SUCCEEDED: Fix is validated. Proceed to Step 4.
• FAILED (same error): Fix didn't work. Return to Step 2, update the PR, re-validate.
• FAILED (different error): New problem uncovered. Diagnose it, extend the PR, re-validate.
• WARNING: Treat as passing unless the warning is in the same model/test that was originally failing. Note it in the triage summary.

Do not present anything to the user until the branch run reaches a terminal state.

If the pipeline writes data (ingestion, reverse-ETL), warn the user before running: "This pipeline writes data — branch run will also write. Proceed?"

---

Step 4 — Present triage summary and STOP

Output the triage summary in the format below, then explicitly stop. Do not schedule a wakeup. Do not merge anything. Wait for the user.

---

Triage output format

Optimised for Slack: scannable, no preamble, decision-ready.

Error-first format

## Triage: `<pipeline name>`

**What broke:** <one sentence — which task, which test/error>
**Why:** <one sentence — specific root cause>

| Value / change | Field | Origin |
|---|---|---|
| `VALUE` | `column_name` | API enum expansion / bad commit / etc. |

---
**How it was found:**
1. `list_task_runs` → dbt task FAILED, exit code 1
2. Downloaded `run_results.json` → 3 `accepted_values` test failures
3. `list_operations` → `REUSED` present in live operation data

---
**PRs opened:**

| PR | File | Change |
|---|---|---|
| [#N](url) | `models/staging/schema.yml` | Added `REUSED` to `operation_status` accepted values |

**Branch validation:** Run `<run-id>` on `<branch>` — ✅ SUCCEEDED / ❌ FAILED

---
**Why this fixes it:** <2–3 sentences on what the test does, why the new value broke it,
and why this is the right fix rather than a data issue.>

---
> **Ready to apply?** Reply `merge` to merge the PR(s) and trigger a production run,
> or `reject` to close them.

Symptom-first format

## Triage: `<pipeline name>` — symptom: "<user's description>"

**Entry layer:** <system the user named>
**Root cause found at:** <layer where the issue was identified>
**What changed:** Commit `<sha>` on `<date>` — "<commit message>"

---
**Layers checked:**

| Layer | Task status | Commits checked | Finding |
|---|---|---|---|
| Lightdash | SUCCEEDED | 0 in last 7d | Clean |
| dbt Core | SUCCEEDED | 4 in last 7d | ⚠️ Commit `abc1234` changed `store_sales_example.sql` |
| Fivetran | — | not reached | — |

---
**Suspect commit diff:**
- File: `models/clean/store_sales_example.sql`
- Change: `WHERE sales > 0` → `WHERE sales > 1000` (filters out low-value stores)

---
**PRs opened:**

| PR | File | Change |
|---|---|---|
| [#N](url) | `models/clean/store_sales_example.sql` | Reverted threshold change |

**Branch validation:** Run `<run-id>` on `<branch>` — ✅ SUCCEEDED / ❌ FAILED

---
**Why this fixes it:** <explanation of the causal chain from the commit to the symptom>

---
> **Ready to apply?** Reply `merge` to merge and trigger a production run,
> or `reject` to close the PR(s).
> If this was an intentional change, reply `intentional` and I'll investigate the
> downstream layer instead.

---

Approval handling

merge (or: "yes", "approve", "ship it", "lgtm"):

1. gh pr merge <N> --repo <owner/repo> --squash --delete-branch for each PR
2. start_pipeline(pipeline_id, environment=Production)
3. Poll until terminal, output resolution summary
4. (Optional) Record the fix in your client's persistent memory — or a local ../../references/orchestra/pipeline/knowledge-store.md if the user keeps one. Off by default.

reject (or: "no", "close it", "abandon"):

1. gh pr close <N> --repo <owner/repo> for each PR
2. Report closed. Stop.

intentional (symptom-first only — the commit was deliberate, not a bug):

1. Do not revert. Close the open PR.
2. Re-enter the traversal at the next downstream layer — the fix may need to be in Lightdash config to match the new dbt logic, rather than reverting dbt.
3. Re-present the triage summary for the downstream fix.

User provides feedback ("change X to Y", "also fix Z"):

1. Apply changes to the existing PR branch, push (do not open a new PR)
2. Re-run branch validation (Step 3)
3. Re-present triage summary with updated diff and new run result

---

Important notes

• Never auto-merge. The entire purpose of this skill is the human review gate.
• One PR per logical fix. If multiple files need the same change, one PR covers all.
• Symptom traversal stops at first finding. Do not keep looking once a cause is identified — present it and let the user decide if it's the right one.
• Commits are suspects, not convictions. If a commit looks related but you're not certain, say so in the triage summary and give the user the option to investigate further before opening a PR.
• Recall/record is optional. Past-fix memory is deferred to the calling agentic client (Claude Code memory, Cursor rules), with a local knowledge-store.md as an opt-in fallback — see fix-orchestra-pipeline. Never commit workspace-specific fix history to this repo.