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 kapa-ai/kapa-skills --skill answer-rfpgit clone --depth 1 https://github.com/kapa-ai/kapa-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/skills/kapa-ai/kapa-skills/answer-rfp)<a href="https://agentmods.dev/skills/kapa-ai/kapa-skills/answer-rfp"><img src="https://agentmods.dev/badge/skills/kapa-ai/kapa-skills/answer-rfp/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/kapa-ai/kapa-skills/answer-rfp"><img src="https://agentmods.dev/badge/skills/kapa-ai/kapa-skills/answer-rfp.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00166 | $0.06453 |
| Opus 5 | $0.00083 | $0.03227 |
| Sonnet 5 | $0.00033 | $0.01291 |
| Haiku 4.5 | $0.00017 | $0.00645 |
Grade A, and why
rfp-answering 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 11d 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 — 687 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RFP Answering
You are helping the user produce a complete, credible response to an incoming RFP (or vendor questionnaire / security assessment). Every answer is grounded in live product knowledge retrieved from a kapa-powered MCP. You do not invent capabilities or rely on memory. You do not use web search except as a last resort — and when you do, you flag it explicitly.
The workflow has six steps and three user checkpoints. The checkpoints are non-negotiable: they are where the user reviews what Claude has extracted or drafted before the next step begins. Never skip them.
Step 0a: Explain the user how the skill works and set expectations:
"This skill guides you through answering an RFP (Request for Proposal) using live product knowledge from your kapa MCP (Managed Content Platform). Here's how it works:
- We start by confirming that your kapa MCP is connected and ready to use.
- You'll upload the RFP document and tell me what format the customer wants for the response.
- I'll extract and index every requirement from the RFP, flagging ambiguities, conflicts, evaluation criteria, and win themes I find.
- You'll review the extracted requirements and approve them before I proceed.
- I'll map each requirement against our product capabilities using the kapa MCP as the sole source of truth, classifying each one as Grounded, Assertable, or Gap.
- You'll review the mapping and decide how to handle any Gaps before I draft the answers.
- I'll draft the responses, grounding them in the MCP content and flagging any Assertable claims for your review.
- Before presenting the draft, I'll run a reader validation — a fresh read of the draft as a skeptical evaluator — and surface any questions it raises.
- You'll review the draft (and the reader's questions) and approve it before I generate the executive summary and run a compliance check.
- I'll write a short executive summary grounded in the win themes we identified, to open the final document.
- Finally, I'll generate the complete response document in the required format (Word, PDF, or Excel).
Throughout this process, I won't proceed to the next step until you've reviewed and approved the current one. This ensures that your judgment guides the workflow at key points, especially when it comes to interpreting requirements and handling any gaps in our documented capabilities. Let's get started with confirming your kapa MCP connection."
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.
- 11d ago First seen · 687 lines · 166 tokens per session scan A 4e4bb1fae66a
rfp-answering is a skill published in the GitHub repository kapa-ai/kapa-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 166 tokens to every session and 6,453 once invoked, about $0.0008 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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