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.
npx skills add travisjneuman/.claude --skill fundraising-analyzergit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/skills/travisjneuman/.claude/fundraising-analyzer)<a href="https://agentmods.dev/skills/travisjneuman/.claude/fundraising-analyzer"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/fundraising-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/travisjneuman/.claude/fundraising-analyzer"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/fundraising-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.02719 |
| Opus 5 | $0.00019 | $0.01359 |
| Sonnet 5 | $0.00008 | $0.00544 |
| Haiku 4.5 | $0.00004 | $0.00272 |
Grade A, and why
fundraising-analyzer 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fundraising Analyzer
Frameworks for analyzing nonprofit fundraising performance, segmenting donors, evaluating campaign effectiveness, and building data-driven fundraising strategies.
Donor Analysis
Donor Segmentation Framework
DONOR SEGMENTATION MATRIX:
BY GIVING LEVEL:
Tier | Annual Giving | % of Donors | % of Revenue
------------------|--------------------| ------------|-------------
Major donors | $10,000+ | [%] | [%]
Mid-level donors | $1,000 - $9,999 | [%] | [%]
Grassroots donors | $100 - $999 | [%] | [%]
Small donors | Under $100 | [%] | [%]
BY ENGAGEMENT:
Segment | Definition | Strategy
------------------|-------------------------------|------------------
Champions | Top 10% by giving + volunteer | Steward, recognize
Loyal | 3+ consecutive years giving | Retain, upgrade
Growing | Increased gift this year | Encourage, cultivate
Lapsed risk | Decreased gift or late renewal| Re-engage campaign
Lapsed | No gift in 13+ months | Win-back campaign
New | First gift in last 12 months | Welcome series
BY RECENCY-FREQUENCY-MONETARY (RFM):
Score each 1-5:
Recency: How recently did they give? (5 = this month)
Frequency: How often do they give? (5 = monthly)
Monetary: How much do they give? (5 = top tier)
RFM Score | Segment | Priority
555 | Best | Highest — personal stewardship
5XX | Active | High — upgrade opportunities
X5X | Frequent | Medium — increase gift size
XX5 | High value | High — increase frequency
1XX | At risk | High — re-engagement needed
111 | Lost | Low — win-back or remove
Donor Lifetime Value
DONOR LIFETIME VALUE (LTV) CALCULATOR:
INPUTS:
Average annual gift: $______
Average giving years: ______ years
Donor retention rate: ______%
Discount rate: ______% (typically 5-8%)
SIMPLE LTV:
LTV = Average annual gift × Average giving years
LTV = $______ × ______ = $______
ADJUSTED LTV (with retention):
LTV = Average gift × (Retention rate / (1 - Retention rate))
LTV = $______ × (____% / (1 - ____%)) = $______
COST-ADJUSTED LTV:
Acquisition cost: $______
Annual stewardship cost: $______
Net LTV = Adjusted LTV - Acquisition cost - (Stewardship × Years)
Net LTV = $______
BY SEGMENT:
Segment | Avg Gift | Retention | LTV | Acq Cost | Net LTV
-----------------|----------|-----------|---------|----------|--------
Major donors | $______ | ____% | $______ | $______ | $______
Mid-level | $______ | ____% | $______ | $______ | $______
Grassroots | $______ | ____% | $______ | $______ | $______
Small | $______ | ____% | $______ | $______ | $______
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.
- 9d ago First seen · 284 lines · 38 tokens per session scan A 0c14e0084316
fundraising-analyzer is a skill published in the GitHub repository travisjneuman/.claude (97 stars, last pushed 7d ago), licensed MIT. It adds 38 tokens to every session and 2,719 once invoked, about $0.0002 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 2026-09-03.
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