Serennity007Serennity007/invest-skill/skills/result-validator/SKILL.md

result-validator

Skill0 starsChanged 3 months agoMITView on GitHub

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

In this repository26 other files it gives its agents.
- 10k-digestSkill
- bear-caseSkill
- catalyst-calendarSkill
- chart-masterSkill
- competitor-analysisSkill
- dcf-valuationSkill
- dividend-analysisSkill
- earnings-call-analysisSkill
- economics-analysisSkill
- financial-report-analystSkill
- full-reportSkill
- fundamental-analysisSkill
- industry-mapSkill
- insider-tradingSkill
- institutional-ownershipSkill
- options-analysisSkill
- portfolio-reviewSkill
- position-ladderSkill
- report-generatorSkill
- research-bundleSkill
- sector-analysisSkill
- short-interestSkill
- stock-evalSkill
- stock-screenerSkill
- stock-valuationSkill
- technical-analysisSkill

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.

Common
Distinctive
Very specific

Distinctive:
If the validated analysis contained an Investment Signal block, reproduce it below with a confidence adjustment applied:
line 128
Distinctive:
Always end with the standard signal block reflecting the validation result itself (not the original analysis):
line 151

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.

Files

SKILL.md

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, /fundamental-analysis, /dcf-valuation, /technical-analysis, or any other skill). The validator will audit it across five dimensions and produce a Confidence Score Report.

Validation Framework

Dimension 1: Data Quality (0–20 pts)

Evaluate the underlying data used in the analysis:

Check	Points	Notes
Data sources cited or identifiable	0–5	Named sources score higher
Data recency (how fresh?)	0–5	<30 days = 5, 30–90 days = 3, >90 days = 1
Data completeness (missing fields?)	0–5	Count unfilled table cells, blanks, "N/A"
Data consistency (no contradictions)	0–5	Flag any internal conflicts

Data Quality Score: __ / 20

Dimension 2: Methodology Soundness (0–20 pts)

Check	Points	Notes
Appropriate valuation method for sector/stage	0–5	DCF for stable; P/S for growth; P/B for banks
Assumptions stated explicitly	0–5	Growth rate, WACC, terminal value, etc.
Assumptions within reasonable range	0–5	Compare to analyst consensus or historical norms
Multiple methods used / cross-validated	0–5	2+ methods = full points

Methodology Score: __ / 20

Dimension 3: Signal Consistency (0–20 pts)

Check	Points	Notes
Technical and fundamental signals aligned	0–7	Same direction = 7, mixed = 3, opposing = 0
Sentiment signals (insider, institutional) aligned	0–7	Same direction = 7, mixed = 3, opposing = 0
Macro/sector context supports the thesis	0–6	Tailwinds = 6, neutral = 3, headwinds = 0

Signal Consistency Score: __ / 20

Dimension 4: Risk Coverage (0–20 pts)

Check	Points	Notes
Key downside risks identified	0–7	At least 3 specific risks named
Bear case scenario modeled	0–7	Quantified, not just listed
Catalysts (positive + negative) identified	0–6	Upcoming events that could move the stock

Risk Coverage Score: __ / 20

Dimension 5: Reasoning Transparency (0–20 pts)

Check	Points	Notes
Conclusion follows logically from evidence	0–7	No unsupported leaps
Contrarian arguments considered	0–7	"Bull case even if wrong because…"
Limitations of the analysis acknowledged	0–6	Honest 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:

Which dimension(s) to strengthen first
Which additional skills to run (e.g., "Run /dcf-valuation to cross-check the valuation multiple")
Specific data points to verify or refresh

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        ║
╚══════════════════════════════════════════════╝

Disclaimer: Educational analysis only. Not financial advice.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

It worked
Partly
It didn't

What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free