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alirezarezvani
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rfp-responder

Parses RFP requirements, builds proof-point matrices, and estimates winrates for bid decisions.

25.3k|3.6k|Updated Oct 19, 2025
Search Tags:#proposal#rfp#sales engineering#winrate#bid management#compliance matrix#shipley
One-click install
npx skills add https://github.com/alirezarezvani/claude-skills --skill rfp-responder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rfp-responder
Source: https://github.com/alirezarezvani/claude-skills/tree/main/commercial/skills/rfp-responder
Command: npx skills add https://github.com/alirezarezvani/claude-skills --skill rfp-responder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Responding to RFPs, RFIs, RFQs, and security questionnaires requires parsing dozens of buyer-dictated requirements, mapping verifiable proof points to each one, and making an honest bid/no-bid call — work that teams often do ad hoc, missing mandatory disqualifiers until late or inventing claims to fill gaps.

Core Features & Use Cases

  • RFP Parsing: Extracts every requirement from an RFP document and tags it MANDATORY, WEIGHTED, or NICE-TO-HAVE using cue-word heuristics, capturing deadlines, scoring criteria, and format constraints.
  • Proof-Point Matrix & GAP Audit: Maps each requirement to a verifiable proof point (case study, certification, customer quote, attestation, benchmark) with STRONG / PARTIAL / GAP ratings, and flags win-themes that fail to thread across multiple requirements.
  • Winrate Estimation: Produces a Shipley-derived winrate estimate with confidence band and a BID / PARTNER-BID / NO-BID verdict, tuned by industry profile (SaaS, enterprise software, services, government, healthcare).
  • Use Case: A 40-page security RFP lands with a 10-day deadline. Run the parser on Day 1 to surface a mandatory FedRAMP requirement you cannot meet, then use the winrate estimate to make a defensible no-bid call before burning proposal capacity.

Quick Start

Parse the attached RFP document, score our fit against each requirement using our proof-point library, and recommend whether we should bid.

Frequently Asked Questions about rfp-responder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I parse an RFP into structured requirements?▼

Run rfp_parser.py with --input pointing to your RFP markdown or text file. It splits the document into sections, extracts requirement lines, and tags each MANDATORY, WEIGHTED, or NICE-TO-HAVE based on cue words like must, shall, should, and preferred, outputting JSON or markdown.

How do I decide whether to bid on an RFP?▼

Feed deal context (fit percentages, incumbent strength, relationship, competitor count, late entry) into winrate_predictor.py. It produces a winrate estimate with confidence band and a verdict: below 20% triggers NO-BID, 20-34% suggests PARTNER-BID, and 35% or above recommends BID.

What is a proof-point matrix in RFP responses?▼

A proof-point matrix maps every RFP requirement to a verifiable evidence source such as a case study, certification, customer quote, technical attestation, or benchmark. Requirements with two or more tag matches and strong proof types rate STRONG; unmatched requirements surface as GAPs for leadership decision.

Does the RFP parser require external Python libraries?▼

No. All three scripts (rfp_parser.py, response_drafter.py, winrate_predictor.py) use only the Python standard library: argparse, json, re, sys, pathlib, collections, and statistics. No pip installs or NLP dependencies are needed.

When should I not use an RFP response automation approach?▼

Do not use it for free-form proposal narrative writing, contract redlining after award, marketing collateral, or pricing-model design. It handles buyer-dictated structured Q&A before award; narrative proposals and legal review belong to separate workflows.

Why does the winrate estimate penalize late entry to an RFP?▼

Late entry (entering after the RFP issued with no prior engagement) applies a -15 percentage point penalty because research shows late-entry vendors win only 8-12% of RFPs versus 25-35% for vendors engaged before issuance. The model reflects this structural disadvantage honestly.

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