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Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.

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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.

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Exhaustive idea extraction through recursive breadth-before-depth exploration with Deep Think protocol and fractal structure. Use during /ideate to transform raw ideas into comprehensive, structured ideation output. Writes to a fractal folder hierarchy — every node has an index, CX file, and children.

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Execute recursive breadth-before-depth ideation discovery with Deep Think, CX gate enforcement, and feature deepening

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Bu template'in tasarim sistemi referansi — renk (colorScheme + AppThemeColors semantic token'lari), tipografi (textTheme slotlari, Poppins/Inter), spacing/radius/golge/sure token'lari (AppPaddings, AppRadius, AppShadows, AppDurations, AppIconSizes), paylasilan widget katalogu (AppPrimaryButton, dort-durum widget'lari, AppTextField, sheet'ler), 8 varyantli light/dark tema ve easy_localization. Kullanici UI yazarken/duzenlerken "renkleri projeye gore kullan", "bu butonu projeye gore yaz", "hangi spacing/radius", "tasarim sistemine uydur", "style guide", "tema token'lari" dediginde kullan.

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Search the web through Tavily with bounded depth, domains, dates, and result counts. Use when the user needs current information or source discovery and does not already have a specific URL.

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You are an expert software engineer and systems analyst. Use when user asks "how does X work" with expectation of depth, user wants to understand a complex system spanning many files, or user asks for architectural analysis or pattern investigation.

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You are a senior documentation engineer that generates comprehensive technical documentation pages with evidence-based depth.

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Alternative Slowbar coffee intelligence — scientific analysis from seed to cup using First Principle + System Thinking. Covers: varieties, terroir, cultivation, disease diagnosis, processing, roasting physics/chemistry, extraction science, brewing, business strategy, storytelling. ALWAYS trigger for coffee-related questions requiring scientific depth. DO NOT trigger for simple recipes or general food questions. Style: science-driven, farmer-first, quality-obsessed.

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Build a tdmcp TouchDesigner-bridge vertical slice the house way — promote an op from /api/exec to a first-class REST endpoint (e.g. POST /api/connect, GET /api/logs, node_detail flags), add its typed method in src/td-client/touchDesignerClient.ts, its Zod envelope in src/td-client/validators.ts, rewire the tool(s) to prefer the endpoint with an exec fallback, and write py_compile + td/tests unittest + offline msw client tests. Use when adding/changing a bridge REST endpoint, editing td/, extending node_detail/get_bridge_logs, promoting connect/disconnect/param-modes/DAT-text off exec, or doing any exec→REST work — especially the td-depth bridge-robustness backlog. Honors probe-live discipline when TouchDesigner is offline.

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Survey one surface of tdmcp (artist controls, library/packaging, CLI/DX, AI/LLM, or TouchDesigner depth) for candidate NEW features — inventory what exists, cross-check the roadmap, apply the gap-finding lenses, vet each idea, and emit a structured, novelty- and confidence-labelled candidate list to _workspace/discovery/. Use when a td-surveyor agent is scouting a surface during the feature-discovery harness.

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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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Multi-agent SWE team built on the Workshop model. Full-stack vertical workers, meeting room with @mention notification, private pipes, shared task board. Git worktree isolation, Leader-driven coordination. Mixed Codex/Claude Code engine. Use when a task needs engineering depth beyond a single agent. NOT for simple task parallelism (use agent-task-orchestration) or design discussions (use agent-brainstorm).

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When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.

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Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences. Use when: 3D website, three.js, WebGL, react three fiber, 3D experience.

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Performs GraphQL introspection attacks to Şunu çıkar: full API schema including types, queries, mutations, subscriptions, and field definitions from GraphQL endpoints. The tester uses introspection queries to map the attack surface, identifies sensitive fields and mutations, tests for query depth and complexity limits, and exploits GraphQL-specific vulnerabilities including batching attacks, alias-based brute force, and nested query DoS. Activates for requests involving GraphQL security testi...

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Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.

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Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain framework to identify which phases an adversary has completed, where defenses succeeded or failed, and what controls would have interrupted the attack at earlier phases. Use when conducting post-incident analysis, building prevention-focused security controls, or mapping detection gaps to kill chain phases. Activates for requests involving kill chain analysis, intrusion kill chain, attack phase mapping, or Lockheed Martin kill chain framework.

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General Signals scout for PostHog projects. Cross-product explorer that scans a team's project and emits findings into the Signals inbox. Sibling signals-scout-* specialists each watch a single product surface in depth; this scout looks for cross-product correlations and explores the surfaces no specialist covers. Each scout runs on its own schedule (default hourly), so general fires independently of the specialists over time.

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Assesses what a page's heatmap is telling you and recommends concrete changes. Pulls click / rageclick / scroll-depth data for a URL, names the hot elements by cross-referencing autocapture events on the same page, and can create a saved heatmap the user opens in PostHog, then summarizes the behavior and proposes improvements. TRIGGER when: user asks what a heatmap shows, why people aren't clicking something, where users rage-click, how far they scroll, what to change on a page based on heatmap/click data, or to 'analyze/assess/review the heatmap' for a URL. DO NOT TRIGGER when: the user only wants to create a saved heatmap screenshot with no analysis (use heatmaps-saved-create directly), or is asking about session replay in general (use investigating-replay).

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End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.

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Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

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Listens to a CARLA sensor and either saves its data to files, shows it live in a window, prints a one-shot summary, or (ros-info) reports the native ROS 2 topics, QoS and enabled-for-ROS state so you can echo it from ROS instead. Cameras save as PNG (depth/semantic auto-colourised) and display in a pygame window; lidar saves as .ply and shows as a top-down scatter; IMU/GNSS/radar/collision stream to JSONL or the console. Use when the user asks to "show/view the camera", "display the lidar", "save the sensor data / capture a dataset", or "what is this sensor reading". Select the sensor by id, type, or the actor it's attached to.

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Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.

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