output-workflow-reset
Re-run an Output SDK workflow from after a specific completed step, creating a new run that replays up to that point and re-executes subsequent steps. Use when iterating on a later step's prompt or logic without re-running the entire workflow, or when recovering from a failure that only affects steps after a known-good point.
What this skill does
# Rerun Workflow From a Step ## Overview This skill resets a workflow to re-run from after a specific completed step. The current run is terminated and a new run is created that replays the workflow up to the given step (reusing its recorded output), then re-executes every step after it. Use this to avoid re-running expensive early steps (like LLM calls or slow HTTP requests) when you only need to iterate on a later step. ## When to Use This Skill - A workflow failed late, but every step before the failure succeeded — rerun from after the last known-good step instead of starting over - You edited a prompt or step function that only affects steps after step N, and want to validate the change against the same upstream inputs - You want to branch off an existing run for debugging without re-paying the cost of its earlier LLM/API calls - Investigating non-determinism in a later step and want to hold earlier outputs constant ## When NOT to Use This Skill - The workflow is still running — stop it first with `npx output workflow stop <id>` or `terminate <id>` - The target step never completed — reset requires a completed step to replay up to (returns a `409`) - You actually want a clean run from scratch — use `npx output workflow run <name>` or `workflow start <name>` - Earlier steps had side effects you need to re-execute (e.g. writes to an external system) — the replay skips them ## Instructions ### Basic Syntax ```bash npx output workflow reset <workflowId> --step <stepName> npx output workflow reset <workflowId> --step <stepName> --reason "<why>" ``` | Flag | Short | Required | Description | |------|-------|----------|-------------| | `--step` | `-s` | yes | Name of the completed step to reset after | | `--reason` | `-r` | no | Free-text reason, recorded in Temporal history for auditability | The `workflowId` argument is required. The step name is the step function name as it appears in the trace (e.g. `fetchArticle`, `consolidateCompetitors`). ### Finding the Step Name Step names come from the workflow's execution trace: ```bash npx output workflow debug <workflowId> --format json ``` Look for step entries with `status: "completed"`. The step name is the one you pass to `--step`. ### What the Command Returns On success the CLI prints the original `workflowId` and a **new** `runId` — the new run created by the reset. The pre-reset run is terminated. ``` Workflow reset successfully Workflow ID: lead_enrichment-a1b2c3d4 New Run ID: 8f3e2a91-... Reset after step: consolidateCompetitors Reason: retrying with updated prompt ``` Use the new `runId` (via the pinned `workflow runs list`) to inspect the new execution. The `workflowId` is unchanged, so `workflow status` / `workflow result` will target the latest run by default. ## Examples **Scenario**: Rerun after fixing a downstream prompt ```bash # The workflow failed at `generateBlogPost`, but `consolidateCompetitors` # (the step before it) completed successfully. npx output workflow debug lead_enrichment-a1b2c3d4 --format json # ... confirms consolidateCompetitors completed # Edit src/workflows/lead_enrichment/prompts/[email protected] # Then rerun from after the last good step — skipping the expensive # competitor consolidation LLM call. npx output workflow reset lead_enrichment-a1b2c3d4 \ --step consolidateCompetitors \ --reason "Retry with updated blog-post prompt" ``` **Scenario**: Iterate on a late step without re-paying upstream costs ```bash # Workflow completed, but step output is wrong. Rerun just the last step. npx output workflow reset blog_evaluator-xyz789 --step analyze_claims # Check the new run's result npx output workflow result blog_evaluator-xyz789 ``` **Scenario**: Record an audit reason ```bash npx output workflow reset wf-12345 \ --step fetchCompanyData \ --reason "Source API returned stale data; rerunning after cache invalidation" ``` **Scenario**: Capture the new run ID for follow-up ```bash # Grab the new runId from the reset output, then watch it npx output workflow reset lead_enrichment-a1b2c3d4 --step lookupCompany npx output workflow status lead_enrichment-a1b2c3d4 npx output workflow result lead_enrichment-a1b2c3d4 ``` ## Error Handling | Error | Cause | Solution | |-------|-------|----------| | `404` Workflow or step not found | Wrong `workflowId` or `--step` name | Check with `npx output workflow runs list` and `workflow debug <id>` | | `409` Step has not completed | Target step is still running or never ran | Wait for the step to complete, or pick an earlier completed step | | API returned invalid response | Transport failure | Check services with `docker ps | grep output` and `curl http://localhost:3001/health` | ## Best Practices 1. **Start from `workflow debug`** — confirm which steps completed before picking a reset point 2. **Reset to the step whose output you want to keep** — reset runs everything *after* `<stepName>`; the step itself is not re-executed 3. **Record a `--reason`** — it shows up in Temporal history and helps teammates (and future you) understand why the run forked 4. **Prefer reset over full rerun for expensive workflows** — if early steps make multi-dollar LLM calls, reset saves real money 5. **Verify side effects first** — if an early step writes to an external system, the replay won't re-write, which is usually what you want but occasionally isn't ## Related Commands - `npx output workflow debug <id>` — find completed step names to pass to `--step` - `npx output workflow runs list` — see the new run created by reset alongside the terminated original - `npx output workflow status <id>` — check the new run's status - `npx output workflow result <id>` — get the new run's final output - `npx output workflow stop <id>` / `workflow terminate <id>` — stop a running workflow before resetting - `npx output workflow run <name>` / `workflow start <name>` — fresh run from scratch (no replay)
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.