Talend ans agent
An AI-powered noise suppression and defect triage agent addressing an industry-wide gap in observability of data integration platforms. It enriches alerts with execution context, failed components, downstream dependency impact, and LLM-summarized notifications.
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Work on this Talend Alert Noise Suppression Azure Functions app, including Talend task/plan parsing, retry classification, alert storage, digest email, and optional OpenAI summaries.
SKILL.md
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Talend ANS Agent Skill
Use this skill when modifying the Talend Alert Noise Suppression source code in this repository.
First Read
- Read
AGENTS.md. - Read
function_app.py. - Check whether
alert_recipients.jsonor any edited file contains unresolved merge markers.
Code Map
Settings: all environment-driven runtime configuration.TalendClient: Talend API calls.SqliteAlertStoreandAzureSqlAlertStore: duplicate alert persistence contracts; keep schemas aligned.EmailNotifier: daily digest SMTP delivery.OpenAIErrorSummarizer: optional JSON-mode summary fallback.- Decision helpers: retry/noise classification, rule-based summaries, master-job dependency parsing, plan dependency parsing.
- Timer triggers:
poll_talend_alertsandsend_daily_digest.
Endpoint Flow To Preserve
Shared polling starts with GET /processing/executables/tasks/executions. Read response items, group by taskId, and keep the latest run using finishTimestamp, startTimestamp, then triggerTimestamp.
For every latest failed execution, call GET /monitoring/observability/executions/{executionId}/component.
For MANUAL task failures:
- Use
taskId,executionId,executionStatus,executionType,errorMessage, and component metrics to classify the failure. - Retry only transient network/engine/platform failures with
POST /processing/executionsand payload{"executable": "<taskId>", "logLevel": "WARN"}. - Treat data, component, permission, syntax, and ambiguous failures as valid alerts.
For MANUAL Master Jobs:
- Detect child jobs from component metrics where
connector_type == "tRunJob". - Order children by numeric suffix in
connector_id, such astRunJob_1beforetRunJob_2. - Use the first
tRunJobwith a stack trace as the failed child. - Report later
tRunJobcomponents as downstream jobs that should not be expected to run.
For PLAN failures, enrich context in this exact order:
GET /monitoring/observability/executions/{executionId}/componentGET /processing/executables/plans/executions, matching byplanIdto findplanExecutionIdGET /processing/executions/plans/{planExecutionId}/steps, extracting failed step IDs/statusGET /orchestration/executables/plans/{planId}, extracting planname, planexecutable, step names, task names,chart,nextStep, andflows
For retryable plan failures, call POST /processing/executions/plans with payload {"executable": "<plan executable>"} where the executable comes from /orchestration/executables/plans/{planId}.
Change Checklist
- Keep new settings in
Settingswith safe defaults. - Keep Talend parsing tolerant of missing keys and alternate sample response shapes.
- Update both storage backends when alert row fields change.
- Classify unknown failures as valid failures.
- Retry only transient platform/network failures.
- Preserve digest filtering through
include_in_digest. - Do not expose secrets from
local.settings.json. - Run
python -m compileall function_app.pyafter Python changes.
Common Extension Points
- New transient failure: add a precise pattern to
TRANSIENT_PATTERNS. - New non-retryable failure: add a precise pattern to
NON_RETRIABLE_PATTERNS. - New human-readable summary: update
summarize_error_rule_based. - New Talend component relationship: extend
parse_master_job_dependencyor add a similarly tolerant helper. - New plan response shape: adjust
flatten_plan_stepsorenrich_plan_contextwithout breaking the linkedchart.nextStepshape. - New alert column: update SQLite DDL/migration, SQL Server DDL/migration, insert field lists, digest HTML if user-facing, and any test/sample data.
Verification
Minimum syntax check:
python -m compileall function_app.py
For runtime verification, install dependencies and start the Functions host:
.\.venv\Scripts\python -m pip install -r requirements.txt
func host start
Use real Talend/OpenAI/SMTP calls only when the required environment variables are present and the user expects networked verification.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most context ai engineering skills give in 938 tokens
Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-07
- Dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
- Dispatch a final code reviewer after all tasksin 33 of 1193, across 8 files
- Provide full task text to the subagentin 30 of 1193, across 9 files
- Review spec compliance before code qualityin 27 of 1193, across 10 files
- Make the hook script executablein 26 of 1193, across 8 files
- Re-snapshot after navigation or DOM changesin 25 of 1193, across 19 files
- Read files before editing themin 22 of 1193, across 11 files
- Answer subagent questions before proceedingin 22 of 1193, across 7 files
- Mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
- Merge hook into existing settingsin 21 of 1193, across 3 files
- Ask if installation is global or projectin 20 of 1193, across 2 files
- Copy the hook script to target locationin 20 of 1193, across 2 files
Said here and by no other author read
- check for unresolved merge markers
- keep storage backend schemas aligned
- keep new settings safely defaulted
- make talend parsing tolerate missing keys
- update both storage backends for field changes
- classify unknown failures as valid failures
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.