Qualitative Assessment logo

Qualitative Assessment

Community
Ruinius
Qualitative Assessment

Assess a company's economic moat, EBITA margin trajectory, and organic growth trajectory from analyst reports, earnings transcripts, or long-form articles. Produces structured qualitative outlook that feeds into the Financial Modeling skill's assumption generation.

Overview

PublisherRuinius
Repositoryfinancial-analyst-skills
Skill nameQualitative Assessment
Stars
5
Forks
1
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Qualitative Assessment 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/Ruinius/financial-analyst-skills/tree/main/skills/qualitative_assessment
  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/Ruinius/financial-analyst-skills.git /tmp/financial-analyst-skills
mkdir -p .claude/skills
cp -r /tmp/financial-analyst-skills/skills/qualitative_assessment .claude/skills/ruinius-qualitative-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qualitative Assessment 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 Qualitative Assessment 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 Qualitative Assessment 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.

Qualitative Assessment Skill

Purpose

This skill reads qualitative source documents (analyst reports, earnings call transcripts, press articles) and produces a structured assessment of three dimensions that directly feed into the DCF model assumptions:

  1. Economic Moat — determines terminal growth rate and margin sustainability
  2. EBITA Margin Outlook — adjusts Stage 2 margin assumptions (+/- pp)
  3. Organic Growth Outlook — adjusts Stage 2 growth assumptions (+/- pp)

Without this skill, the Financial Modeling skill falls back to pure historical trend continuation, which misses forward-looking information.

Prerequisites

  • The document must already be classified (Skill 1) — ticker, company name, and document type known
  • The company's output_data/TICKER/ directory must already exist (i.e., at least one financial report has been processed)
  • TICKER_metadata.md should exist with a Financial History table for context

Inputs

  • Classified document in processing_data/ (analyst report, transcript, or article)
  • output_data/TICKER/TICKER_metadata.md — for existing assessments to compare/harmonize

Outputs

  • Updated TICKER_metadata.md with Qualitative Assessment section (new or harmonized with existing)

Step-by-Step Instructions

Step 1: Read the Source Document

Read the classified document from processing_data/. Identify:

  • Source type: Analyst report, earnings transcript, press article, etc.
  • Source date: Publication or earnings call date
  • Author/firm: e.g., "Morningstar (Dan Romanoff, CPA)"

Also read the company's TICKER_metadata.md to check for any existing Qualitative Assessment.

Step 2: Assess Economic Moat

Determine the company's economic moat rating: Wide, Narrow, or None.

RatingDefinitionTerminal Growth Implication
WideStructural advantages expected to sustain excess ROIC for 20+ years. Dominant network effects, high switching costs, or irreplaceable intangible assets.Terminal growth = 4.0%
NarrowClear competitive advantage, but likely to face erosion within 10-20 years. Currently outperforming cost of capital but not deep enough for long-term protection.Terminal growth = 3.0%
NoneHighly commoditized or intensely competitive. No sustainable advantage. Short-term excess profits are quickly competed away.Terminal growth = 2.5%

For each moat rating, provide:

  • Rating: Wide / Narrow / None
  • Confidence: High / Medium / Low
  • Source: Document name and date
  • Three bullets of rationale — each should be specific, cite evidence from the document, and name the moat source (network effects, switching costs, intangible assets, cost advantage, or efficient scale)

💡 If the source document explicitly states a moat rating (e.g., Morningstar reports), use that rating and cite it. Your rationale should support or contextualize their assessment.

Step 3: Assess EBITA Margin Trajectory

Determine whether the company's EBITA margin will expand or shrink over the next 5 years, and by how much.

FieldHow to Determine
DirectionExpand / Shrink / Stable
Magnitude+/- 1-4 pp over 5 years (typical range)
ConfidenceHigh / Medium / Low

For the three bullets of rationale, consider:

  • Revenue mix shifts (high-margin vs. low-margin segments growing at different rates)
  • Operating leverage (fixed costs being spread over growing revenue)
  • Investment cycles (R&D, capex ramps that pressure near-term margins)
  • Competitive pricing pressure
  • Regulatory costs

⚠️ Be conservative. A +2 pp expansion over 5 years is significant. Moves larger than ±4 pp require very strong evidence.

Step 4: Assess Organic Growth Trajectory

Determine whether the company's organic revenue growth will increase or decrease relative to the current L4Q trend, and by how much.

FieldHow to Determine
DirectionIncrease / Decrease / Stable
Magnitude+/- 1-4 pp over 5 years relative to L4Q trend
ConfidenceHigh / Medium / Low

For the three bullets of rationale, consider:

  • TAM expansion or saturation
  • New product launches and adoption curves
  • Competitive dynamics and market share trends
  • Macro/cyclical headwinds or tailwinds
  • Management guidance and forward-looking commentary
  • Base effects (easy or tough YoY comparisons)

Step 5: Harmonize with Existing Assessment

If TICKER_metadata.md already has a Qualitative Assessment section:

  1. Compare each dimension (moat, margin, growth) with the existing assessment
  2. If the new source agrees with the existing assessment: keep the existing, update the "Last updated" date and source
  3. If the new source disagrees:
    • Consider recency (more recent = more weight)
    • Consider source quality (Morningstar analyst > news article)
    • Consider confidence levels
    • Update the assessment if the new evidence is stronger; otherwise keep existing and note the disagreement in a comment

Step 6: Write Output to Markdown

Write or update the following section in TICKER_metadata.md:

markdown
---

## Qualitative Assessment

_Last updated: {date} | Source: {source_description}_

### Economic Moat

| Field      | Value                  |
| ---------- | ---------------------- |
| Rating     | **{Wide/Narrow/None}** |
| Confidence | {High/Medium/Low}      |
| Source     | {source and date}      |

1. **{Moat Source 1}**: {specific evidence from the document}
2. **{Moat Source 2}**: {specific evidence}
3. **{Moat Source 3}**: {specific evidence}

### EBITA Margin Outlook

| Field      | Value                      |
| ---------- | -------------------------- |
| Direction  | **{Expand/Shrink/Stable}** |
| Magnitude  | {+/- N pp over 5 years}    |
| Confidence | {High/Medium/Low}          |

1. {Specific rationale with evidence}
2. {Specific rationale with evidence}
3. {Specific rationale with evidence}

### Organic Growth Outlook

| Field      | Value                          |
| ---------- | ------------------------------ |
| Direction  | **{Increase/Decrease/Stable}** |
| Magnitude  | {+/- N pp over 5 years}        |
| Confidence | {High/Medium/Low}              |

1. {Specific rationale with evidence}
2. {Specific rationale with evidence}
3. {Specific rationale with evidence}

Step 7: Move Source Document

Move the processed document from processing_data/ to output_data/TICKER/:

  • Rename to remove the _temp suffix
  • Both the PDF and the markdown file

How Downstream Skills Use This Output

The Financial Modeling assumptions skill (skills/financial_modeling/assumptions/) reads this section to adjust historical trends:

AssessmentDownstream Effect
Economic Moat = WideTerminal growth 4.0%, terminal margin = Stage 2 margin (sustained)
Economic Moat = NarrowTerminal growth 3.0%, terminal margin converges toward industry average
Economic Moat = NoneTerminal growth 2.5%, terminal margin = industry average
EBITA Margin: Expand +2 ppStage 2 margin = L4Q margin + 2 pp
EBITA Margin: Shrink -2 ppStage 2 margin = L4Q margin - 2 pp
Organic Growth: Increase +4 ppStage 2 growth = L4Q growth + 4 pp
Organic Growth: Decrease -1 ppStage 2 growth = L4Q growth - 1 pp

If no qualitative assessment exists, the assumptions skill defaults to Narrow moat behavior and pure historical continuation.


Important Notes

  • Be specific, not generic. Every rationale bullet should cite evidence from the source document — numbers, quotes, product names, dates. Avoid vague statements like "strong brand" or "good management."
  • Three bullets per dimension, no more. Forces prioritization of the most important factors.
  • Magnitude matters. "+2 pp" and "+4 pp" produce very different DCF outcomes. Be precise and conservative.
  • Confidence is about the evidence quality, not your personal certainty. A Morningstar deep-dive = High. A single news article = Low.
  • The margin and growth outlooks are relative to the current L4Q trend, not absolute targets. If L4Q margin is 38% and you say "Expand +2 pp", the assumptions skill will set Stage 2 margin to 40%.

Reference

Based on extract_qualitative_assessment() in tiger-cafe/app/app_agents/qualitative_extractor.py, adapted from LLM-only assessment to document-grounded analysis.

Changelog

  • 2026-05-06: Processed first FISV analyst report. Added FISV example and improved guidance on handling the first document for a new ticker.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qualitative Assessment AI skill do?

Assess a company's economic moat, EBITA margin trajectory, and organic growth trajectory from analyst reports, earnings transcripts, or long-form articles. Produces structured qualitative outlook that feeds into the Financial Modeling skill's assumption generation.

Why use Qualitative Assessment on TypingMind?

Because you install it once and use it with any model. Qualitative Assessment 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 Qualitative Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ruinius/financial-analyst-skills/tree/main/skills/qualitative_assessment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qualitative Assessment?

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 Qualitative Assessment?

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

Is the Qualitative Assessment AI skill free?

Yes. It is published on GitHub by Ruinius 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇