multi-tool-architecture-assessment
Systematic comparison of competing tools/approaches before committing to a multi-account, multi-tool architecture. Uses parallel subagents for research, system-state audit, and data quality analysis. Produces a decision matrix with explicit trade-offs.
Best use case
multi-tool-architecture-assessment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Systematic comparison of competing tools/approaches before committing to a multi-account, multi-tool architecture. Uses parallel subagents for research, system-state audit, and data quality analysis. Produces a decision matrix with explicit trade-offs.
Teams using multi-tool-architecture-assessment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/multi-tool-architecture-assessment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How multi-tool-architecture-assessment Compares
| Feature / Agent | multi-tool-architecture-assessment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Systematic comparison of competing tools/approaches before committing to a multi-account, multi-tool architecture. Uses parallel subagents for research, system-state audit, and data quality analysis. Produces a decision matrix with explicit trade-offs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Multi-Tool Architecture Assessment Pattern: When choosing between 3+ competing approaches for a feature area, DO NOT guess. Systematically assess all options with parallel research before committing. ## When to Use - Choosing between multiple tools, libraries, or approaches - Need to support multiple accounts, tenants, or personas differently - Existing tools have partial coverage but no single winner - Decision will block dependent work for weeks ## Step Pattern (Parallel 3-Agent) ### Agent 1: External Research Research all available tools/approaches on the open market. ``` goal: "Research [tool category] options. Check: 1) GitHub repos, 2) npm/PyPI packages, 3) Star counts and download stats. Compare: setup complexity, multi-account support, feature completeness, auth methods. Provide a comparison table with recommendation." toolsets: ["web", "terminal"] ``` ### Agent 2: System State Audit Check what's installed/missing locally. ``` goal: "Check current state of [tool candidates] on this machine. Run: which commands, check config dirs, check for tokens/credentials, check installed packages, verify skill scripts exist and their dependencies. Report exact state." toolsets: ["terminal", "file"] ``` ### Agent 3: Data Quality Analysis Analyze the actual data the tools will operate on. ``` goal: "Analyze the input data files for [feature]. Check: row counts, data quality issues, encoding, duplicates, missing fields, domain distribution. Recommend normalization steps." toolsets: ["terminal", "file"] ``` ### Synthesis Combine results into: 1. Decision matrix table (approach × criteria) 2. Primary + fallback selection with rationale 3. Execution plan with phases and dependencies 4. Concrete GitHub issues (one per phase/component) ## Output Template ``` TOOL ASSESSMENT — WINNER: [primary] + [fallback] ================================================================ Evaluated N approaches: | Approach | Multi | Setup | Auth | Feature X | Verdict | | ... | ... | ... | ... | ... | ... | CHOSEN (primary): [name] - Reason 1 - Reason 2 CHOSEN (fallback): [name] - For [specific use case] REJECTED: - [name]: reason - [name]: reason KEY GAPS: [what no tool does natively] EXECUTION PLAN — 3 PHASES ================================================================ PHASE 1: Infrastructure (BLOCKING) PHASE 2: Data/Migration (PARALLEL) PHASE 3: Workflow/Feature (after 1+2) AGENT ASSIGNMENT ================================================================ ``` ## Pitfalls 1. Don't pick the most popular tool — pick the one matching constraints (multi-account, CLI-only, etc.) 2. Always pick a fallback (not everything needs to go through the "primary" tool) 3. Identify what NO tool does — those become custom scripts/parsers you need to write 4. If data is messy (80%+ uncategorized), plan normalization BEFORE workflows 5. External repos may be in separate git clones or submodules — commit changes from within each repo 6. Don't block all work on the hardest-to-set-up component — run parallel work on data/prep 7. For skills: write-back to both ~/.hermes/skills/ AND .Codex/skills/ in repo (per WRITE-BACK RULE)
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