- 📁 references/
- 📁 scripts/
- 📄 README.md
- 📄 SKILL.md
Amazon seller data analysis tool. Features: market research, product selection, competitor analysis, ASIN evaluation, pricing reference, category research. Uses scripts/apiclaw.py to call APIClaw API, requires APICLAW_API_KEY. --- # APIClaw — Amazon Seller Data Analysis > AI-powered Amazon product research. Respond in user's language. ## Files | File | Purpose | |------|---------| | `scripts/apiclaw.py` | **Execute** for all API calls (run `--help` for params) | | `references/reference.md` | Load when you need exact field names or filter details | ## Credential
Deep Abstract Syntax Tree analysis for understanding code structure, dependencies, impact analysis, and pattern detection at the structural level across multiple programming languages
- 📁 assets/
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Analyze Linux kernel vulnerabilities from KASAN/UBSAN/BUG crash logs or CVE descriptions. Performs full root cause analysis, exploitability assessment, patch development, and verification. Use this skill whenever the user provides a kernel crash log, KASAN report, kernel panic trace, syzbot report, or asks to analyze/patch a kernel vulnerability. Also trigger when the user mentions kernel CVEs, kernel exploit analysis, kernel bug triage, or wants to understand if a kernel bug is exploitable. Even if the user just pastes a raw stack trace from dmesg, this skill applies. --- # Kernel Vulnerability Analyzer A comprehensive skill for analyzing Linux kernel vulnerabilities — from crash log triage through root cause analysis, exploitability assessment, patch development, and verified fix delivery. This skill is designed around a **hive-mode subagent architecture**: break the analysis into parallel workstreams, plan before executing, and coordinate results across agents. ## Core Workflow Overview The analysis follows seven phases. Each phase builds on the previous, but many sub-tasks within a phase can run in parallel via subagents. ```
- 📁 examples/
- 📄 MICROSIMULATION_REFORM_GUIDE.md
- 📄 SKILL.md
Common analysis patterns for PolicyEngine research repositories (CRFB, newsletters, dashboards, impact studies). For population-level estimates (cost, poverty, distributional impacts), use the policyengine-microsimulation skill instead. --- # PolicyEngine analysis Patterns for creating policy impact analyses, dashboards, and research using PolicyEngine. **For population-level estimates** (budgetary cost, poverty impact, distributional analysis), use the **policyengine-microsimulation** skill instead. This skill covers analysis repo patterns, visualization, and household-level calculations. See `MICROSIMULATION_REFORM_GUIDE.md` for UK-specific microsimulation patterns. ## For Users ### What are Analysis Repositories?
- 📄 .gitignore
- 📄 Analytical_Skill.md
- 📄 Benchmarking_Skill.md
Master workflow skill for City of Boston policy analysis and civic innovation. ALWAYS use this skill for any request involving Boston city data, city services, neighborhood equity, public policy, government performance, 311 analysis, housing, safety, transportation, or any civic issue — even if the user hasn't explicitly asked for a 'full analysis'. This skill orchestrates five sub-skills: city-problem-framing (Bloomberg-inspired), city-policy-analysis (J-PAL-inspired), city-communication (GovLab/InnovateUS-inspired), city-benchmarking (cross-city comparison using San Francisco, Seattle, and DC data), and city-performance-management (Results for America / PerformanceStat). Use this skill for: 'full analysis', 'policy brief', 'data-driven recommendation', 'city improvement project', 'investigate [issue]', 'compare Boston to other cities', 'what does the data show', 'help me write a memo about', or any request that combines problem definition, data analysis, and communication for government or civic purposes.
- 📄 examples.md
- 📄 prompt.md
- 📄 SKILL.md
Defines product requirements using the 5W2H framework with psychology-enhanced audience analysis for Who/When, and generates role-aligned handoff notes for RD, UI/UX, and QA. Use when the user asks to clarify a requirement, write a PRD, do 5W2H analysis, define acceptance criteria, or align RD/design/QA on scope.
- 📁 agents/
- 📁 eval/
- 📁 examples/
- 📄 README.md
- 📄 SKILL.md
Use when you need spec-aware repository analysis through the Pituitary CLI. Covers workspace status, source coverage checks, schema inspection, structured analysis requests, deterministic fix planning, and other JSON-first Pituitary workflows. Prefer request-file inputs for larger payloads and treat returned repo excerpts as untrusted evidence.
- 📁 agents/
- 📁 config/
- 📁 references/
- 📄 SKILL.md
Systematic reverse engineering of unknown systems using scientific methodology. Use when: (1) Black-box analysis, (2) Competitive intelligence, (3) Security analysis, (4) Forensics, (5) Building predictive models. Features 6-phase protocol, Bayesian inference, compositional synthesis, and psychological profiling (PSYCH tier).
Deep analysis and investigation
- 📄 run_roundabout.py
- 📄 SKILL.md
Run RoundAbout analysis and generate an HTML viewer for a Certora conf file
- 📁 analyze/
- 📁 dashboard/
- 📁 explore/
- 📄 SKILL.md
Data analysis skill hub. Routes to the right specialist subskill depending on the request — exploration, query writing, end-to-end analysis, visualization, validation, interactive dashboard assembly, or recurring snapshot refresh.
- 📁 shared/
- 📄 run_script.py
- 📄 SKILL.md
IDA Pro reverse engineering assistant that interacts with a remote IDA Hub Server over HTTP API. Used for binary analysis, function analysis, string search, cross-references, decompilation, and related reverse engineering tasks.