agent-smith
Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.
What this skill does
# Agent Smith
An intelligent financial management skill for Claude Code that transforms PocketSmith from a passive tracking tool into an active financial intelligence system.
## Core Capabilities
### 1. Hybrid Rule Engine
- **Platform Rules**: Simple keyword patterns synced to PocketSmith API
- **Local Rules**: Advanced regex, multi-condition logic, confidence scoring
- **Intelligence Modes**: Conservative (manual approval), Smart (auto-apply ≥90%), Aggressive (auto-apply ≥80%)
- **Performance Tracking**: Rule accuracy, matches, user overrides
### 2. 3-Tier Tax Intelligence (Australian ATO)
- **Level 1 (Reference)**: ATO category mappings, basic tax reports, GST tracking
- **Level 2 (Smart)**: Deduction detection, CGT tracking, confidence scoring, expense splitting
- **Level 3 (Full)**: BAS preparation, compliance checks, scenario planning, audit-ready documentation
### 3. Transaction Categorization
- **Batch Processing**: Categorize uncategorized transactions with AI assistance
- **Merchant Intelligence**: Automatic payee normalization and variation detection
- **AI Fallback**: LLM-powered categorization when rules don't match
- **Dry-Run Mode**: Preview categorization before applying
### 4. Financial Analysis & Reporting
- **Spending Analysis**: By category, merchant, time period
- **Trend Detection**: Increasing/decreasing categories, anomaly detection
- **Multi-Format Reports**: Markdown, CSV/JSON, HTML dashboards, Excel
- **Comparative Analysis**: YoY, QoQ, MoM comparisons
### 5. Scenario Analysis
- **Historical**: What-if replays ("What if I cut dining by 30% last year?")
- **Projections**: Spending forecasts, affordability analysis, goal modeling
- **Optimization**: Subscription analysis, expense rationalization, tax optimization
- **Tax Planning**: Purchase timing, income deferral, CGT scenarios
### 6. Health Check System
- **6 Health Dimensions**: Data Quality, Category Structure, Rule Engine, Tax Readiness, Automation, Budget Alignment
- **Automated Monitoring**: Weekly checks, EOFY prep, post-operation validation
- **Prioritized Recommendations**: Top 3 highest-impact improvements
### 7. Advanced Features
- **Smart Alerts**: Budget overruns, tax thresholds, pattern detection
- **Document Tracking**: Receipt requirements ($300 ATO threshold)
- **Multi-User**: Shared expense tracking and settlement
- **Audit Trail**: Complete activity log with undo capability
## Quick Start
### First-Time Setup
1. **Prerequisites**:
- PocketSmith account with API access
- Developer API key from PocketSmith (Settings > Security)
- Python 3.9+ with uv package manager
2. **Configuration**:
- Copy `.env.sample` to `.env`
- Add `POCKETSMITH_API_KEY=<your_key>`
- Set `TAX_INTELLIGENCE_LEVEL=smart` (reference|smart|full)
- Set `DEFAULT_INTELLIGENCE_MODE=smart` (conservative|smart|aggressive)
3. **Installation**:
```bash
# From the skill directory
uv sync
```
4. **Guided Onboarding**:
```bash
# Launch integrated onboarding (8 stages)
/agent-smith:install
```
The onboarding process will:
- Discover your PocketSmith account structure
- Recommend and apply a rule template
- Help customize rules for your needs
- Configure intelligence modes
- Incrementally categorize transactions
- Show measurable improvement with health scores
- Provide ongoing usage guidance
- Generate intelligent suggestions
**Time required**: 30-60 minutes for first-time setup
### Daily Usage
**Two Usage Modes:**
1. **Guided Journey Mode** - When you start Claude Code, Agent Smith displays a status dashboard with:
- Your health score
- Uncategorized transaction count
- Conflicts awaiting review
- Suggested next steps with both natural language and command options
2. **Power User Mode** - Use slash commands directly for specific operations.
**Slash Commands** (7 specialized commands):
- `/smith:install` - Installation and onboarding wizard
- `/smith:categorize [--mode] [--period]` - Transaction categorization
- `/smith:review-conflicts` - Review transactions flagged for review
- `/smith:health [--full|--quick]` - Health check and recommendations
- `/smith:insights <spending|trends|scenario|report>` - Financial analysis and reports
- `/smith:tax <deductions|cgt|bas|eofy>` - Tax intelligence (ATO)
- `/smith:schedule [--setup|--status|--remove]` - Automated categorization scheduling
## Python Scripts
All Python scripts are in `scripts/` directory. **Always run with `uv run python -u`** for proper dependency resolution and unbuffered output.
### Core Operations
```bash
# Categorize transactions
uv run python -u scripts/operations/batch_categorize.py --mode=smart --period=2025-11
# Run health check
uv run python -u scripts/health/check.py --full
# Generate spending report
uv run python -u scripts/reporting/generate.py --period=2025 --format=all
# Tax deduction analysis
uv run python -u scripts/tax/deduction_detector.py --period=2024-25
```
### Script Organization
- **`scripts/core/`** - API client, rule engine, unified rules
- **`scripts/operations/`** - Categorization, batch processing, transaction updates
- **`scripts/analysis/`** - Spending analysis, trend detection
- **`scripts/reporting/`** - Multi-format report generation
- **`scripts/tax/`** - ATO mappings, deductions, CGT, BAS preparation
- **`scripts/scenarios/`** - Historical, projections, optimization, tax scenarios
- **`scripts/status/`** - Status dashboard for SessionStart hook
- **`scripts/scheduled/`** - Automated scheduled tasks (cron/launchd)
- **`scripts/health/`** - Health check engine, recommendations, monitoring
- **`scripts/workflows/`** - Interactive categorization workflows
- **`scripts/utils/`** - Backup, validation, logging, merchant normalization
## Unified Rule System
Agent Smith uses a YAML-based unified rule system for transaction categorization and labeling.
### Quick Start with Rules
```bash
# 1. Choose a template for your household type
uv run python scripts/setup/template_selector.py
# 2. Customize rules in data/rules.yaml
# 3. Test with dry run
uv run python scripts/operations/batch_categorize.py --mode=dry_run --period=2025-11
# 4. Apply to transactions
uv run python scripts/operations/batch_categorize.py --mode=apply --period=2025-11
```
### Example Rule
```yaml
rules:
# Category rule - categorize transactions
- type: category
name: WOOLWORTHS → Groceries
patterns: [WOOLWORTHS, COLES, ALDI]
category: Food & Dining > Groceries
confidence: 95
# Label rule - apply labels based on context
- type: label
name: Shared Groceries
when:
categories: [Groceries]
accounts: [Shared Bills]
labels: [Shared Expense, Essential]
```
### Key Features
- **Two-phase execution**: Categories first, then labels
- **Pattern matching**: Regex patterns with exclusions
- **Confidence scoring**: 0-100% for auto-apply logic
- **LLM fallback**: AI categorization when rules don't match
- **Intelligence modes**: Conservative/Smart/Aggressive
- **Template system**: Pre-built rules for common household types
- **Operational modes**: DRY_RUN/VALIDATE/APPLY for safe testing
See [references/unified-rules-guide.md](references/unified-rules-guide.md) for complete documentation.
## Tax Intelligence
### ATO Compliance (Australian Tax Office)
**Three Levels**:
1. **Reference (Level 1)**:
- ATO category mappings
- Basic tax reports
- GST tracking
- Resource links
2. **Smart (Level 2)** - Recommended:
- Deduction detection (14 pattern-based rules)
- CGT tracking with FIFO matching
- Confidence scoring
- Substantiation threshold checking ($300, $75 taxi/Uber)
- Time-based commuting detection
- Instant asset write-off tracking
3. **Full (Level 3)** - Power Users:
- BAS preparation with GST calculations
- Compliance checks
- Scenario planning
- Audit-ready documentation
- Professional advice disclaimers
**Configure Tax Level**:
```bash
# In .env file
TAX_INTELLIGENCE_Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.