tracking-plan-audit

Given event_candidates YAML (output from discover-event-surfaces), generates a concrete instrumentation plan for priority-3 (critical) events. Acts as a Software Architect: discovers existing analytics patterns in the codebase, reads the hinted files to determine what variables are in scope, designs minimal chart-useful properties, and identifies the exact insertion point for each tracking call. Outputs a structured JSON trackingPlan. Use this as step 3 of the analytics instrumentation workflow, after discover-event-surfaces. Trigger whenever a user has event_candidates and wants to generate tracking code, asks "instrument these events", "generate tracking plan", "add analytics for these events", "where should I put the tracking calls", or any request to turn event candidates into concrete implementation guidance.

Monitors AI agent health across quality, cost, performance, and errors. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when the user asks "how are our AI agents doing", "AI quality check", "agent health", "AI errors", "agent performance", "LLM cost", or wants a proactive health report on their AI/LLM features.

Lists projects for an employee-accessible organization or writes organization/project AI context with `manage_amp_context`; use `get_amplitude_context` for reads.

Investigates live product issues against Amplitude data — "is X firing today", "did the release break Y", "which users are affected by Z". Covers the verify-then-query loop, query_amplitude_data parameterization, and its common failure modes. Use for incident analysis, instrumentation checks, and affected-user discovery.

Deep-dives into specific AI agent sessions or failure patterns to explain why something went wrong. Only use when the user has Amplitude Agent Analytics instrumented in their project. Use when investigating a specific session ID, debugging agent failures, understanding why quality is low, tracing tool errors, or when monitor-ai-quality surfaces an issue that needs root cause analysis.

Discovers product opportunities by analyzing Amplitude analytics, experiments, session replays, and customer feedback. Synthesizes evidence into prioritized, actionable opportunities with RICE scoring. Use when the user asks to "find opportunities", "what should we build", "where are we losing users", "product gaps", or wants a data-driven backlog of improvements.

Discovers how analytics tracking calls are actually written in this codebase — the concrete SDK calls, function signatures, and import patterns used to send events. Use this skill whenever you need to understand the existing analytics instrumentation patterns before adding new tracking, when someone asks "how do we track events here?", "show me the analytics setup", "what's the analytics pattern in this codebase?", or any time the instrument-events or discover-event-surfaces skills are about to run and you need to know the correct coding style to follow. Outputs a deduplicated list of patterns with generalized examples and the file paths where each pattern appears, plus the dominant event and property naming conventions inferred from those call sites. Always use this skill before writing any analytics instrumentation code.

Given a change_brief YAML (output from diff-intake), generates an exhaustive list of candidate analytics events to instrument. Takes the perspective of an engineer with a PM mindset — surfaces everything worth considering so a PM can decide what actually matters. Use this as step 2 of the analytics instrumentation workflow, immediately after diff-intake produces a change_brief. Trigger whenever a user has a change_brief YAML and wants to know what analytics events to add, or asks "what should I track for this PR", "what events should I instrument", "generate event candidates", or any request to surface analytics coverage gaps for a code change.

Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," "indexing issues," "stuck on page 2," "striking distance keywords," "title tag rewrite," "low CTR," "local SEO," "Google Business Profile," or "map pack." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.

Security guidance and vulnerability review for codebases, APIs, services, CLI tools, libraries, and daemons. Use for security questions, focused reviews, vulnerability research, security audits, or pen tests. Run the complete workflow only for explicit codebase audit or pen-test requests, full/comprehensive/end-to-end reviews, or requested report artifacts.

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' 'should I offer a free plan,' 'pricing page teardown,' 'pricing page audit,' 'is my pricing page AI-readable,' or 'can AI read my pricing.' Use this whenever someone is figuring out what to charge, how to structure their plans, or wants to audit a pricing page (for humans and for the AI agents that shortlist tools). For in-app upgrade screens, see paywalls. For offer construction (bonuses, guarantees, value framing, naming) on services/courses/coaching/high-ticket B2B, see offers.

Interpreting Honeycomb SLO compliance, burn rates and trigger status, and what to do about them: detecting misconfigured SLIs, freezing deploys vs paging on-call, designing burn alert thresholds. Load before calling get_slos or get_triggers. Trigger phrases: "check our SLOs", "are we meeting our SLOs", "which SLOs are healthy", "is the error budget OK", "are any alerts firing", "what's the burn rate", "set up an SLO", "create a trigger", "configure alerts", "set up burn alerts", "check trigger status", "starting on-call", "reliability picture", "should we freeze deploys", "is this SLO misconfigured", "are we within budget", "SLO is broken", "budget is negative".

Assesses the security posture of Google Cloud Storage (GCS) buckets and projects. Grounds every finding in gathered telemetry, evaluates buckets against Google security best practices (public access, IAM over-granting, CMEK, VPC Service Controls, audit logging), and correlates signals to flag toxic combinations of individually low-risk settings, with actionable remediation. Use whenever a user asks for a security scan, audit, review, vulnerability check, or compliance assessment (including SAIF) — or simply asks whether their buckets, project, or data are secure, exposed, public, or misconfigured, who can access their data, or wants storage hardened or locked down, e.g. before a launch. Don't use for diagnosing a specific access failure or 403, managing or configuring storage, investigating a live outage, or non-GCS resources (Compute Engine, GKE, etc.).

Creates and manages secrets in AWS Secrets Manager following security best practices. Always use this skill when creating secrets — it sets up dedicated KMS encryption keys, automatic rotation, least-privilege IAM policies, CloudTrail auditing, and lifecycle management that are essential for production-grade secret handling.

Audit an existing eval suite for trustworthiness. Use when inheriting evals, suspecting evals miss real failures, or after significant pipeline changes.