123 skills · sorted by relevance
Time-based cohort analysis with retention and behaviour tracking. Activate when you need to measure how groups of users/customers behave over time — retention rates, revenue by cohort, or feature adoption curves.
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Shrink a Robonix change before review without changing behaviour. Use when a feature works and tests pass, before opening or updating a PR, and when a review asks for a smaller change.
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Conducts Privacy Impact Assessment for large-scale systematic monitoring under GDPR Article 35(3)(c). Covers CCTV and video surveillance, employee monitoring, location tracking, internet monitoring, and behavioural analytics. Applies EDPB WP248rev.01 criteria for systematic monitoring of publicly accessible areas. Keywords: DPIA, large-scale monitoring, CCTV, employee monitoring, systematic monitoring, surveillance, location tracking.
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Conducts Data Protection Impact Assessments for biometric identification and authentication systems under GDPR Article 35 and Article 9 special category rules. Covers facial recognition, fingerprint, iris scanning, voice recognition, and behavioural biometrics. Applies EDPB Guidelines 3/2019, CNIL Reglement Type Biometrie, and ISO/IEC 24745 biometric template protection. Keywords: DPIA biometric, facial recognition, fingerprint, Art. 35, Art. 9, biometric template, special category.
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Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
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Think and work like an expert Behavioral Ecologist. Use when a task calls for Behavioral Ecologist judgment. Reasons from versioned ethograms, Altmann focal/scan sampling, and activity budgets; scores with BORIS (Cohen’s κ), analyzes sequences with Markov/HMM tools, runs sham-controlled playbacks under ARRIVE 2.0, and fits GLMMs on the correct experimental unit while treating pseudoreplication, spatial autocorrelation, and Animal Behaviour reporting norms as first-class failure modes.
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Capture durable, project-specific learnings from the work just done into the knowledge/ base — gotchas, implementation quirks, things looked up or web-searched, live-API behaviours, and design decisions (as ADRs). Use before opening a PR (it runs automatically in /deliver), or any time you've learned something worth remembering. Records only non-obvious, reusable facts; skips anything already in the repo, CLAUDE.md, or git history.
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Audits and reorganises what an AI coding agent carries in its context window: instruction files, path-scoped rules, skills, subagents, hooks and the running conversation. Use when someone says "the agent ignores our CLAUDE.md", "set this repo up for Claude Code / Codex / Cursor", "my AGENTS.md is too long", "quality drops in long sessions", "it keeps inventing APIs", "what should go in a skill versus the rules file", or asks how to hand work to a subagent. Grounded in the documented loading behaviour of Claude Code, with the equivalents for Codex, Gemini CLI and Cursor.
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Helper text, placeholders, validation and submit behaviour. Use when designing or reviewing a form, input field or data entry UI.
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Use when a screen design was approved (the design task is in done) - the hand-off document implementers build from - components mapped to the inventory, tokens, every state, responsive behaviour per breakpoint, accessibility notes and exact copy
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Use after the targeted cases pass - choosing the adjacent behaviour this change can break from the PR's files and the component's links, and the per-repository smoke set
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Root-cause debugging discipline. Use when a test, build or pipeline fails, behaviour does not match expectations, a bug task arrives, or a task comes back in need_revision — before proposing any fix.
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Use when you design or change an endpoint's errors, status codes, pagination, idempotency or concurrency behaviour — one error shape, the status-code table, bounded lists, safe retries
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Anti-detection behaviour for Genesis browser automation - what the Camoufox tools already do, what the agent must still do, Cloudflare Turnstile handling, and per-site notes for bot-hostile sites
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Bug-fix routine that fixes the whole class of bug, not just the reported instance. Use for any bug report, failing production behaviour, monitoring alert, audit finding or review finding that does real harm (a smaller review finding goes on the item's list, as ship-check says), and whenever the same kind of bug has been seen before. Reproduces it with a failing test, names the failure class, searches the task's scope for the same pattern (the whole codebase when the user's own prompt asks for the fix, not when it is picked from a task's list), fixes or records every hit, and adds the rule, invariant, helper or check that stops it coming back.
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Run before writing or changing code for any feature, bug fix or refactor that touches data, jobs, money, outside services or user-facing behaviour. Finds where the change will break before it is built, by walking ten named failure classes (twice, halfway, outside call, failure-is-not-empty, neighbors, endings, money, hostile user, words, scale and time) against the code, and writes the answers into the plan. Use when planning a change, when asked to "think it through", "cover the gaps", "make sure it's bug free", or before any edit to payment, publishing, deletion, auth or background-job code.
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Design a restorative practice protocol for addressing harm, rebuilding trust, and restoring relationships. Use when managing conflict, behavioural incidents, or relationship breakdown between students.
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Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.
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Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.
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Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.
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Write and change a product spec. Covers what belongs in the spec rather than in architecture, the status vocabulary, requirement IDs and how to keep them stable, and how to size a requirement so it becomes one story. Use when adding or editing a spec chapter, when a PR changes behaviour and the spec has to move with it, or when asked what the product should do. Triggers - "write the spec", "add a requirement", "spec this out", "update the spec", "what should this do".
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Pin a measurable, outcome-based Definition of Done for a diamond — a behaviour-change that creates value, not a build-list. Problem-first Socratic sequence; writes definition_of_done to the diamond at birth or retrofits it when missing.
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Rules for building offline-first Progressive Web Apps that launch instantly from the home screen. Use when building, reviewing or debugging a PWA, or anything touching a service worker, a web app manifest, precaching, an in-app update prompt, iOS home screen behaviour, or safe-area/notch layout.
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Use before writing PlayCanvas behaviour from scratch, to reuse the scripts and helpers the installed Engine already ships.
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