591 skills · sorted by relevance
Repair an entire broken feature by mapping its files and dependencies, checking configuration and logs, and confirming root causes. Apply fixes in sequence, then test the feature and the code that uses it.
Assess Web3 vulnerability findings before submission by checking scope, reproducibility, prerequisites, and attack costs. Organize the root cause, measurable impact, working proof of concept, and proposed fix into an Immunefi report, with examples for comparison.
Investigate a bug's root cause, implement a focused repair, and verify the same behavior before and after the change. Select lasting regression tests, preserve reproduction evidence, and record the corrected user journey when possible.
Investigate a reported bug before choosing a fix. Trace the actual failure, test competing explanations, and examine design history so the proposed change preserves intended behavior and clearly separates verified findings from unresolved assumptions.
Review an existing bug-fix pull request using validated reproduction tests and the complete code diff. Assess root-cause coverage, scope, and regressions, then document behavioral evidence and follow the workflow's branch and approval rules.
Investigate bugs through symptom gathering and hypothesis testing, with saved investigation state across context resets. Resume earlier sessions, check their status, or request a root cause report without applying a fix.
Investigate and fix production errors reported in Sentry through MCP, a connection to external tools. Analyze error details and execution history, check them against source code, implement fixes, and verify them with tests and regression checks.
Investigate errors, crashes, and failures after updates using reproducible steps, logs, and runtime checks. Identify the root cause before making authorized fixes, then verify the result and check for the same problem elsewhere.
Investigate bugs by tracing symptoms through code and recent changes, reproducing failures, and testing a specific cause before fixing it. Produce a focused correction, a regression test to catch recurrence, and an evidence-based debugging report.
Understand common web application vulnerabilities through organized explanations of their causes, impacts, and mitigations. Use the reference to review authentication, APIs, access controls, and configuration issues, or build checklists for security assessments.
Review completed work and its process against the original goals and acceptance criteria. Identify specific problems, root causes, and actionable fixes, while checking external criticism against evidence and correcting valid issues.
Consult an archived reference for classifying errors, reading stack traces, investigating root causes, and recording reusable fixes. Retained for migration, it documents a debugging process while directing current bug investigation to systematic-debugging.
Run a deep root-cause investigation on the AWS DevOps Agent. Use when the user describes an incident, alarm, outage, or unexplained behavior — keywords like "5xx", "503", "OOM", "latency spike", "deployment failure", "rollback", "sev1", "investigate", "root cause", "debug", "alarm fired", "service down". Polls and streams progress, then surfaces recommendations.
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Operate a live AWS workload with AWS DevOps Agent — incident investigation, root-cause analysis, and release-readiness review — for startups with no dedicated DevOps or SRE engineer. Use when a live AWS workload is misbehaving (production is down, 5xx errors, latency spike, timeouts, it broke after the last deploy, an alarm fired, a pasted CloudWatch alarm or stack trace, find the root cause, write a postmortem, no one on call), for pre-merge review (is this PR safe to ship, blast radius), or to set up, pause, stop, or check the cost of AWS DevOps Agent. Detects existing setup first and never re-onboards. Asks one explicit Yes/No covering cost (two-month trial, Activate credits) and account access before any setup; code egress stays off unless asked. Do not use for: new architecture (architect-for-startups), scaffolding (start-building-for-startups), migrations (gcp-to-aws, azure-to-aws, heroku-to-aws, llm-to-bedrock), or general Activate and credits questions (knowledge-base-for-startups).
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Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across six capabilities — migration (Solr/ES/self-managed into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock); log-analytics (PPL, OSI, anomaly detection, Dashboards); trace-analytics (OTel spans, service maps, Data Prepper); ai-assistant (natural language data exploration, incident investigation, root cause analysis). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, Solr, vector/k-NN/semantic/hybrid search, RAG, log analytics, PPL, trace analytics, ISM, FAISS, HNSW, Migration Assistant, UltraWarm, OR1, query my data, analyze logs, investigate errors, root cause analysis.
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Builds quality into operations — defining standards, catching defects at the right point, root cause analysis, and continuous improvement. Use this to reduce a defect or error rate, design quality controls into a process, run a root cause analysis, respond to a customer quality complaint, or set up quality metrics that drive behavior.
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Root-cause a failing or wrong empirical result with a disciplined reproduce → minimise → hypothesise → instrument → fix loop, instead of guessing-and-poking. Use when the user says "why is my regression wrong", "this number changed", "my script errors out", "the result won't reproduce", "debug this", "this estimate looks wrong", or "it worked yesterday". Tuned for research code (R/Stata/Python): type coercion, NA/merge blow-ups, factor levels, clustering/SE choices, weighting, collinearity/convergence, seeds, package-version drift. Use `--no-fix` to localize the root cause without editing shared or load-bearing files.
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AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).
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Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.
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Diagnose a bug systematically instead of guessing — reproduce, isolate, form hypotheses, and test them to root cause. Use when debugging, chasing a defect, an intermittent failure, or 'why is this happening?'. Produces a structured diagnosis: a reliable repro, the narrowed-down location, ranked hypotheses with how to test each, and the root cause + fix once found.
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Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo. Use when a test fails intermittently in CI but passes on rerun or locally, when `hogli ci:insights` or the debugging-ci-failures skill classifies a failure as a flaky test, when given a GitHub Actions URL for a flaky job, when asked to check Trunk Flaky Tests for a test, PR, or master, or when asked to deflake, stabilize, or fix a flaky Jest, pytest, or Playwright test. Core discipline: reproduce locally before changing anything, fix the root cause (never mask it with sleeps, retries, or bigger timeouts), and prove the fix with an N-run validation loop sized to the observed failure rate. Stabilizing is not the only valid outcome — the skill also gates whether the test should exist, so deleting a test that catches nothing real, or re-leveling one that flakes because of the level it runs at, are first-class endings.
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Run a structured after-action review (postmortem, retrospective) on a launch, incident, or completed project to capture timeline, root cause analysis, contributing factors, and actionable lessons. Use this skill whenever the user wants to run a postmortem, retrospective, AAR, or after-action review on any past event. Triggers on after-action report, AAR, postmortem, retrospective, retro, post-incident review, what went well what didn't, lessons learned, blameless postmortem, root cause analysis, RCA, five whys. Also triggers when the user has just shipped something or just resolved an incident and wants to capture learnings.
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CAPA system management for medical device QMS, covering root cause analysis, corrective action planning, and effectiveness verification. Use for CAPA investigations, 5-Why and fishbone analysis, and corrective action tracking.
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Performs root-cause analysis of a bug — from a GitHub issue, an issue URL, or a free-text description — before any reproduction or fix is written. TRIGGER when: triaging or diagnosing a bug, investigating why something misbehaves, an issue number/URL handed over for analysis, needing the root cause before touching code. DO NOT TRIGGER when: a failing reproduction test already exists and you are ready to fix → opsmill-dev-fixing-bugs; writing that reproduction test → opsmill-dev-test-driving-bugs; capturing a new bug as a ticket → opsmill-dev-creating-issues.
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