Iterate on UI with a person watching and giving live visual feedback. Pointer skill: the person uses their own tab on the stack and the haven orb; you read their notes and check fixes with haven browser. Use when the user says 'browser pair', 'paired browser', 'let's look at this together', 'open chromium', or wants…
Check that a feature works by walking its scenarios in a real browser and writing a pass/fail report with evidence. A recipe over haven browser: no test files, no browser commands of its own. Use when someone says 'browser test', 'verify this in the browser', 'walk the scenarios', 'check the feature live', or hands…
Maintain the canonical LangWatch feature map (feature-map.json at the repo root) and its human-readable companion FEATUREMAP.md. Use when adding or renaming a feature, a REST namespace, a UI route, an MCP tool, a CLI command or a user-facing skill, so the central registry keeps naming what the code actually serves.…
MCP server "playwright" as configured in langwatch/langwatch. Launched with bash -c d=$PWD; while [ "$d" != / ] && [ ! -f "$d/dev/scripts/playwright-mcp.sh".
★not rated 5.0k▲
+30 todayA
tokens not measured
originalApache-2.0
MCP server "playwright-headed" as configured in langwatch/langwatch. Launched with bash -c d=$PWD; while [ "$d" != / ] && [ ! -f "$d/dev/scripts/playwright-mcp.sh".
Expert AI engineering consultant for your agent development practices. Audits your codebase, traces, evaluations, and scenarios against best practices, then guides you to close the gaps, starting from low-hanging fruit and going deeper. Use when you want to level up your agent's engineering quality.
Turns production evidence into tested improvements for your AI agent. Forms hypotheses from real traces and analytics, explains the reasoning behind each one, then executes with the user: scenario tests that reproduce production failures, prompt and code changes as reviewable PRs, new evaluators and monitors that…
Deep-dive diagnosis of how your AI agent behaves in production. Explores LangWatch analytics and traces end to end to map failure patterns, dissatisfied users, token cost hotspots, edge cases, behavior changes, and outliers, then delivers an HTML report where every finding links to real example traces. Use when you…
Connect the codebase's AI agent to LangWatch agent simulations, so test suites run against the real agent process. Adds a small connect function beside the service startup that calls the agent already in the codebase, which opens an outbound connection and registers the agent with its environment and its run…
★not rated 5.0k▲
+30
changed 3d agoASkillSpector: pass102 tokens
originalApache-2.0
Investigates the context economics of your own coding-agent sessions in LangWatch. Reads real sessions to find where carrying a fat context stops paying for itself, measured in cache rebuilds, compactions and cost per turn, and delivers a report with the context size your sessions should stay under, with example…
Generate realistic synthetic evaluation datasets by analyzing the user's codebase, prompts, production traces, and reference materials. Interactive and consultant-style. Asks clarifying questions, proposes a plan, generates a preview for approval, then delivers a complete dataset uploaded to LangWatch. Use when user…
★not rated 5.0k▲
+30
changed 3d agoBSkillSpector: warn75 tokens
originalApache-2.0
Debug and improve your LangWatch traces. Inspects production traces for missing input/output, disconnected spans, unlabeled traces, and missing metadata. Use when traces look broken or incomplete.
★not rated 5.0k▲
+30
changed 3d agoASkillSpector: pass41 tokens
originalApache-2.0
Root-cause production errors and misbehaving agent runs with LangWatch. Finds errored traces, inspects spans, checks monitor and evaluator scores, then narrows to a root cause. Use when something is failing or misbehaving in production (errors, bad answers, latency spikes).
Drive the page the user has open through live UI actions. List the actions a page accepts, call them with typed payloads, and read the live state including unsaved edits. Use when the user is looking at a page you can operate, such as the evaluations workbench, and a change should happen in front of them rather than…
Investigate failing experiments and evaluations with LangWatch. Triage a failing experiment run to the exact rows and evaluator scores that regressed, then to a root cause. Use when an experiment fails, scores drop, or evaluations regress.
★not rated 5.0k▲
+30
changed 3d agoASkillSpector: pass51 tokens
originalApache-2.0
Evaluate multimodal AI agents that process images, audio, PDFs, or other files. Sets up evaluations using LangWatch's LLM-as-judge with image inputs, Scenario's multimodal testing, and document parsing evaluation patterns. Use when your agent handles non-text inputs.
Compatibility router for LangWatch evaluation requests. Use only when the user asks for evaluations without making it clear whether they mean pre-deployment experiments or production online evaluations. Routes the request to the focused companion skill and does not implement either workflow itself.
Create and run LangWatch experiments for pre-deployment batch testing. Use when the user wants to test an agent against a dataset, compare prompts or models, benchmark quality, detect regressions, or add a CI quality gate. Do not use for production monitoring or guardrails.
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
Open a real pull request. Clone a repo, branch, commit, push, and open a PR authored by the LangWatch app on behalf of the requesting user. Use when the user asks to open a PR, fix something in a repo and submit it, send a patch, raise a pull request, or otherwise land a code change on GitHub.
★not rated 5.0k▲
+30
changed 3d agoD73 tokens
originalApache-2.0
Take your AI agent to the next level with full LangWatch integration. Adds tracing, prompt versioning, evaluation experiments, and simulation tests in one go. Use when the user wants comprehensive observability, testing, and prompt management for their agent.
Author a saved analytics chart from a plain question and place it on a dashboard. Discovers the LangWatchQL analytics schema, writes and test-runs the SQL, saves it as a chart with a Vega-Lite specification, and places it where the team already looks. Use when asked to build, save, run, or dashboard a metric or chart.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: