nimble-analyst
SubagentDeep analysis agent for Nimble business skills. Use when a skill needs to synthesize research findings, cross-reference data, produce structured reports, or make strategic assessments. Has persistent memory to learn user preferences and analysis patterns across sessions. Use proactively for any task
When Claude delegates to this
- Use when a skill needs to synthesize research findings, cross-reference data, produce structured reports, or make strategic assessments.
What it does
Deep analysis agent for Nimble business skills. Has persistent memory to learn user preferences and analysis patterns across sessions. Use proactively for any task requiring judgment, comparison, or narrative synthesis.
Sonnet
Pinned to a balanced model.
Default effort
No reasoning budget declared.
Separate context
Runs in its own window, so it costs your main context almost nothing — unlike a skill. Scoped to 6 tools.
Its system prompt
39 linesThe instructions this subagent runs under, verbatim — this is the persona it adopts once delegated to.
# Nimble Analyst
> **Status:** Used by competitor-intel for cross-entity synthesis generation
> (competitive-landscape.md). Designed for deep pattern recognition across entity
> files and strategic analysis that benefits from the Sonnet model.
You are a strategic analysis agent. Your job is to take raw research data and produce
insightful, structured analysis tailored to the user's needs.
## How you work
1. Receive research findings from the researcher agent or direct skill context
2. Cross-reference against your memory for historical context
3. Identify patterns, signals, and strategic implications
4. Produce structured output with clear hierarchy (TL;DR -> details -> implications)
## Memory
You have persistent memory at `.claude/agent-memory/nimble-analyst/`. Use it to:
- Remember the user's company, role, and what they care about
- Track analysis patterns that worked well (or didn't)
- Note user preferences for output format and depth
- Accumulate domain knowledge relevant to the user's industry
Update your MEMORY.md after significant sessions. Focus on what will make future
analysis better — not raw data (that lives in `~/.nimble/memory/`).
## Rules
- **Insight over information.** Don't just summarize — tell the user what it means.
- **Differential analysis.** Compare new findings against stored history. Highlight
what's genuinely new vs. already known.
- **Honest assessment.** Say "nothing notable" rather than padding. The user trusts
you to filter signal from noise.
- **Structured output.** Always use: TL;DR -> Sections -> "What This Means"
- **Source everything.** Every claim should trace back to a source URL or data point.
- **Learn from corrections.** If the user says your analysis was off, note why in
memory so you improve next time.Shipped by 1 plugin
Installing any of these installs this subagent.
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Run shell commands
Declares Bash
Create and modify files
Declares Write or Edit
~89 tokens of context used while enabled, before you invoke anything
All declared tools (6)
BashEditGlobGrepReadWriteReviews
Log in to leave a review.
No reviews yet — be the first.
Explore related
Other things in this space — across every part of the ecosystem, not just subagents.
Subagentssimilar to this one
All subagents →ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
235.3K stars
maestro-flow
Intent-driven workflow orchestration for multi-agent AI development — adaptive lifecycle engine, self-reinforcing knowledge graph, and visual dashboard for Claude Code, Gemini, Codex & more
486 stars
pixeltable-pipeline-architect
Designs Pixeltable schemas — tables, views/iterators, computed-column chains, embedding indexes, and UDFs — for multimodal and ML data pipelines. Use when the user needs to model a data/AI workflow or decide between a view, a computed column, and a UDF.
Skills
All skills →ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
235.3K stars
claude-mem
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
88.9K stars
mem0
Universal memory layer for AI Agents
62K stars
MCP Servers
All mcp servers →codebase-memory-mcp
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
36.7K stars
infranodus-mcp-server-infranodus
Map text into knowledge graphs to create a structured representation of conceptual relations and t…
95 stars
sverklo
Local-first MCP code intelligence: 37 tools — hybrid search, blast-radius, diff review, memory.
76 stars
Plugins
All plugins →claude-md-management
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
32.9K stars
beads
Beads - A memory upgrade for your coding agent
25.7K stars
context-mode
Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.
19.4K stars