# Skene

> Prove what worked. Every play a GTM team ships is a bet, and almost none get graded, because the number that would grade it sits downstream of product code the team never sees. Skene names the evidence a bet needs, builds what is missing, checks every release against that plan, and computes the verdict from the company's own database.

Product data your GTM team can trust. Skene is product intelligence for GTM teams: a trust layer over the product-data collection that feeds your analytics and BI. It maps what your product collects, checks every release against that plan, and names the event, the file and the line when a change breaks one.

Three jobs. Improve the journey: make a product change measurable before it launches, then check whether the metric moved after, and recommend the next useful signals. Audit the gaps: map what the product collects and flag the broken, incomplete and missing signals. Protect the signal: check every release for drift and name the exact signal, location and failure.

Skene is not a replacement for your analytics or BI tool. It does not store, model or visualise event data; it protects the collection layer that feeds PostHog, Mixpanel, Amplitude and Segment. It is not a coding agent either: it runs alongside Cursor, Claude Code, Codex and Devin as an independent check.

Where it runs: Skene Cloud, with a journey canvas, a table of the signals your product collects, Flows for how users actually move, and the Evaluator for deciding whether a change is measurable before you ship it. A GitHub App reviews every pull request and requests changes when one breaks a signal the plan needs. An MCP server puts the same check inside a coding agent. A cloud API exposes it to any script. Skene OSS is the MIT-licensed command-line tool and terminal UI, which needs no Skene account.

This file is a machine-readable index. For the expanded plain-text corpus, see [/llms-full.txt](/llms-full.txt). For the same pages as markdown, see [/sitemap.md](/sitemap.md); each page also answers `Accept: text/markdown` at its own URL, and offers a `.md` counterpart at `<path>.md`.

## Product

- [GTM Agents for driving growth](https://www.skene.ai/) ([markdown](https://www.skene.ai/index.md)): Skene helps plan, run, and learn from GTM experiments
- [How Skene works](https://www.skene.ai/product/how-it-works) ([markdown](https://www.skene.ai/product/how-it-works.md)): See what your product collects, define the outcome before launch, and protect the evidence through code changes. After launch, check the result against the plan.
- [Make every change measurable](https://www.skene.ai/product/features) ([markdown](https://www.skene.ai/product/features.md)): Define the outcome, verify the signals, and measure what changed. Skene protects the evidence behind each result as your team keeps shipping.
- [Integrations](https://www.skene.ai/product/integrations) ([markdown](https://www.skene.ai/product/integrations.md)): Three connections: GitHub for your code, Supabase for your database, and PostHog or Mixpanel for analytics. None are needed to write your first plan. GitHub is the one you need to implement it.
- [Security: what runs where](https://www.skene.ai/product/security) ([markdown](https://www.skene.ai/product/security.md)): Skene OSS runs on your machine and reads your codebase read-only. Skene Cloud connects through scoped permissions. Review access, retention and third parties.
- [Measure a launch](https://www.skene.ai/product/measurement) ([markdown](https://www.skene.ai/product/measurement.md)): Describe what you are shipping and Skene writes the plan: the outcome, the questions the data has to answer, and the signals that must exist to answer them.
- [Skene for Supabase](https://www.skene.ai/supabase) ([markdown](https://www.skene.ai/supabase.md)): Skene connects to Supabase over OAuth, read-only by default, reads the schema, and grounds the customer journey in the tables you actually have.
- [Pricing](https://www.skene.ai/pricing) ([markdown](https://www.skene.ai/pricing.md)): Run the first analysis free, no card. Pro is $249 a month for continuous monitoring on every pull request. Enterprise adds an expert-curated setup.

## Who it is for

- [Three questions, answered from data you can check](https://www.skene.ai/use-cases) ([markdown](https://www.skene.ai/use-cases.md)): Know whether the change you shipped actually moved the number. Growth, revenue operations, customer success and product marketing leaders each run on a handful of product signals. Skene checks those signals against the code on every pull request.
- [Check the activation number before the growth review](https://www.skene.ai/use-cases/growth) ([markdown](https://www.skene.ai/use-cases/growth.md)): Check the event behind an activation metric, then read the result against its reporting window. Skene keeps source evidence separate from the measured outcome.
- [Catch the signal change before your campaign sends](https://www.skene.ai/use-cases/lifecycle) ([markdown](https://www.skene.ai/use-cases/lifecycle.md)): Every lifecycle campaign assumes a signal means what you think it means. A campaign wired to a broken signal stops sending without erroring.
- [When at-risk accounts disappear from your list](https://www.skene.ai/use-cases/customer-success) ([markdown](https://www.skene.ai/use-cases/customer-success.md)): A health score is only as good as the usage behind it. Two ways the account list lies to you, and how to tell a quiet account from a broken signal.

## For engineers

- [See the tracking a pull request changes](https://www.skene.ai/developers) ([markdown](https://www.skene.ai/developers.md)): On enabled repositories, Skene reviews supported pull request changes when configured GitHub events run. It proposes changes through review rather than pushing to your default branch.
- [Developer reference](https://www.skene.ai/developers/reference) ([markdown](https://www.skene.ai/developers/reference.md)): The Skene developer reference: CLI commands and flags, the two check paths, the GitHub App permissions, and the Supabase connection facts.
- [Catch the tracking call your agent's diff broke](https://www.skene.ai/vs/coding-agents) ([markdown](https://www.skene.ai/vs/coding-agents.md)): A feature can work while its tracking is missing. Skene checks code against the planned baseline over MCP, and separately reviews pull requests through the GitHub App.

## Company & trust

- [About Skene](https://www.skene.ai/about) ([markdown](https://www.skene.ai/about.md)): Skene Technologies, five people, with its home in Helsinki. Teemu, Michele and Teppo have worked together since 2021, tired of not trusting the numbers.
- [Contact Skene](https://www.skene.ai/contact) ([markdown](https://www.skene.ai/contact.md)): Talk to the Skene team about a tracking question, Pro billing or adapting Skene to your systems.
- [Community](https://www.skene.ai/community) ([markdown](https://www.skene.ai/community.md)): A Supabase and Claude community series, run as co-working days, not talk tracks, and Skene OSS, the MIT-licensed command-line tool that needs no Skene account.
- [Skene OSS](https://www.skene.ai/community/open-source) ([markdown](https://www.skene.ai/community/open-source.md)): Skene OSS is an MIT-licensed command-line tool and terminal UI. Run uvx skene analyse-journey . against OpenAI, Gemini, Anthropic, Ollama or LM Studio.
- [Where you can find us](https://www.skene.ai/community/events) ([markdown](https://www.skene.ai/community/events.md)): Co-working days, not talk tracks. A casual venue, desks, and one local anchor partner per city. The Supabase Helsinki kickoff at Bar tÿpo, three Cursor meetups, a hackathon with Femcode, and Dublin.

## Policy

- [Editorial standards](https://www.skene.ai/editorial): Authorship, fact-checking, AI-assistance disclosure, corrections policy.
- [Privacy](https://www.skene.ai/privacy): Data handling policy.
- [Terms](https://www.skene.ai/terms): Service terms.

## Documentation

- [Documentation hub](https://www.skene.ai/resources/docs): Docs for Skene Cloud and the Skene CLI: map your customer journey from your codebase and Supabase schema. Work in the cloud, or run everything locally in your terminal.
- [Skene Cloud](https://www.skene.ai/resources/docs/cloud): Skene Cloud builds a customer journey from your codebase and Supabase schema, shows you what's tracked and what's missing, and keeps tracking honest on every pull request.
- [Skene CLI](https://www.skene.ai/resources/docs/skene): Get from zero to a customer journey map.

## Glossary

- [Glossary index](https://www.skene.ai/resources/glossary): Terms for tracking, drift, and validation.
- [Analytics call](https://www.skene.ai/resources/glossary/analytics-call): A statement in your code that records an event. In a Supabase-first setup, a write into a Postgres table, like `supabase.from('events').insert(...)`.
- [Event name](https://www.skene.ai/resources/glossary/event-name): The string identifier passed to an analytics call, used to group occurrences of the same thing on a dashboard.
- [Payload (event properties)](https://www.skene.ai/resources/glossary/payload): The object you write alongside the event name, carrying contextual properties that land on the event row in your database.
- [Identify call](https://www.skene.ai/resources/glossary/identify-call): The call that ties subsequent events to a known user identity, typically by mapping an anonymous ID to a real user ID.
- [Event taxonomy](https://www.skene.ai/resources/glossary/event-taxonomy): The set of event names and properties a product agrees to emit, and the rules for naming them.
- [Instrumentation surface](https://www.skene.ai/resources/glossary/instrumentation-surface): The total set of analytics calls in a codebase. The thing Skene reads, indexes, and watches for drift.
- [Baseline manifest](https://www.skene.ai/resources/glossary/baseline-manifest): The recorded state of an instrumentation surface at a point in time, used as the comparison point for future PRs.
- [Instrumentation drift](https://www.skene.ai/resources/glossary/instrumentation-drift): Unintended changes to analytics calls between two states of a codebase. Often introduced by refactors or coding agents.
- [Semantic diff](https://www.skene.ai/resources/glossary/semantic-diff): A diff between two states that compares meaning, not text. The kind of diff Skene runs on instrumentation manifests.
- [Removed event](https://www.skene.ai/resources/glossary/removed-event): An analytics call that existed in the previous version of the codebase and no longer exists in the new one.
- [Renamed event](https://www.skene.ai/resources/glossary/renamed-event): An analytics event whose name string changes between two states of the codebase, splitting funnels across two names.
- [Moved event](https://www.skene.ai/resources/glossary/moved-event): An analytics call that still exists in the codebase but now fires from a different control-flow context than before.
- [Altered payload](https://www.skene.ai/resources/glossary/altered-payload): A change to the properties object of an analytics call: renamed key, dropped key, or changed type.
- [Conditional firing change](https://www.skene.ai/resources/glossary/conditional-firing-change): A change to the if-block guarding an event. The event still fires, but for a different set of users or sessions.
- [Customer Data Platform (CDP)](https://www.skene.ai/resources/glossary/cdp): A tool that ingests events from your app and fans them out to multiple downstream destinations. Segment, RudderStack, Hightouch Events.
- [Schema registry](https://www.skene.ai/resources/glossary/schema-registry): A central catalogue of event names and payload shapes that an analytics team agrees to. Sometimes called a tracking plan.
- [MCP (Model Context Protocol)](https://www.skene.ai/resources/glossary/mcp): An open protocol for letting AI agents call out to external tools. Skene exposes its validation engine as an MCP server.
- [Coding agent](https://www.skene.ai/resources/glossary/coding-agent): An AI tool that reads and writes code: Cursor, Claude Code, Codex, Devin, Aider. The category Skene is built to live alongside.
- [Side effect](https://www.skene.ai/resources/glossary/side-effect): Code that affects something outside the function it lives in. Analytics calls are side effects. So are logs, emails, and metric increments.
- [Funnel](https://www.skene.ai/resources/glossary/funnel): A sequence of events users move through. The chart most teams check daily; the chart most affected by instrumentation drift.
- [Conversion event](https://www.skene.ai/resources/glossary/conversion-event): An event that represents a desired outcome, usually tied to revenue. The most expensive thing to lose instrumentation on.
- [Activation event](https://www.skene.ai/resources/glossary/activation-event): The event that marks when a new user reaches first value. Usually one of the most-watched metrics in product orgs.
- [Cohort](https://www.skene.ai/resources/glossary/cohort): A group of users defined by a shared property or behaviour. Most retention analysis runs on cohorts.

## Playbooks

- [All playbooks](https://www.skene.ai/resources/playbooks): Audit, set up, recover, validate.
- [Audit your current event taxonomy](https://www.skene.ai/resources/playbooks/audit-your-event-taxonomy): You can't stop instrumentation from drifting until you know what you have. Audit once. The run writes a manifest that every later decision can sit on top of.
- [Set up Skene for a Supabase codebase](https://www.skene.ai/resources/playbooks/set-up-skene-for-supabase): Step-by-step for repos that write events into Supabase. Covers the MCP install, the GitHub Action, and how to baseline the existing surface.
- [Fix dashboards that have already drifted](https://www.skene.ai/resources/playbooks/fix-dashboards-that-have-drifted): Recovery playbook. When you discover the funnel has been wrong for weeks, here is the order of operations to get back to honest data.
- [Validate analytics in CI as part of code review](https://www.skene.ai/resources/playbooks/validate-analytics-in-ci): Treat instrumentation drift like any other code regression: caught on the PR, fixed before merge, reviewed by humans on the same surface they review everything else.
- [Catch instrumentation breakage from coding agents](https://www.skene.ai/resources/playbooks/catch-instrumentation-breakage-from-coding-agents): When the agent is writing the code, the agent is the first reviewer, and the earlier Skene's signal reaches it, the cheaper the fix.
- [Migrate from one analytics tool to another without losing fidelity](https://www.skene.ai/resources/playbooks/migrate-analytics-tools-without-losing-fidelity): Moving events from one backend to another, including into your own Supabase. The SDK swap is the easy part. The hard part is getting every event across without silently losing or renaming it.

## Blog

- [Blog index](https://www.skene.ai/resources/blog): All Skene posts.
- [What to publish: a Postgres publication is an interface, not a backup](https://www.skene.ai/resources/blog/supabase-pipelines-what-to-publish): Publishing every table is the wrong answer. Primary keys, replica identity, column subsets, row filters, and the tables we leave in Postgres on purpose. Episode 3 of the build series, written up.
- [Four layers, four jobs: the shape of a Supabase to BigQuery stack](https://www.skene.ai/resources/blog/supabase-bigquery-stack-four-layers): Postgres, Supabase Pipelines, BigQuery, and the tools your go-to-market team opens on a Monday. What each layer owns, what it refuses to own, and why a wrong number can stay green at every hop after it enters. Episode 1 of the build series, written up.
- [Our YC application, published before we know the answer](https://www.skene.ai/resources/blog/our-yc-application): The Winter 2027 application we sent, close to verbatim, with the weak answers marked. Written for founders filling in the same form, by someone who has no idea yet whether it worked.
- [Supabase Pipelines to BigQuery: from an empty dataset to a table at Live](https://www.skene.ai/resources/blog/supabase-pipelines-bigquery-setup): The whole path, in order. The publication is SQL you write yourself, the destination takes four fields, and two things nobody mentions until you hit them: who gets the bill, and the region field you set once and can never change. Episode 2 of the build series, written up.
- [Supabase Pipelines is faithful. Your tracking has to be too.](https://www.skene.ai/resources/blog/supabase-pipelines-tracking-integrity): Supabase Pipelines replicates eligible Postgres changes to analytical destinations with at-least-once processing. Four illustrative failure modes show why replication cannot create an event the application never captured, and which checks Skene actually performs.
- [Product analytics that live in your own Supabase](https://www.skene.ai/resources/blog/product-analytics-in-your-own-supabase): How Skene sends skene.track() events to an Analytics Bucket in your Supabase project, maps evidence from code and schema, and reviews tracking changes before merge.
- [Analytics is not retroactive](https://www.skene.ai/resources/blog/analytics-isnt-retroactive): A pull request can remove the event behind a report without breaking the feature. Skene reviews tracking changes before merge, while the missing data can still be prevented.
- [Skene Welcomes Supabase to Helsinki](https://www.skene.ai/resources/blog/skene-welcomes-supabase-to-helsinki-finland-tech-scene): Skene Welcomes Supabase to Helsinki by launching a Supabase community events in Helsinki
- [Why Custom Event Tracking Makes or Breaks Product Growth](https://www.skene.ai/resources/blog/custom-event-tracking-product-growth): Product growth dies when custom events break silently. Here's why tracking user actions matters more than vanity metrics and how to protect your data.
- [How to Speed Up Development (Without Breaking Everything)](https://www.skene.ai/resources/blog/speed-up-development-without-breaking-everything): Practical strategies to accelerate development speed without sacrificing code quality. Real tactics from founders who ship fast and maintain stable products.
- [How Skene Helped CelX Build V1 and Pivot from SaaS to Agents-as-a-Service](https://www.skene.ai/resources/blog/celx-plg-case-study): A three-run case study: CelX used Skene as a continuous build loop to fix onboarding leakage, harden security for agentic execution, and pivot from seat-based SaaS to outcome-based Agents-as-a-Service.
- [Historical Pitchkit account: first-pitch completion rose from 39% to 52%](https://www.skene.ai/resources/blog/pitchkit-plg-case-study): I ran Skene's PLG skills on my pitch deck builder. The analysis found decision paralysis on the creation page. I built a 4-step onboarding survey based on the recommendations. First-pitch completion rose 13 percentage points, about 33.3% relative.
- [How redesigning Pitchkit's creation page cut time to first pitch by 59%](https://www.skene.ai/resources/blog/pitchkit-creation-page-redesign): Pitchkit's fastest creation method (URL generation) was buried as one of four equal links. Only 12% of users tried it. I promoted it to hero, added progress animations, and switched to a card layout. Completion went from 38% to 58%.
- [AI onboarding tools compared: what to verify in 2026](https://www.skene.ai/resources/blog/ai-onboarding-tools-comparison): A needs-based comparison of onboarding tools, documented pricing models, and implementation questions to test before buying.
- [SaaS onboarding checklist template (2026): from signup to activation](https://www.skene.ai/resources/blog/saas-onboarding-checklist-template): A ready-to-use SaaS onboarding checklist template covering every step from signup to activation, with examples and metrics.
- [What is activation rate and how to improve it (with a scoped benchmark)](https://www.skene.ai/resources/blog/activation-rate-benchmarks-improvement): How to define, measure, and improve activation rate, with one dated B2B benchmark and guidance for comparing your own cohorts.
- [Churn prediction for SaaS: how to spot at-risk accounts before they leave](https://www.skene.ai/resources/blog/churn-prediction-product-signals): How to identify at-risk SaaS accounts using product usage signals, support data, and engagement patterns before they churn.
- [Developer onboarding: how to get developers to first API call in under 5 minutes](https://www.skene.ai/resources/blog/developer-onboarding-guide): Patterns from Stripe, Twilio, and Vercel for developer onboarding that gets developers to their first API call in minutes, not hours.
- [Reverse trial strategy: when freemium beats free trial (and vice versa)](https://www.skene.ai/resources/blog/reverse-trial-strategy): A decision framework for choosing between reverse trials, freemium, and free trials in your PLG pricing strategy.
- [How to define product-qualified leads (PQLs) for your SaaS product](https://www.skene.ai/resources/blog/how-to-define-pql-criteria): Step-by-step guide to defining PQL criteria, building a scoring model, and routing product-qualified leads to sales effectively.
- [How to reduce time-to-value in SaaS onboarding (with examples)](https://www.skene.ai/resources/blog/time-to-value-saas-onboarding): A practical framework for measuring and reducing time-to-value in SaaS onboarding, with explicitly illustrative product patterns.
- [Self-serve onboarding without a CS team: the complete playbook](https://www.skene.ai/resources/blog/self-serve-onboarding-without-cs-team): How to build effective self-serve onboarding when you have no customer success team. A playbook for early-stage SaaS with zero CS headcount.
- [Expansion revenue playbook: how to measure and build expansion](https://www.skene.ai/resources/blog/expansion-revenue-playbook): Frameworks for measuring expansion, designing triggers, and testing upsells, cross-sells, and usage-based growth.
- [Product-led sales: how to add sales to your PLG motion without killing self-serve](https://www.skene.ai/resources/blog/product-led-sales-guide): A guide to implementing product-led sales (PLS) alongside your PLG motion. Learn when to layer in sales, how to define PQLs, and avoid killing self-serve.
- [Usage-based pricing for SaaS: how to implement with Chargebee, Stripe, or Paddle](https://www.skene.ai/resources/blog/usage-based-pricing-implementation-guide): A practical guide to implementing usage-based pricing in your SaaS product, comparing Chargebee, Stripe, and Paddle for metering and billing.
- [Retention loops vs growth loops: which to build first](https://www.skene.ai/resources/blog/retention-loops-vs-growth-loops): A decision framework for choosing between retention loops and growth loops in your PLG motion, with design templates and real examples.
- [Stalled accounts: how to re-engage users who stopped onboarding](https://www.skene.ai/resources/blog/stalled-accounts-reengagement-playbook): A practical playbook for identifying and re-engaging stalled SaaS accounts that stopped onboarding midway, with email sequences and product signals.
- [The PLG metrics dashboard: 12 metrics every product-led team should track](https://www.skene.ai/resources/blog/plg-metrics-dashboard): The 12 essential product-led growth metrics you should track, with benchmarks and a framework for building your PLG dashboard.
- [BoxyHQ SaaS Starter Kit Review: PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-boxyhq-saas-starter-kit): Is the BoxyHQ SaaS Starter Kit good for product-led growth? We audited signup, feature gating, analytics, and billing. Score: 4.2/10 for PLG. Strong enterprise features (SAML, SCIM), but no plan-based gating or trials.
- [ixartz SaaS Boilerplate scored 3/10 for PLG. Here is why.](https://www.skene.ai/resources/blog/plg-analysis-ixartz-saas-boilerplate): We audited the popular ixartz/SaaS-Boilerplate against ten product-led growth criteria. Free version lacks checkout, trials, and analytics. Pro pricing locks the parts you need to grow. Full breakdown plus what to use instead.
- [JustShip SvelteKit Boilerplate Review: PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-justship-sveltekit): Is JustShip good for product-led growth? We audited signup, feature gating, analytics, and billing. Score: 2/10 for PLG. Clean auth with PostHog, but Stripe webhooks don't save to database and no feature gating exists.
- [Launch MVP Review: Best Supabase SaaS Starter PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-launch-mvp-supabase): Is Launch MVP the best Supabase starter for SaaS? Highest PLG score (5.9/10) with working trials, onboarding, and PostHog analytics. See the full audit and what needs enabling.
- [Nextbase Review: Next.js 16 + Supabase Starter PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-nextbase-supabase-starter): Is Nextbase good for SaaS? We audited signup flow, auth, and database schema. Best signup (8/10) among Supabase starters but zero monetization. Score: 3.2/10 for PLG. See what's missing.
- [Saasfly Boilerplate Review: PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-saasfly): Is Saasfly good for product-led growth? We audited signup, feature gating, analytics, and billing. Score: 4/10 for PLG. Has PostHog and Stripe, but feature limits aren't enforced: code is commented out.
- [SvelteKit Supabase SaaS Starter Review: PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-sveltekit-supabase-starter): Is the CMSaasStarter template good for product-led growth? We audited signup, feature gating, analytics, and billing. Score: 3.7/10 for PLG. Best Stripe integration of any SvelteKit starter, but no analytics or feature gating.
- [Vercel Next.js Subscription Payments Template Review: PLG Audit 2026](https://www.skene.ai/resources/blog/plg-analysis-nextjs-subscription-payments): Is the Vercel Next.js Subscription Payments template good for SaaS? We audited signup flow, Stripe integration, analytics, and feature gating. Score: 3/10 for PLG. See what's missing and how to fix it.
- [Skene’s January 2026 pre-seed announcement: historical release](https://www.skene.ai/resources/blog/skene-raises-800k-pre-seed): A maintained historical release about Skene’s January 2026 pre-seed announcement. Financial figures and current-product claims are omitted or scoped under current disclosure rules.
- [Best resources for learning product-led growth (2026)](https://www.skene.ai/resources/blog/best-resources-for-learning-product-led-growth): A curated list of the top resources for learning product-led growth in 2026: books, courses, newsletters, communities, and people to follow.
- [How to achieve product-led growth?](https://www.skene.ai/resources/blog/how-to-achieve-product-led-growth): A practical playbook for early-stage teams whose PLG deck looks great but revenue doesn't.
- [How to choose a CDP for product-led growth?](https://www.skene.ai/resources/blog/how-to-choose-a-cdp-for-product-led-growth): How to pick a CDP that actually supports product-led growth instead of becoming an expensive logging system.
- [How to learn product-led growth strategy?](https://www.skene.ai/resources/blog/how-to-learn-product-led-growth-strategy): A practical path to actually learning product-led growth, by running real experiments instead of just reading buzzword decks.

## Open source

- [SkeneTechnologies/skene](https://github.com/SkeneTechnologies/skene): Skene OSS, the MIT-licensed command-line tool and terminal UI. Run `uvx skene analyse-journey .`, or install from PyPI with `pip install skene`. It reads a codebase read-only, writes `skene-context/journey.yaml`, needs no Skene account, and runs against your own model provider.
- [SkeneTechnologies](https://github.com/SkeneTechnologies): The GitHub organisation. Source and issue tracker.

## Machine-readable

- [/llms-full.txt](https://www.skene.ai/llms-full.txt): Reference content concatenated as one plain-text corpus.
- [/sitemap.md](https://www.skene.ai/sitemap.md): Every page grouped by section, each with its markdown counterpart.
- [/sitemap.xml](https://www.skene.ai/sitemap.xml): The crawler sitemap, including blog, glossary, playbooks and tools.
- [/robots.txt](https://www.skene.ai/robots.txt): Crawler policy. Fifteen AI crawlers are allowed by name, with the same private-route guards as the default policy.