debugging-signals-pipeline
Skill727 starsChanged yesterdayMITView on GitHub
Debug the signals pipeline locally end-to-end. Covers emitting test signals from fixtures, monitoring Temporal workflows via the REST API, reading sandbox agent logs from object storage, inspecting Docker sandbox containers, and diagnosing common failures (stale ClickHouse embeddings, agentsh network denials, inactivity timeouts). Use when a signal isn't reaching the inbox, a signal-report-summary workflow fails, or a sandbox task run times out.
- Reads credentials
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 49
Always clean up before re-emitting to avoid stale embeddings causing phantom report matches.
- Very specific: line 91
When a workflow has continued-as-new, use the execution.runId query param:
- Very specific: line 162
Fix: Add CLICKHOUSE_DATABASE=posthog to .env and restart workers.
- Distinctive: line 183
Diagnosing: Read the agent log from object storage (see above).
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Debugging the signals pipeline
Pipeline flow
emit_signals_from_fixture
→ signal-emitter (Temporal workflow)
→ buffer-signals (batches signals, 5s flush timer)
→ safety_filter_activity
→ flush_signals_to_s3_activity
→ signal_with_start_grouping_v2_activity
→ team-signal-grouping-v2 (30s batch collect window)
→ read_signals_from_s3_activity
→ get_embedding_activity + generate_search_queries_activity
→ run_signal_semantic_search_activity
→ match_signal_to_report_activity
→ assign_and_emit_signal_activity
→ wait_for_signal_in_clickhouse_activity
→ (if new report) signal-report-summary
→ fetch_signals_for_report_activity
→ report_safety_judge_activity
→ select_repository_activity (spawns Docker sandbox)Emitting test signals
# Emit a single signal from the Zendesk fixture at offset 26
DEBUG=1 python manage.py emit_signals_from_fixture --type zendesk --team-id 1 --offset 26 --limit 1
# Clean up all signal data before re-emitting (avoids stale matches)
DEBUG=1 python manage.py cleanup_signals --team-id 1 --yes
# Check pipeline status
python manage.py signal_pipeline_status --team-id 1 --wait --expected-signals 1 --poll-interval 10Always clean up before re-emitting to avoid stale embeddings causing phantom report matches.
Monitoring Temporal workflows
The Temporal UI runs at http://localhost:8081. The REST API is useful for scripted inspection.
List recent workflows
curl -s 'http://localhost:8081/api/v1/namespaces/default/workflows?query=ORDER+BY+StartTime+DESC&maximumPageSize=15' \
| python3 -c "
import sys, json
for wf in json.load(sys.stdin).get('executions', []):
info = wf['execution']
status = wf['status'].replace('WORKFLOW_EXECUTION_STATUS_', '')
print(f'{wf[\"startTime\"][:19]} {status:20s} {wf[\"type\"][\"name\"]:35s} {info[\"workflowId\"][:90]}')
"Inspect workflow history
WF_ID="buffer-signals-1" # or team-signal-grouping-v2-1, signals-report:1:<uuid>
curl -s "http://localhost:8081/api/v1/namespaces/default/workflows/$WF_ID/history?maximumPageSize=200" \
| python3 -c "
import sys, json
for event in json.load(sys.stdin).get('history', {}).get('events', []):
etype = event['eventType'].replace('EVENT_TYPE_', '')
etime = event['eventTime'][:19]
details = ''
for key, attrs in event.items():
if key.endswith('Attributes') and isinstance(attrs, dict):
if 'activityType' in attrs: details = attrs['activityType'].get('name', '')
elif 'signalName' in attrs: details = f'signal: {attrs[\"signalName\"]}'
elif 'startToFireTimeout' in attrs: details = f'timer: {attrs[\"startToFireTimeout\"]}'
elif 'failure' in attrs: details = f'FAILED: {attrs[\"failure\"].get(\"message\", \"\")[:200]}'
if details: print(f' {etime} {etype:50s} {details}')
"Inspect a previous run (continued-as-new)
When a workflow has continued-as-new, use the execution.runId query param:
curl -s "http://localhost:8081/api/v1/namespaces/default/workflows/$WF_ID/history?execution.runId=<run-id>&maximumPageSize=200"Reading sandbox agent logs
Agent logs are stored in object storage (MinIO locally) as JSONL files.
The log URL is on the TaskRun model.
# In Django shell (python manage.py shell)
from products.tasks.backend.models import TaskRun
from posthog.storage import object_storage
# Find the most recent task run
run = TaskRun.objects.order_by("-created_at").first()
print(f"status: {run.status}, error: {run.error_message}")
print(f"log_url: {run.log_url}")
# Read the log
content = object_storage.read(run.log_url, missing_ok=True)
# Print last 3000 chars (most useful — shows what happened before failure)
print(content[-3000:])The log is JSONL with entries like:
{
"type": "notification",
"timestamp": "...",
"notification": { "jsonrpc": "2.0", "method": "_posthog/console", "params": { "level": "debug", "message": "..." } }
}Key things to look for in the log tail:
- agentsh network events —
DENYentries show blocked network calls _posthog/progressevents — show which setup step the sandbox reached_posthog/consoledebug messages — show sandbox provisioning, cloning, agent startup
Inspecting Docker sandbox containers
# List running sandbox containers
docker ps --filter "name=task-sandbox" --format "table {{.Names}}\t{{.Status}}\t{{.Image}}"
# See processes inside a running sandbox
docker exec <container-name> ps aux
# Read the agent-server log inside the container (while it's still running)
docker exec <container-name> cat /tmp/agent-server.logThe container is named task-sandbox-<task-id>-<random> and uses the posthog-sandbox-base image.
Containers are ephemeral — they're removed after the task run completes, so inspect while running.
Common failures
SignalReport matching query does not exist
The assign_and_emit_signal_activity tried to assign a signal to a report that doesn't exist.
Usually caused by stale embeddings in ClickHouse after a cleanup_signals that failed to delete them.
Root cause: CLICKHOUSE_DATABASE not set in .env. The cleanup command uses sync_execute
which connects to the CLICKHOUSE_DATABASE (defaults to default), but the embedding tables
live in the posthog database.
Fix: Add CLICKHOUSE_DATABASE=posthog to .env and restart workers.
Manual cleanup of stale embeddings:
curl -s 'http://localhost:8123/' --data-binary \
"ALTER TABLE posthog.sharded_posthog_document_embeddings_text_embedding_3_small_1536 DELETE WHERE product = 'signals' AND team_id = 1 SETTINGS mutations_sync = 1"Verify embeddings are clean:
curl -s 'http://localhost:8123/' --data-binary \
"SELECT count() FROM posthog.sharded_posthog_document_embeddings_text_embedding_3_small_1536 WHERE team_id = 1 AND product = 'signals'"Run timed out due to inactivity on select_repository_activity
The sandbox Claude agent went idle for longer than TASKS_INACTIVITY_TIMEOUT_SECONDS. When unset
this falls back to a 2 hour timeout — set TASKS_INACTIVITY_TIMEOUT_SECONDS=30 locally to force fast failures.
Diagnosing: Read the agent log from object storage (see above). Check the tail for:
- agentsh network denials —
DENY host.docker.internalmeans the MCP server URL is blocked by the sandbox network policy. TheSIGNALS_REPO_DISCOVERYenvironment's domain allowlist doesn't includehost.docker.internal. - No log content at all — sandbox failed to start, check Docker container logs.
- Claude API errors — check if
ANTHROPIC_API_KEYis valid.
buffer-signals sits idle, never receives signals
The signal-emitter completed but buffer-signals never got the submit_signal.
This happens when the emitter sent the signal to a previous buffer run that then continued-as-new,
and the new run started fresh without the pending signal. Re-emit the signal.
ClickHouse embedding tables "not found" during cleanup
The tables exist in the posthog database but sync_execute queries the default database.
# Verify tables exist
curl -s 'http://localhost:8123/' --data-binary "SHOW TABLES FROM posthog LIKE '%embed%'"
# Check current CLICKHOUSE_DATABASE setting
grep CLICKHOUSE_DATABASE .envUseful management commands
| Command | Purpose |
emit_signals_from_fixture | Emit test signals from JSON fixtures |
DEBUG=1 cleanup_signals --team-id N --yes | Delete all signal data and terminate workflows |
signal_pipeline_status --team-id N --wait | Wait for pipeline to finish processing |
list_signal_reports --team-id N --signals --json | Inspect grouping results |
ingest_signals_json <file> --team-id N | Ingest pre-processed signals from JSON |
ingest_report_json <file> --team-id N | Seed a pre-researched report (skip sandbox) |
Key file locations
- Pipeline workflow definitions:
products/signals/backend/temporal/ - Buffer workflow:
products/signals/backend/temporal/buffer.py - Grouping workflow:
products/signals/backend/temporal/grouping_v2.py - Report summary workflow:
products/signals/backend/temporal/summary.py - Docker sandbox implementation:
products/tasks/backend/logic/services/docker_sandbox.py - Sandbox Dockerfiles:
products/tasks/backend/sandbox/images/ - Agent log polling:
products/tasks/backend/logic/services/custom_prompt_internals.py - Cleanup command:
products/signals/backend/management/commands/cleanup_signals.py - Management command docs:
products/signals/backend/management/CLAUDE.md
Other versions1 project uses a version of this.
Word for word · 1
- PostHog/posthog★ 40k100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository124 other files it gives its agents.
- AGENTS.mdAGENTS.md
- common/AGENTS.mdAGENTS.md
- hog/AGENTS.mdAGENTS.md
- proto/AGENTS.mdAGENTS.md
- rust/AGENTS.mdAGENTS.md
- .cursor/rules/django-python.mdcCursor rule
- .cursor/rules/react-typescript.mdcCursor rule
- .cursor/rules/rust.mdcCursor rule
- adding-activity-loggingSkill
- adding-api-scopesSkill
- adding-inbound-webhooksSkill
- adding-inbox-sourcesSkill
- adding-ingestion-warningsSkill
- adding-mcp-store-serversSkill
- adding-personhog-rpcSkill
- adding-product-alertingSkill
- adding-project-secret-api-key-authSkill
- adding-today-highlightsSkill
- adopting-generated-api-typesSkill
- analyzing-experiment-precompute-canarySkill
- analyzing-experiment-query-performanceSkill
- analyzing-insights-across-teamsSkill
- announcing-behavior-changesSkill
- auditing-llm-gateway-paritySkill
- auditing-warehouse-source-coverageSkill
- authenticating-to-clickhouseSkill
- authoring-ci-workflowsSkill
- building-product-empty-statesSkill
- building-the-toolbarSkill
- clickhouse-migrationsSkill
- debugging-ci-failuresSkill
- debugging-local-replaySkill
- debugging-local-task-agent-runsSkill
- debugging-mcp-analyticsSkill
- debugging-table-access-deniedSkill
- depot-ciSkill
- depot-container-buildsSkill
- depot-github-runnersSkill
- developing-subscriptionsSkill
- django-migrationsSkill
- django-startup-timeSkill
- documenting-warehouse-sourcesSkill
- editing-agents-mdSkill
- establishing-code-ownershipSkill
- extending-hobby-smoke-testsSkill
- extending-personhog-test-harnessSkill
- finding-llm-gateway-migration-candidatesSkill
- fixing-flaky-testsSkill
- formatting-slack-messagesSkill
- gating-production-deploysSkill
- gating-sensitive-actionsSkill
- generating-clickhouse-query-performance-reportsSkill
- hogliSkill
- implementing-mcp-toolsSkill
- implementing-mcp-ui-appsSkill
- implementing-warehouse-sourcesSkill
- improving-drf-endpointsSkill
- improving-mcp-toolsSkill
- ingestion-pipeline-doctor-nodejsSkill
- instrumenting-first-party-metricsSkill
- The first 60 shown.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

