feat: Redesigned portfolio for Premium SaaS V6. (Secured: Removed cre…

Skills/analyze-project/examples/sample_session_analysis_report.md

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+# Sample Output: session_analysis_report.md
+# Generated by /analyze-project skill on a ~3-week project with ~50 substantive sessions.
+# (Trimmed for demo; real reports include full per-conversation breakdown and more cohorts.)
+
+# 📊 Session Analysis Report — Sample AI Video Studio
+
+**Generated**: 2026-03-13  
+**Conversations Analyzed**: 54 substantive (with artifacts)  
+**Date Range**: Feb 18 – Mar 13, 2026
+
+## Executive Summary
+
+| Metric                  | Value       | Rating |
+|-------------------------|-------------|--------|
+| First-Shot Success Rate | 52%         | 🟡     |
+| Completion Rate         | 70%         | 🟢     |
+| Avg Scope Growth        | +58%        | 🟡     |
+| Replan Rate             | 30%         | 🟢     |
+| Median Duration         | ~35 min     | 🟢     |
+| Avg Revision Intensity  | 4.8 versions| 🟡     |
+| Abandoned Rate          | 22%         | 🟡     |
+
+**Narrative**: High velocity with strong completion on workflow-driven tasks. Main friction is **post-success human scope expansion** — users add "while we're here" features after initial work succeeds, turning narrow tasks into multi-phase epics. Not primarily prompt or agent issues — more workflow discipline.
+
+## Root Cause Breakdown (non-clean sessions only)
+
+| Root Cause                  | %   | Notes                                      |
+|-----------------------------|-----|--------------------------------------------|
+| Human Scope Change          | 37% | New features/epics added mid-session after success |
+| Legitimate Task Complexity  | 26% | Multi-phase builds with expected iteration |
+| Repo Fragility              | 15% | Hidden coupling, pre-existing bugs         |
+| Verification Churn          | 11% | Late test/build failures                   |
+| Spec Ambiguity              | 7%  | Vague initial ask                          |
+| Agent Architectural Error   | 4%  | Rare wrong approach                        |
+
+Confidence: **High** for top two (direct evidence from version diffs).
+
+## Scope Change Analysis Highlights
+
+**Human-Added** (most common): Starts narrow → grows after Phase 1 succeeds (e.g., T2E QA → A/B testing + demos + editor tools).  
+**Necessary Discovered**: Hidden deps, missing packages, env issues (e.g., auth bcrypt blocking E2E).  
+**Agent-Introduced**: Very rare (1 case of over-creating components).
+
+## Rework Shape Summary
+
+- Clean execution:          52%  
+- Progressive expansion:    18% (dominant failure mode)  
+- Early replan → stable:    11%  
+- Late verification churn:  7%  
+- Exploratory/research:     7%  
+- Abandoned mid-flight:     4%
+
+**Pattern**: Progressive expansion often follows successful implementation — user adds adjacent work in same session.
+
+## Friction Hotspots (top areas)
+
+| Subsystem              | Sessions | Avg Revisions | Main Cause          |
+|------------------------|----------|---------------|---------------------|
+| production.py + domain | 8        | 6.2           | Hidden coupling     |
+| fal.py (model adapter) | 7        | 5.0           | Legitimate complexity |
+| billing.py + tests     | 6        | 5.5           | Verification churn  |
+| frontend/ build        | 5        | 7.0           | Missing deps/types  |
+| Auth/bcrypt            | 3        | 4.7           | Blocks E2E testing  |
+
+## Non-Obvious Findings (top 3)
+
+1. **Post-Success Expansion Dominates** — Most scope growth happens *after* initial completion succeeds, not from bad planning. (High confidence)  
+2. **File Targeting > Acceptance Criteria** — Missing specific files correlates more with replanning (44% vs 12%) than missing criteria. Anchors agent research early. (High)  
+3. **Frontend Build is Silent Killer** — Late TypeScript/import failures add 2–4 cycles repeatedly. No pre-flight check exists. (High)
+
+## Recommendations (top 4)
+
+1. **Split Sessions After Phases** — Start new conversation after successful completion to avoid context bloat and scope creep. Expected: +13% first-shot success. (High)  
+2. **Enforce File Targeting** — Add pre-check in prompt optimizer to flag missing file/module refs. Expected: halve replan rate. (High)  
+3. **Add Frontend Preflight** — Run `npm run build` early in frontend-touching sessions. Eliminates common late blockers. (High)  
+4. **Fix Auth Test Fixture** — Seed test users with plain passwords or bypass bcrypt for local E2E. Unblocks browser testing. (High)
+
+This sample shows the forensic style: evidence-backed, confidence-rated, focused on actionable patterns rather than raw counts.

Skills/antigravity-balance/SKILL.md

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+---
+name: antigravity-balance
+description: Check Google Antigravity AI model quota/token balance. Use when a user asks about their Antigravity usage, remaining tokens, model limits, quota status, or rate limits. Works by detecting the local Antigravity language server process and querying its API.
+---
+
+# Antigravity Balance
+
+Check your Antigravity AI model quota and token balance.
+
+## Quick Start
+
+```bash
+# Check quota (auto-detects local Antigravity process)
+node scripts/agquota.js
+
+# JSON output for parsing
+node scripts/agquota.js --json
+
+# Verbose output (debugging)
+node scripts/agquota.js -v
+```
+
+## How It Works
+
+1. **Process Detection**: Finds the running `language_server_macos_arm` (or platform equivalent) process
+2. **Extracts Connection Info**: Parses `--extension_server_port` and `--csrf_token` from process args
+3. **Port Discovery**: Scans nearby ports to find the HTTPS API endpoint (typically extensionPort + 1)
+4. **Queries Local API**: Hits `https://127.0.0.1:{port}/exa.language_server_pb.LanguageServerService/GetUserStatus`
+5. **Displays Quota**: Shows remaining percentage, reset time, and model info
+
+## Output Format
+
+Default output shows:
+- User name, email, and tier
+- Model name and remaining quota percentage
+- Visual progress bar (color-coded: green >50%, yellow >20%, red ≤20%)
+- Reset countdown (e.g., "4h 32m")
+
+JSON output (`--json`) returns structured data:
+```json
+{
+  "user": { "name": "...", "email": "...", "tier": "..." },
+  "models": [
+    { "label": "Claude Sonnet 4.5", "remainingPercent": 80, "resetTime": "..." }
+  ],
+  "timestamp": "2026-01-28T01:00:00.000Z"
+}
+```
+
+## Requirements
+
+- Node.js (uses built-in `https` module)
+- Antigravity (or Windsurf) must be running
+
+## Troubleshooting
+
+If the script fails:
+1. Ensure Antigravity/Windsurf is running
+2. Check if the language server process exists: `ps aux | grep language_server`
+3. The process must have `--app_data_dir antigravity` in its args (distinguishes from other Codeium forks)
+
+## Platform-Specific Process Names
+
+| Platform | Process Name |
+|----------|--------------|
+| macOS (ARM) | `language_server_macos_arm` |
+| macOS (Intel) | `language_server_macos` |
+| Linux | `language_server_linux` |
+| Windows | `language_server_windows_x64.exe` |

Skills/antigravity-balance/_meta.json

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+{
+  "owner": "finderstrategy-cyber",
+  "slug": "antigravity-balance",
+  "displayName": "Antigravity Balance",
+  "latest": {
+    "version": "1.0.0",
+    "publishedAt": 1769563664027,
+    "commit": "https://github.com/clawdbot/skills/commit/bebc719d7d3d2712df5d389c05a891d02676bf6d"
+  },
+  "history": []
+}

Skills/antigravity-design-expert/SKILL.md

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+--- 
+name: antigravity-design-expert
+description: Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.
+risk: safe
+source: community
+date_added: "2026-03-07"
+---
+
+# Antigravity UI & Motion Design Expert
+
+## 🎯 Role Overview
+
+You are a world-class UI/UX Engineer specializing in "Antigravity Design." Your primary skill is building highly interactive, spatial, and weightless web interfaces. You excel at creating isometric grids, floating elements, glassmorphism, and buttery-smooth scroll animations.
+
+## 🛠️ Preferred Tech Stack
+
+When asked to build or generate UI components, default to the following stack unless instructed otherwise:
+
+- **Framework:** React / Next.js
+- **Styling:** Tailwind CSS (for layout and utility) + Custom CSS for complex 3D transforms
+- **Animation:** GSAP (GreenSock) + ScrollTrigger for scroll-linked motion
+- **3D Elements:** React Three Fiber (R3F) or CSS 3D Transforms (`rotateX`, `rotateY`, `perspective`)
+
+## 📐 Design Principles (The "Antigravity" Vibe)
+
+- **Weightlessness:** UI cards and elements should appear to float. Use layered, soft, diffused drop-shadows (e.g., `box-shadow: 0 20px 40px rgba(0,0,0,0.05)`).
+- **Spatial Depth:** Utilize Z-axis layering. Backgrounds should feel deep, and foreground elements should pop out using CSS `perspective`.
+- **Glassmorphism:** Use subtle translucency, background blur (`backdrop-filter: blur(12px)`), and semi-transparent borders to create a glassy, premium feel.
+- **Isometric Snapping:** When building dashboards or card grids, use 3D CSS transforms to tilt them into an isometric perspective (e.g., `transform: rotateX(60deg) rotateZ(-45deg)`).
+
+## 🎬 Motion & Animation Rules
+
+- **Never snap instantly:** All state changes (hover, focus, active) must have smooth transitions (minimum `0.3s ease-out`).
+- **Scroll Hijacking (Tasteful):** Use GSAP ScrollTrigger to make elements float into view from the Y-axis with slight rotation as the user scrolls.
+- **Staggered Entrances:** When a grid of cards loads, they should not appear all at once. Stagger their entrance animations by `0.1s` so they drop in like dominoes.
+- **Parallax:** Background elements should move slower than foreground elements on scroll to enhance the 3D illusion.
+
+## 🚧 Execution Constraints
+
+- Always write modular, reusable components.
+- Ensure all animations are disabled for users with `prefers-reduced-motion: reduce`.
+- Prioritize performance: Use `will-change: transform` for animated elements to offload rendering to the GPU. Do not animate expensive properties like `box-shadow` or `filter` continuously.

Skills/antigravity-skill-orchestrator/README.md

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+# antigravity-skill-orchestrator
+
+A meta-skill package for the Antigravity IDE ecosystem.
+
+## Overview
+
+The `antigravity-skill-orchestrator` is an intelligent meta-skill that enhances an AI agent's ability to handle complex, multi-domain tasks. It provides strict guidelines and workflows enabling the agent to:
+
+1. **Evaluate Task Complexity**: Implementing guardrails to prevent the overuse of specialized skills on simple, straightforward tasks.
+2. **Dynamically Select Skills**: Identifying the best combination of skills for a given complex problem.
+3. **Track Skill Combinations**: Utilizing the `agent-memory-mcp` skill to store, search, and retrieve successful skill combinations for future reference, building institutional knowledge over time.
+
+## Installation
+
+This skill is designed to be used within the Antigravity IDE and integrated alongside the existing suite of AWESOME skills.
+
+Make sure you have the `agent-memory-mcp` skill installed and running to take full advantage of the combination tracking feature.
+
+## Usage
+
+When executing a prompt with an AI assistant via the Antigravity IDE, you can invoke this skill:
+
+```bash
+@antigravity-skill-orchestrator Please build a comprehensive dashboard integrating fetching live data, an interactive UI, and performance optimizations.
+```
+
+The agent will then follow the directives in the `SKILL.md` to break down the task, search memory for similar challenges, assemble the right team of skills (e.g., `@react-patterns` + `@nodejs-backend-patterns`), and execute the task without over-complicating it.
+
+---
+
+**Author:** [Wahid](https://github.com/wahidzzz)  
+**Source:** [antigravity-skill-orchestrator](https://github.com/wahidzzz/antigravity-skill-orchestrator)

Skills/antigravity-skill-orchestrator/SKILL.md

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+---
+name: antigravity-skill-orchestrator
+description: "A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks."
+category: meta
+risk: safe
+source: community
+tags: "[orchestration, meta-skill, agent-memory, task-evaluation]"
+date_added: "2026-03-13"
+---
+
+# antigravity-skill-orchestrator
+
+## Overview
+
+The `skill-orchestrator` is a meta-skill designed to enhance the AI agent's ability to tackle complex problems. It acts as an intelligent coordinator that first evaluates the complexity of a user's request. Based on that evaluation, it determines if specialized skills are needed. If they are, it selects the right combination of skills, explicitly tracks these combinations using `@agent-memory-mcp` for future reference, and guides the agent through the execution process. Crucially, it includes strict guardrails to prevent the unnecessary use of specialized skills for simple tasks that can be solved with baseline capabilities.
+
+## When to Use This Skill
+
+- Use when tackling a complex, multi-step problem that likely requires multiple domains of expertise.
+- Use when you are unsure which specific skills are best suited for a given user request, and need to discover them from the broader ecosystem.
+- Use when the user explicitly asks to "orchestrate", "combine skills", or "use the best tools for the job" on a significant task.
+- Use when you want to look up previously successful combinations of skills for a specific type of problem.
+
+## Core Concepts
+
+### Task Evaluation Guardrails
+Not every task requires a specialized skill. For straightforward issues (e.g., small CSS fixes, simple script writing, renaming a variable), **DO NOT USE** specialized skills. Over-engineering simple tasks wastes tokens and time. 
+
+Additionally, the orchestrator is strictly forbidden from creating new skills. Its sole purpose is to combine and use existing skills provided by the community or present in the current environment.
+
+Before invoking any skills, evaluate the task:
+1. **Is the task simple/contained?** Solve it directly using the agent's ordinary file editing, search, and terminal capabilities available in the current environment.
+2. **Is the task complex/multi-domain?** Only then should you proceed to orchestrate skills.
+
+### Skill Selection & Combinations
+When a task is deemed complex, identify the necessary domains (e.g., frontend, database, deployment). Search available skills in the current environment to find the most relevant ones. If the required skills are not found locally, consult the master skill catalog.
+
+### Master Skill Catalog
+The Antigravity ecosystem maintains a master catalog of highly curated skills at `https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/CATALOG.md`. When local skills are insufficient, fetch this catalog to discover appropriate skills across the 9 primary categories:
+- `architecture`
+- `business`
+- `data-ai`
+- `development`
+- `general`
+- `infrastructure`
+- `security`
+- `testing`
+- `workflow`
+
+### Memory Integration (`@agent-memory-mcp`)
+To build institutional knowledge, the orchestrator relies on the `agent-memory-mcp` skill to record and retrieve successful skill combinations.
+
+## Step-by-Step Guide
+
+### 1. Task Evaluation & Guardrail Check
+[Triggered when facing a new user request that might need skills]
+1. Read the user's request.
+2. Ask yourself: "Can I solve this efficiently with just basic file editing and terminal commands?"
+3. If YES: Proceed without invoking specialized skills. Stop the orchestration here.
+4. If NO: Proceed to step 2.
+
+### 2. Retrieve Past Knowledge
+[Triggered if the task is complex]
+1. Use the `memory_search` tool provided by `agent-memory-mcp` to search for similar past tasks.
+   - Example query: `memory_search({ query: "skill combination for react native and firebase", type: "skill_combination" })`
+2. If a working combination exists, read the details using `memory_read`.
+3. If no relevant memory exists, proceed to Step 3.
+
+### 3. Discover and Select Skills
+[Triggered if no past knowledge covers this task]
+1. Analyze the core requirements (e.g., "needs a React UI, a Node.js backend, and a PostgreSQL database").
+2. Query the locally available skills using the current environment's skill list or equivalent discovery mechanism to find the best match for each requirement.
+3. **If local skills are insufficient**, fetch the master catalog with the web or command-line retrieval tools available in the current environment: `https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/CATALOG.md`.
+4. Scan the catalog's 9 main categories to identify the appropriate skills to bring into the current context.
+5. Select the minimal set of skills needed. **Do not over-select.**
+
+### 4. Apply Skills and Track the Combination
+[Triggered after executing the task using the selected skills]
+1. Assume the task was completed successfully using a new combination of skills (e.g., `@react-patterns` + `@nodejs-backend-patterns` + `@postgresql`).
+2. Record this combination for future use using `memory_write` from `agent-memory-mcp`.
+   - Ensure the type is `skill_combination`.
+   - Provide a descriptive key and content detailing why these skills worked well together.
+
+## Examples
+
+### Example 1: Handling a Simple Task (The Guardrail in Action)
+**User Request:** "Change the color of the submit button in `index.css` to blue."
+**Action:** The skill orchestrator evaluates the task. It determines this is a "simple/contained" task. It **does not** invoke specialized skills. It directly edits `index.css`.
+
+### Example 2: Recording a New Skill Combination
+```javascript
+// Using the agent-memory-mcp tool after successfully building a complex feature
+memory_write({ 
+  key: "combination-ecommerce-checkout", 
+  type: "skill_combination", 
+  content: "For e-commerce checkouts, using @stripe-integration combined with @react-state-management and @postgresql effectively handles the full flow from UI state to payment processing to order recording.",
+  tags: ["ecommerce", "checkout", "stripe", "react"]
+})
+```
+
+### Example 3: Retrieving a Combination
+```javascript
+// At the start of a new e-commerce task
+memory_search({ 
+  query: "ecommerce checkout", 
+  type: "skill_combination" 
+})
+// Returns the key "combination-ecommerce-checkout", which you then read:
+memory_read({ key: "combination-ecommerce-checkout" })
+```
+
+## Best Practices
+
+- ✅ **Do:** Always evaluate task complexity *before* looking for skills.
+- ✅ **Do:** Keep the number of orchestrated skills as small as possible.
+- ✅ **Do:** Use highly descriptive keys when running `memory_write` so they are easy to search later.
+- ❌ **Don't:** Use this skill for simple bug fixes or UI tweaks.
+- ❌ **Don't:** Combine skills that have overlapping and conflicting instructions without a clear plan to resolve the conflict.
+- ❌ **Don't:** Attempt to construct, generate, or create new skills. Only combine what is available.
+
+## Related Skills
+
+- `@agent-memory-mcp` - Essential for this skill to function. Provides the persistent storage for skill combinations.