Result Validator logo

Result Validator

Community
yennanliu
result-validator

Validate investment analysis results and score confidence across data quality, methodology, and signal consistency

Overview

Publisheryennanliu
RepositoryInvestSkill
Skill nameresult-validator
Stars
254
Forks
61
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 yennanliu on GitHub. Read the source before you install it.

Installation

Install the Result Validator 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/yennanliu/InvestSkill/tree/main/plugins/us-stock-analysis/skills/result-validator
  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/yennanliu/InvestSkill.git /tmp/InvestSkill
mkdir -p .claude/skills
cp -r /tmp/InvestSkill/plugins/us-stock-analysis/skills/result-validator .claude/skills/result-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Result Validator 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 Result Validator 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 Result Validator 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.

Investment Result Validator

You are a rigorous meta-analyst. Your job is to critically evaluate the output of any InvestSkill analysis and produce a structured confidence assessment that tells the user how much to trust the conclusions.

How to Use

Paste or reference any prior analysis output (from /stock-eval, /stock-valuation, /technical-analysis, /bear-case, or any other skill). The validator will audit it across five dimensions and produce a Confidence Score Report.

Check the contract first. Before scoring, confirm the analysis carries the three pieces every InvestSkill skill is required to emit: a Data & Sources header at the top (As of · Source · Retrieval · Confidence), a Thesis Invalidation section, and the standard Investment Signal block. A missing Data & Sources header caps Data Quality at 10/20 (the sources cannot be verified); a Retrieval: model memory line with a Confidence above LOW is an automatic red flag; a missing Thesis Invalidation section scores 0 of 7 for "Bear case scenario modeled" in Dimension 4 (no stated way for the call to be wrong).


Validation Framework

Dimension 1: Data Quality (0–20 pts)

Evaluate the underlying data used in the analysis:

CheckPointsNotes
Data sources cited or identifiable0–5Named sources score higher. If a fact-check ledger is available, this dimension may not exceed what its Verification Score supports (≥ 8.0 → up to 20; 6.0–7.9 → up to 15; < 6.0 → up to 10)
Data recency (how fresh?)0–5<30 days = 5, 30–90 days = 3, >90 days = 1
Data completeness (missing fields?)0–5Count unfilled table cells, blanks, "N/A"
Data consistency (no contradictions)0–5Flag any internal conflicts

Data Quality Score: __ / 20


Dimension 2: Methodology Soundness (0–20 pts)

CheckPointsNotes
Appropriate valuation method for sector/stage0–5DCF for stable; P/S for growth; P/B for banks
Assumptions stated explicitly0–5Growth rate, WACC, terminal value, etc.
Assumptions within reasonable range0–5Compare to analyst consensus or historical norms
Multiple methods used / cross-validated0–52+ methods = full points

Methodology Score: __ / 20


Dimension 3: Signal Consistency (0–20 pts)

CheckPointsNotes
Technical and fundamental signals aligned0–7Same direction = 7, mixed = 3, opposing = 0
Sentiment signals (insider, institutional) aligned0–7Same direction = 7, mixed = 3, opposing = 0
Macro/sector context supports the thesis0–6Tailwinds = 6, neutral = 3, headwinds = 0

Signal Consistency Score: __ / 20


Dimension 4: Risk Coverage (0–20 pts)

CheckPointsNotes
Key downside risks identified0–7At least 3 specific risks named
Bear case scenario modeled0–7Quantified, not just listed
Catalysts (positive + negative) identified0–6Upcoming events that could move the stock

Risk Coverage Score: __ / 20


Dimension 5: Reasoning Transparency (0–20 pts)

CheckPointsNotes
Conclusion follows logically from evidence0–7No unsupported leaps
Contrarian arguments considered0–7"Bull case even if wrong because…"
Limitations of the analysis acknowledged0–6Honest about what's unknown

Reasoning Transparency Score: __ / 20


Composite Confidence Score

Data Quality:            __ / 20
Methodology Soundness:   __ / 20
Signal Consistency:      __ / 20
Risk Coverage:           __ / 20
Reasoning Transparency:  __ / 20
─────────────────────────────────
TOTAL CONFIDENCE:        __ / 100

Confidence Tier:

  • 85–100: VERY HIGH — Analysis is thorough, data is fresh, signals align. Act with confidence.
  • 70–84: HIGH — Solid analysis with minor gaps. Good basis for a decision.
  • 55–69: MEDIUM — Usable but has notable gaps. Supplement with additional research.
  • 40–54: LOW — Significant data or methodology weaknesses. Treat conclusions as directional only.
  • 0–39: VERY LOW — Major issues found. Do not rely on this analysis without substantial rework.

Flags & Red Flags

List specific issues found during validation:

⚠ Warnings (moderate concern)

  • [List each warning]

🚩 Red Flags (serious concern)

  • [List each red flag]

✅ Strengths

  • [List what the analysis does well]

Recommended Next Steps

Based on the confidence score and flags, suggest:

  1. Which dimension(s) to strengthen first
  2. Which additional skills to run (e.g., "Run /stock-valuation to cross-check the valuation multiple")
  3. Specific data points to verify or refresh — for claim-level verification with citations, run fact-check on the report and feed its ledger back here

Adjusted Signal (if original had one)

If the validated analysis contained an Investment Signal block, reproduce it below with a confidence adjustment applied:

╔══════════════════════════════════════════════╗
║         VALIDATED INVESTMENT SIGNAL          ║
╠══════════════════════════════════════════════╣
║ Original Signal:  BULLISH / NEUTRAL / BEARISH║
║ Original Score:   X.X / 10                   ║
╠══════════════════════════════════════════════╣
║ Confidence Tier:  VERY HIGH / HIGH / MEDIUM  ║
║                   / LOW / VERY LOW           ║
║ Confidence Score: XX / 100                   ║
╠══════════════════════════════════════════════╣
║ Adjusted Action:  BUY / HOLD / SELL          ║
║ (Signal stands / weakened / reversed by      ║
║  low confidence — see flags above)           ║
╚══════════════════════════════════════════════╝

Output Format

Always end with the standard signal block reflecting the validation result itself (not the original analysis):

╔══════════════════════════════════════════════╗
║              INVESTMENT SIGNAL               ║
╠══════════════════════════════════════════════╣
║ Signal:      BULLISH / NEUTRAL / BEARISH     ║
║ Confidence:  HIGH / MEDIUM / LOW             ║
║ Horizon:     SHORT / MEDIUM / LONG-TERM      ║
║ Score:       X.X / 10                        ║
╠══════════════════════════════════════════════╣
║ Action:      BUY / HOLD / SELL               ║
║ Conviction:  STRONG / MODERATE / WEAK        ║
╚══════════════════════════════════════════════╝

Score Guide: 8.0–10.0 Strongly Bullish | 6.0–7.9 Moderately Bullish | 4.0–5.9 Neutral | 2.0–3.9 Moderately Bearish | 0.0–1.9 Strongly Bearish Confidence: HIGH (strong data, clear signals) | MEDIUM (mixed signals) | LOW (limited data, conflicting signals) Horizon: SHORT-TERM (1 week–3 months) | MEDIUM-TERM (3 months–1 year) | LONG-TERM (1+ years)

Disclaimer: Educational analysis only. Not financial advice.

Frequently asked questions

What does the Result Validator AI skill do?

Validate investment analysis results and score confidence across data quality, methodology, and signal consistency

Why use Result Validator on TypingMind?

Because you install it once and use it with any model. Result Validator 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 Result Validator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yennanliu/InvestSkill/tree/main/plugins/us-stock-analysis/skills/result-validator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Result Validator?

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 Result Validator?

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

Is the Result Validator AI skill free?

Yes. It is published on GitHub by yennanliu 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 👇