Depuració
Detecció i correcció de problemes i anàlisi d’errors
screenpipe-logs
screenpipe
Retrieve and analyze Screenpipe CLI backend logs and desktop app logs for debugging
log-error-digest
zebbern
"Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or get distribution stats."
agent-improve
langwatch
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 capture production signals, and experiments that settle open questions. Use when you want to know what to do next to improve your agent.
span-timeline-events
triggerdotdev
Use when adding, modifying, or debugging OTel span timeline events in the trace view. Covers event structure, ClickHouse storage constraints, rendering in SpanTimeline component, admin visibility, and the step-by-step process for adding new events.
n8n:create-instance-ai-eval
n8n-io
Authors a new Instance AI workflow eval case — written locally as JSON, calibrated against a real build, then pushed to the LangTracer suite CI runs — build cases, behaviour/process cases, credential cases, and seeded (mid-conversation) cases — with intent-driven expectations. Use when adding or changing an Instance AI workflow eval, or debugging why one is flaky.
n8n:reproduce-bug
n8n-io
Reproduce a bug from a Linear ticket with a failing test. Expects the full ticket context (title, description, comments) to be provided as input.
debugging-executions
n8n-io
Debug failed or wrong-output workflow executions using executions tools. Load when the user reports execution failures, unexpected node output, empty parameter values after a successful run, or a node showing a red or failed expression error.
webapp-testing
ComposioHQ
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
validate-ui-scenario
microsoft
Use when reproducing a UI bug or verifying a fix by driving a real VS Code window end to end and capturing evidence. Writes a scenario file, runs it against a dev build or installed Insiders, and produces a captioned video, per-step screenshots, a Playwright trace, and an HTML report to attach to an issue or pull request.
fix-errors
microsoft
Guidelines for fixing unhandled errors from the VS Code error telemetry dashboard. Use when investigating error-telemetry issues with stack traces, error messages, and hit/user counts. Covers tracing data flow through call stacks, identifying producers of invalid data vs. consumers that crash, enriching error messages for telemetry diagnosis, and avoiding common anti-patterns like silently swallowing errors.
1k-debug-logging
OneKeyHQ
Add or clean temporary OneKey debug console logs with consistent prefixes and safe stringify. Excludes analytics and permanent server logs.
1k-code-review-pr
OneKeyHQ
Review OneKey PRs and diffs for security, correctness, concurrency, React/RN pitfalls, and repository-specific regressions. Use for code review or 审查 PR.
aoti-debug
pytorch
Debug AOTInductor (AOTI) errors and crashes. Use when encountering AOTI segfaults, device mismatch errors, constant loading failures, or runtime errors from aot_compile, aot_load, aoti_compile_and_package, or aoti_load_package.
e2e-test
metabase
Run Cypress E2E tests, analyze failures including screenshots, and stress test for flakiness
pt2-bug-basher
pytorch
Debug PyTorch 2 compiler stack failures including Dynamo graph breaks, Inductor codegen errors, AOTAutograd crashes, and accuracy mismatches. Use when encountering torch.compile errors, BackendCompilerFailed exceptions, recompilation issues, Triton kernel failures, FX graph problems, or when the user mentions debugging PT2, Dynamo, Inductor, or compiled model issues.
cuda-index-width
pytorch
Choose 32-bit vs 64-bit index math in PyTorch CUDA kernels. Use when fixing large-tensor indexing overflows, deciding whether to use int64_t, canUse32BitIndexMath, CUDA_KERNEL_LOOP_TYPE, or AT_DISPATCH_INDEX_TYPES, and when considering binary-size or performance impact of index-type templating.
scrub-issue
pytorch
Fetch, analyze, reproduce, and minimize GitHub issue reproductions. Use only when the user asks to check whether a GitHub issue reproduces, minimize a GitHub issue's repro, analyze a GitHub bug report, or scrub/triage a GitHub issue for reproducibility. Do not use for standalone reproduction requests without a GitHub issue.
1k-trade-swap-market
OneKeyHQ
Implement and debug OneKey Swap, Bridge, Limit, and Stock/Market trades, including quotes, review, execution, fees, and history. Excludes Perps and chart communication.
announcing-behavior-changes
PostHog
Decides whether a behavior-changing fix needs an in-app notice, then builds one that reaches only the affected users and can be removed later. Use when a change alters what an existing user sees without them doing anything — a metric moves, a chart shifts, a count drops, a date range resolves differently, a matcher matches differently — and when adding, reviewing, or removing such a notice. Trigger terms: behavior change, breaking change, semantics change, "results may differ", change notice, deprecation banner, migration banner. Carries the gate (narrow to the affected users with a tested predicate, or do not ship a notice at all), the pattern from SqlInsightDateFilterNotice, where to anchor the notice, and the flag-based removal path. Not for new features (use the changelog), not for permanent per-object warnings computed by the backend, and not for the wording itself (see /writing-user-facing-copy).
edit-changelog
ClickHouse
Edit an auto-generated ClickHouse release changelog into the form that gets committed to CHANGELOG.md. Use when the user has the output of utils/changelog/changelog.py and wants it cleaned up and re-categorized for a release.
exploring-llm-traces
PostHog
Debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against events.properties.$ai_input / $ai_output_choices returns empty — message content lives only on the dedicated posthog.ai_events table.
analyzing-expensive-users
PostHog
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.
security-audit
PostHog
Focused security audit of code, calibrated to surface real exploitable bugs and suppress theoretical findings. Use when the user asks to "audit", "security-audit", "find vulnerabilities", "check for IDOR/SSRF/XSS/injection", or wants a security review of a file, directory, branch diff, or PR. Covers access control, injection, auth/secrets, sensitive data, business logic, web boundary, and AI agent/LLM trifecta risks. Produces calibrated findings with data flow, exploit request, fix, and confidence — no theoretical or defense-in-depth nits.
validating-and-publishing-canvases
PostHog
Validate and publish a canvas source project safely: the source-project shape, declared capabilities, reading the current version pointer, iterating on validation diagnostics, guarded publishing with expected_current_version_id, staging a draft build and promoting it, waiting out the queued build, and recovering from a 409 version_conflict or a 429 capacity limit without overwriting concurrent work. Use whenever a canvas edit is ready to save, a draft build is wanted, a canvas publish or build returns diagnostics or a conflict, or a task needs to understand canvas version history.