Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add iFurySt/aifi --skill valuation-scenario-analysisgit clone --depth 1 https://github.com/iFurySt/aifiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ifuryst/aifi/valuation-scenario-analysis)<a href="https://agentmods.dev/skills/ifuryst/aifi/valuation-scenario-analysis"><img src="https://agentmods.dev/badge/skills/ifuryst/aifi/valuation-scenario-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ifuryst/aifi/valuation-scenario-analysis"><img src="https://agentmods.dev/badge/skills/ifuryst/aifi/valuation-scenario-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.00553 |
| Opus 5 | $0.00032 | $0.00277 |
| Sonnet 5 | $0.00013 | $0.00111 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
valuation-scenario-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Valuation Scenario Analysis
Use this skill after the target has a financial snapshot and enough evidence to support assumptions. The goal is not a single price target; it is a transparent range of outcomes that shows which drivers matter most and where evidence is weak.
Inputs
ResearchTargetfromresearch-target-resolver- financial snapshot with period labels, currency, and source links
- peer comparison or historical multiple context when available
- thesis, risks, or catalysts that affect assumptions
- current market price and timestamp when price-implied return is requested
Workflow
- Load the target profile, financial evidence, peer context, and current market data if needed.
- Choose the valuation method that fits the business model and data quality: multiples, DCF, sum-of-the-parts, asset value, or probability-weighted event analysis.
- Define the key drivers before calculating outputs: revenue growth, margin, capital intensity, reinvestment, terminal assumptions, share count, net debt, and segment mix where relevant.
- Build bear, base, and bull cases with explicit assumptions and evidence links.
- Run sensitivity checks on the few assumptions that most affect value.
- Compare scenario value ranges with market price only when price data is fresh enough for the user's request.
- Save the model note under
research/targets/<target>/artifacts/valuation/.
Read references/valuation-framework.md before building the model.
Output
Return:
- valuation method and why it fits the target
- assumption table with sources and confidence labels
- bear, base, and bull scenario outputs
- sensitivity table for the most important drivers
- market-implied expectations when current price is used
- data gaps, stale inputs, and assumptions that need manual review
- archive files created or updated
- handoffs to thesis, risk, and watchlist skills
Quality Gate
Before finishing:
- do not present a target price as a recommendation
- state currency, share count basis, enterprise value adjustments, and data timestamps
- separate sourced inputs from agent assumptions
- avoid false precision; round outputs to a level supported by the inputs
- label stale or missing market prices, estimates, and peer multiples
- explain which assumptions drive most of the valuation range
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 65 lines · 64 tokens per session scan A c78a6fb9d5ec
valuation-scenario-analysis is a skill published in the GitHub repository iFurySt/aifi (22 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 553 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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