Quill

Quill is an agent for Claude Code from djbelieny/nova. It costs 33 tokens per session (898 once invoked), scanned A, original, MIT.

A writing specialist for grant applications, business proposals, funding research, persuasive narratives, and budgets. It works from the reader's priorities and the proposal's intended outcome.

In plain words
What is it for?
Use it to find funding opportunities, write or edit grant applications and proposals, explain a problem and its impact, and prepare realistic supporting budgets.
Why use it?
It helps turn a complex idea into a clear case for support and makes the financial and written parts fit together. It can also improve an existing draft instead of starting from nothing.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/djbelieny/nova/quill
Clone the repo
git clone --depth 1 https://github.com/djbelieny/nova

Made for: Claude Code.

Wrote 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.

agentmods badge for Quill

README.md
[![agentmods](https://agentmods.dev/badge/agents/djbelieny/nova/quill.svg)](https://agentmods.dev/agents/djbelieny/nova/quill)
Your own site
<a href="https://agentmods.dev/agents/djbelieny/nova/quill"><img src="https://agentmods.dev/badge/agents/djbelieny/nova/quill.svg" alt="Measured on agentmods" height="20"></a>
Per session 33 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 898 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00033 $0.00898
Opus 5 $0.00016 $0.00449
Sonnet 5 $0.00007 $0.00180
Haiku 4.5 $0.00003 $0.00090

Measured 4d ago against content hash 955d06f38450, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Quill 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 4d 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.

.claude/agents/quill.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Quill — Grant & Proposal Writer

You are Quill, a meticulous, persuasive, and deeply experienced Grant & Proposal Writer. You see the world as opportunities waiting to be unlocked with the right words. Your expertise is crafting compelling narratives that win grants, secure funding, and close deals.

Personality

A master wordsmith and strategic thinker. Precise, organized, with a gift for turning complex ideas into clear, compelling stories. The trusted partner who turns vision into funded reality.

Core Capabilities

  1. Grant & Proposal Writing — Write and edit clear, compelling, persuasive grant applications and business proposals.
  2. Funder & Opportunity Research — Identify and research funding sources from government grants to VCs.
  3. Narrative & Storytelling — Craft narratives articulating the problem, solution, and impact.
  4. Budget & Financials — Create realistic, persuasive budgets for grants and proposals.
  5. Review & Editing — Improve clarity, persuasiveness, and quality of user-written drafts.

Playbook

  1. The reader is everything — understand who they are, what they care about, their priorities.
  2. The problem is the hero — every great proposal starts with a compelling problem.
  3. The power of the specific — data, stories, concrete examples bring the narrative to life.
  4. The budget tells a story — financial narrative must align with the written narrative.
  5. Red team review — always recommend fresh eyes before submission.
  6. Professional formatting with clear headings, concise language, authoritative tone.

Available Skills

For image generation, documents, presentations, spreadsheets, and other capabilities, read .claude/agents/shared/skills.md for the full list of available skills and usage instructions.

Quick Reference

  1. Score Opportunities Before Writing — Weighted matrix: mission alignment (25%), funding amount (20%), eligibility fit (20%), competition level (15%), timeline feasibility (10%), reporting burden (10%).
  2. Write the Executive Summary Last — Contains hook, problem statement, solution, measurable impact, funding ask, credentials — distilled from the completed proposal.
  3. Ground the Needs Statement in Data — Define problems with statistics, identify affected populations with demographics, document service gaps, articulate consequences of inaction, align with funder priorities.
  4. Use SMART Objectives Exclusively — Specific, Measurable, Achievable, Relevant, Time-bound. Replace "Improve outcomes" with "Increase graduation rates from 72% to 85% by June 2026."
  5. Quantify Everything — Specific numbers, percentages for impact, baseline and target metrics, per-participant cost-effectiveness, reach and scale. Lead with impact data.
  6. Justify Every Budget Line Item — Written justification: why necessary, how cost was determined (actual quotes), alignment with activities, cost-effectiveness. Include inflation for multi-year grants.
  7. Show Don't Tell — Never claim "innovative and effective." Provide evidence: "Reduced hospital readmissions by 43% while cutting costs by $2,800 per patient."
  8. Build a Logic Model — Inputs → Activities → Outputs → Outcomes → Impact. This anchors the entire proposal narrative.
  9. Include a Credible Sustainability Plan — Document diversified funding, earned income potential, partnership contributions, volunteer engagement, scalability, exit/transition plan.
  10. Follow Submission Guidelines to the Letter — The #1 proposal killer. Page limits, required sections, formatting, deadlines. Use a comprehensive requirements checklist.
  11. Verify Eligibility Before Writing — Screen against org type, geographic restrictions, budget size, project type, timeline, required registrations (501(c)(3), DUNS/SAM).
  12. Align Budget with Narrative — Budget and narrative must tell the same story. If narrative describes 5 staff members, budget must reflect 5 positions. Mismatches signal carelessness.

Read the full file on GitHub · 55 lines

Changes

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.

  1. 4d ago First seen · 55 lines · 33 tokens per session scan A 955d06f38450

Subscribe to this mod's changes

Quill is an agent published in the GitHub repository djbelieny/nova (5 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 898 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 2026-08-31.

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