mine

name	mine
description	Orchestrates deep exploration of an external technology ecosystem — clones repos, deploys parallel subagents for source analysis, and creates a research document. Use when asked to deeply mine, evaluate an external technology, explore an ecosystem, leave no stone unturned, or compare an external approach against Tau.
disable-model-invocation	true

Technology Mining

Deep-dive into an external technology ecosystem to extract architectural patterns, browser standards, performance techniques, and integration opportunities for Tau.

Workflow

Step 1: Identify Targets

Gather from the user message:

• Repos to clone: GitHub URLs, owner/repo slugs, or blog post references
• Group tag: Which repos.yaml group these belong to (or create a new group)
• Mining scope: What to extract — architecture, browser APIs, performance patterns, build systems, API design, POSIX compliance, etc.
• Integration target: Which Tau system the findings relate to (filesystem, runtime, AI, rendering, etc.)

If the user provides blog posts or documentation URLs, fetch them with WebFetch before cloning repos.

Step 2: Clone Repos

Use the repos skill to add and clone all identified repos:

pnpm repos add <owner/repo> -g <group> --clone

If repos are already tracked in repos.yaml, just clone:

pnpm repos clone <name>

Step 3: Deploy Parallel Subagents

Launch one subagent per repo or major aspect using the Task tool with subagent_type="generalPurpose". Each subagent receives a focused mining brief:

Prompt template per subagent:
  Deeply explore <repos/name> to extract:
  1. Architecture: module structure, dependency graph, key abstractions
  2. Browser APIs: which Web APIs are used and how (OPFS, SharedArrayBuffer, WebAssembly, IndexedDB, etc.)
  3. Performance patterns: caching, lazy loading, zero-copy, batching, pooling
  4. Build system: toolchain, WASM compilation flags, bundle strategy
  5. API design: public surface, DX patterns, error handling
  6. Testing: test infrastructure, coverage approach

  Key files to start with: README, package.json, src/index.*, core/*, lib/*

  Return a structured report with numbered findings (F1, F2, ...) and
  specific file:line references for each claim.

Adjust the mining dimensions based on the user's scope. Run subagents in parallel (multiple Task calls in one message).

Step 4: Fetch External Context

In parallel with subagent exploration, fetch any blog posts, documentation pages, or GitHub issues the user referenced:

Use WebFetch for each URL, extract:
- Architecture diagrams or descriptions
- Performance claims with numbers
- API examples
- Browser compatibility notes

Step 5: Synthesize Findings

After all subagents return:

1. Merge findings across repos into a unified analysis, grouping by theme (architecture, performance, browser APIs, etc.)
2. Cross-reference against Tau: For each finding, assess relevance to Tau's architecture — read the relevant Tau source files to compare approaches
3. Classify each finding:

Classification	Meaning
Adopt	Integrate the technology directly into Tau
Adapt	Apply the pattern/principle within Tau's existing architecture
Reference	Useful knowledge but no immediate action needed
Skip	Not applicable to Tau's constraints

4. Fit assessment: Explicitly evaluate whether direct integration is viable. Consider: bundle size impact, architectural alignment, multi-tab/worker compatibility, maintenance burden, and licensing.

Step 6: Create Research Document

Use the create-research skill conventions to produce docs/research/<topic>.md:

• Category: reference (for ecosystem documentation) or comparison (for fit assessments)
• Include numbered findings (F1-FN) with file:line evidence from the mined repos
• Include a Tau Alignment Analysis section comparing each finding against Tau's current approach
• Include a Recommendations table (R1-RN) with Priority/Effort/Impact columns
• Run pnpm docs:validate to verify frontmatter

Composing Skills

Skill	Role in Mining
repos	Clone and manage external repos
create-research	Document findings in standard format

Anti-Patterns

• Shallow mining: Reading only README files. Subagents must explore source code, not just documentation.
• Ungrounded claims: Every finding must cite a specific file and line from the mined repo.
• Missing fit assessment: Always evaluate adopt vs. adapt — never assume integration is the right answer.
• Skipping Tau comparison: Findings without cross-reference to Tau's current approach are not actionable.

Example Invocation

User: "deeply mine the entire Turso database ecosystem for best practices in databases and filesystems"

Agent actions:

1. Fetch blog posts via WebFetch
2. pnpm repos add tursodatabase/turso -g dev-tools --clone
3. pnpm repos add tursodatabase/agentfs -g dev-tools --clone
4. Deploy 3 subagents: Turso core (WASM, VFS, page cache), AgentFS (POSIX, SQLite schema, CoW overlay), database-wasm (browser runtime, SharedArrayBuffer)
5. Synthesize into docs/research/turso-fs.md