Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Nimbleway/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/nimbleway/agent-skills/nimble-analyst)<a href="https://agentmods.dev/agents/nimbleway/agent-skills/nimble-analyst"><img src="https://agentmods.dev/badge/agents/nimbleway/agent-skills/nimble-analyst.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.00524 |
| Opus 5 | $0.00035 | $0.00262 |
| Sonnet 5 | $0.00014 | $0.00105 |
| Haiku 4.5 | $0.00007 | $0.00052 |
Grade A, and why
nimble-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
- Receive research findings from the researcher agent or direct skill context
- Cross-reference against your memory for historical context
- Identify patterns, signals, and strategic implications
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 63 lines · 70 tokens per session scan A 6d39a4558e03
nimble-analyst is an agent published in the GitHub repository Nimbleway/agent-skills (53 stars, last pushed 12d ago), licensed MIT. It adds 70 tokens to every session and 524 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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timps_log_interpreter
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