Rfp Response Extraction logo

Rfp Response Extraction

Organization
pnp
rfp-response-extraction

Extracts questions, mandatory requirements, submission rules, evaluation criteria, and administrative certifications from a Request for Proposal (RFP) document and populates a SharePoint List with each item categorized, flagged for mandatory status, source-traced, and ready for owner assignment and tracking. Handles addenda by letting later versions override earlier ones.

Overview

Publisherpnp
Repositorysharepoint-skills
Skill namerfp-response-extraction
Stars
141
Forks
60
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by pnp on GitHub. Read the source before you install it.

Installation

Install the Rfp Response Extraction AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pnp/sharepoint-skills/tree/main/Skills/rfp-response-extraction/rfp-response-extraction
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pnp/sharepoint-skills.git /tmp/sharepoint-skills
mkdir -p .claude/skills
cp -r /tmp/sharepoint-skills/Skills/rfp-response-extraction/rfp-response-extraction .claude/skills/rfp-response-extraction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rfp Response Extraction in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Rfp Response Extraction on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Rfp Response Extraction is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

RFP Extraction Skill

Firm Profile (REQUIRED — fill in before use)

These values define who your firm is and how it writes. See SETUP.md.

  • FIRM NAME: [FIRM NAME]
  • WHAT THE FIRM DOES: [one sentence]
  • ROLE BOUNDARY: [what the firm will NOT claim to do — leave blank if none]
  • VENDOR STANCE: [vendor-neutral OR resells/implements specific products]
  • SPECIALIZATION: [domains the firm credibly claims]
  • VOICE: [3–5 adjectives]

Purpose

Parse an RFP stored in SharePoint and populate a structured RFP Tracker List with every actionable item, each broken out by category, flagged mandatory or not, traced to its source location, and given tracking columns for owner assignment and progress monitoring.


Prerequisites

  • The RFP document (PDF, Word, etc.) must be accessible in SharePoint.
  • The agent must have permission to create Lists and List items in the target site.
  • If the package includes addenda, attachments, or a referenced Q&A document, those should also be accessible. Flag any that are missing in the summary.

Step 1 — Read the RFP Document

  1. Locate the RFP file in SharePoint using the path/URL provided. If the package has multiple documents (RFP body, SOW, attachments, addenda, Q&A), list every one and capture filename, page count, and document type.
  2. Extract the full text of each document.
  3. If a document is long, process it in sections but treat it as one document for extraction.
  4. Process addenda last and let them override. If an addendum changes a due date, page limit, or requirement, the later version wins. Diff the changed section against the original — do not assume addenda are purely additive.

Step 1b — (Optional) Capture Solicitation Metadata

Optionally capture solicitation-level facts to an RFP Metadata list (one record per RFP). Skip for simple RFPs unless the user asks.

FieldWhere to look
RFP / reference numberCover page or header
Issuing organizationCover page
Primary contact + email + phoneInstructions to bidders section
Submission deadline (date + time + zone)Instructions section — check addenda
Question / clarification deadlineInstructions section
Page limitsInstructions / format section
Font / margin / spacing rulesFormat section
File format and naming conventionSubmission instructions
Submission method (portal, email)Submission instructions
Period of performance / contract termScope or terms section
Anticipated award / decision dateTimeline or evaluation section
Evaluation approachEvaluation section (lowest price, best value, weighted scoring, etc.)

If a field is missing, write NOT SPECIFIED and flag it in the summary so the team can raise a clarifying question before the question deadline.


Step 2 — Extract RFP Items

Extract every item falling into a category below; record the fields listed.

Categories and What to Look For

CategoryWhat to Extract
QuestionAny question the RFP poses (e.g., "Describe your approach to…", "How does your solution handle…")
Mandatory RequirementStatements using must, shall, required, mandatory, will be required to
Submission RuleHow the proposal must be submitted (format, page limits, deadlines, file types, copies, labeling, volume structure)
Evaluation CriterionCriteria, scoring weights, or factors used to evaluate proposals (e.g., "Price will account for 30% of the score")
Administrative Certification (optional 5th — use when present)Representations & certifications, conflict-of-interest declarations, signatory authority, insurance/registration attestations, and similar pass/fail eligibility items

Fields to Record Per Item (write to the RFP Tracker list)

FieldValue
ItemThe requirement, question, or criterion text (verbatim or lightly trimmed)
CategoryOne of the five categories above
MandatoryYes / No — Yes for anything using obligatory language or marked required
Source LocationPage and/or section number in the source document
Owner(blank — assigned later by the team)
StatusNot Started (default)

Step 3 — De-duplicate and Normalize

  1. RFPs often restate the same requirement in multiple places. Merge obvious duplicates into one row, but keep both source locations in Source Location.
  2. Split compound items — a sentence asking two distinct questions becomes two rows so each can be owned and answered separately.
  3. Normalize phrasing for readability without changing meaning, but keep enough original wording that the row stays traceable to the document.

Step 4 — Write to the RFP Tracker List

  1. Create the RFP Tracker list items (or update existing ones on a re-run).
  2. Place Mandatory = Yes rows at the top of the sort order if the view supports it.
  3. Do not populate Available Content or Source columns — those are filled by the Content Finder skill in the next step of Phase 1.

Step 5 — Report Back to the User

Provide a summary with:

  • Total items extracted, split by category.
  • Count of mandatory vs. non-mandatory items.
  • Any solicitation metadata marked NOT SPECIFIED (if Step 1b ran).
  • Any referenced attachments or addenda that could not be located.
  • Any ambiguous items that may need human classification.
  • Confirmation that the RFP Tracker list is populated, with a link to it.

Example Summary Output

RFP Extraction complete — RFP Tracker populated.

Items extracted: 31
  • Questions:                 18
  • Mandatory Requirements:     7
  • Submission Rules:           4
  • Evaluation Criteria:        2
  • Administrative Certs:       0

Mandatory items: 9 (flagged and sorted to top)

Addenda processed: 1 (Addendum 2 moved the deadline +1 week — applied)

Items needing human classification: 2
  • Row 14 — compound item split into two questions
  • Row 22 — unclear whether procedural or content rule

RFP Tracker:
   https://contoso.sharepoint.com/sites/Proposals/Lists/RFPTracker

Next step: Run the Content Finder skill to match existing content to each row.

Notes & Edge Cases

  • Scanned / image-only RFPs: OCR first. Flag pages with low OCR confidence.
  • Conflicting addenda: The latest one wins. Note the conflict in the summary.
  • No evaluation criteria stated: Record this explicitly — the Strategy Synthesis skill needs to know weights are unstated so it can infer them.
  • Very large packages: Extract from each document but write to a single tracker. Keep the source filename in Source Location so every row is traceable.

Frequently asked questions

What does the Rfp Response Extraction AI skill do?

Extracts questions, mandatory requirements, submission rules, evaluation criteria, and administrative certifications from a Request for Proposal (RFP) document and populates a SharePoint List with each item categorized, flagged for mandatory status, source-traced, and ready for owner assignment and tracking. Handles addenda by letting later versions override earlier ones.

Why use Rfp Response Extraction on TypingMind?

Because you install it once and use it with any model. Rfp Response Extraction is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Rfp Response Extraction in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pnp/sharepoint-skills/tree/main/Skills/rfp-response-extraction/rfp-response-extraction. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rfp Response Extraction?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Rfp Response Extraction?

As many as you like. As long as a model supports skills, you can use Rfp Response Extraction with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Rfp Response Extraction AI skill free?

Yes. It is published on GitHub by pnp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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