# Send Equity Valuation Framework to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
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      "primaryActionLabel": "Download for OpenClaw",
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        "Confirm the extracted package contains the expected setup assets."
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      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
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  },
  "links": {
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    "briefUrl": "https://openagent3.xyz/skills/equity-valuation-framework/agent.md"
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}
```
## Documentation

### Equity Valuation Framework

Use this skill as the "rules of the game" for valuation decisions and report standardization.

### Scope and role

Purpose: transform already-fetched data into a professional valuation view.
This skill does not fetch data.
Upstream data should come from:

vnstock-free-expert for company/price/ratio inputs
nso-macro-monitor, us-macro-news-monitor, vn-market-news-monitor for macro/news context

### When to trigger

User asks: "value this stock", "is it cheap/expensive", "best stock between A/B/C", "give me bull/base/bear", "build an investment memo".
User requests a decision-ready report, not only raw metrics.

### Required input contract

Accept an input bundle with these sections (missing fields allowed, but must be flagged):

{
  "ticker": "HPG",
  "as_of_date": "YYYY-MM-DD",
  "currency": "VND",
  "financials": {
    "income_statement": {},
    "balance_sheet": {},
    "cash_flow": {},
    "ratios": {}
  },
  "price_history": {
    "daily": [],
    "returns": {
      "1m": null,
      "3m": null,
      "6m": null,
      "12m": null
    }
  },
  "peer_set": ["AAA", "BBB"],
  "macro_snapshot": {},
  "news_digest": {},
  "metadata": {
    "source": "kbs|vci",
    "data_quality_notes": []
  }
}

### Execution workflow (ordered)

Validate input bundle completeness and freshness.
Run the data quality gate and assign initial confidence.
Select valuation modules based on available data (Multiples, DCF, sector adaptation).
Build bull/base/bear scenarios with explicit assumptions.
Triangulate fair value, define safety zone, and list key risks.
Apply confidence rubric and disclose gaps that can change conclusions.
Return the report using the required section order.

### Data quality gate (must run first)

Check freshness: state report periods and price cutoff date.
Check completeness: identify missing key lines (revenue, EBIT, net income, CFO, debt, equity, shares).
Check consistency: basic identity checks (assets = liabilities + equity if available).
Mark confidence tier:

High: complete + recent + internally consistent.
Medium: minor gaps, valuation still usable.
Low: major gaps; only directional view allowed.

### Shared confidence rubric (required)

Use this standardized interpretation:

High: valuation triangulation is valid (>= 2 robust methods), assumptions are explicit, and key inputs are complete.
Medium: only one robust method is usable or moderate gaps require wider valuation ranges.
Low: major input gaps/quality issues force directional valuation only (no precise fair-value claim).

Always report:

Confidence level.
Which modules were actually run (Multiples, DCF, sector adaptations).
Critical missing inputs that would most likely change fair value.

### Valuation modules

Run modules based on available data. Prefer triangulation (2+ methods).

### 1) Relative valuation (Multiples)

Use when at least one of earnings/book/EBITDA is reliable.

Core multiples:

P/E (earnings-based)
P/B (capital-intensive, banks/financials)
EV/EBITDA (operating comparison)
Optional: EV/Sales, P/CF


Compare across:

peer median / percentile
company 3-5y own history


Normalize for one-off items when possible.
Output:

implied value range per multiple
weighted relative-value estimate

### 2) DCF valuation

Use only when cash-flow visibility is acceptable.

Model setup:

Forecast horizon: 5-10 years (default 5 if uncertain)
Revenue growth path by scenario
Margin path (EBIT/FCF margin)
Reinvestment assumptions
WACC with explicit inputs (risk-free, ERP, beta, debt cost)
Terminal value: Gordon or exit multiple (state choice)


Mandatory sensitivity grid:

WACC ±100 bps
terminal growth ±50 bps


Output:

base/bull/bear fair value
sensitivity table

### 3) Sector-specific adaptation

Banks / Insurance / Financials

Prioritize: P/B, ROE, asset quality proxies, capital adequacy proxies, funding cost/NIM proxies.
De-emphasize EV/EBITDA.
Evaluate sustainability of ROE and provisioning pressure.

Cyclicals (steel, chemicals, commodities, shipping)

Use cycle-aware assumptions:

normalized margin, not peak margin
conservative terminal assumptions


Add cycle-risk note as first-class risk item.

### Quality and business resilience checklist

Assess each item as Strong / Neutral / Weak with one-line evidence:

Moat and pricing power
Governance and capital allocation
Earnings quality (cash conversion, accrual risk)
Balance-sheet risk (leverage, maturity risk)
Cyclicality and external dependency
Execution track record

### Scenario framework (required)

Always provide three scenarios:

Bull: better macro + execution upside
Base: most likely path under current conditions
Bear: macro/industry shock + execution shortfall

For each scenario include:

Key assumptions
Expected fundamental trajectory
Implied fair value range
Probability weight (optional but preferred)

### Margin of safety rule

Define Fair Value range from module triangulation.
Define Safety Zone below fair value (default 15-30% depending on confidence and cyclicality).
Avoid absolute buy/sell commands.
Use language: "appears undervalued / fairly valued / stretched" and "requires margin-of-safety discipline".

### Decision policy (how to conclude)

Create an integrated view from:

valuation outputs (multiples + DCF if valid)
business quality checklist
macro/news constraints

If the user is managing a watchlist/portfolio, end with conditional action framing suitable for portfolio-risk-manager:

Trigger to add risk (what would increase conviction)
Trigger to reduce risk
Invalidation (what would make the thesis wrong)
Horizon (ngắn/trung/dài)

Conclusion label:

Attractive (valuation discount + acceptable quality/risk)
Watchlist (mixed signals, wait for trigger)
Caution (valuation unsupported or risk too high)

### Required report output template

Return exactly these sections in this order:

Executive Summary

One paragraph: current valuation stance and why.

What Data Was Used

Source, as-of date, statement periods, peer set.

Core Thesis (Bull / Base / Bear)

Key drivers by scenario.

Valuation Work

Multiples table (current vs peer vs implied)
DCF summary (if run)
Sensitivity table

Business Quality Assessment

Checklist table with evidence lines.

Risk Register

Ranked risks with impact, probability, and monitoring trigger.

Fair Value and Safety Zone

Fair value range and margin-of-safety zone with rationale.

Confidence and Gaps

Confidence level and exact missing data that could change the view.

Disclaimer

Educational analysis only, not personalized investment advice.

### Formatting standards

Use simple language and explain terms briefly.
State all critical assumptions explicitly.
Distinguish facts vs assumptions vs inference.
Do not hide data gaps; surface them early.
Keep numbers auditable and unit-consistent (VND bn/trn, %, x).

### Minimal scoring rubric (optional but recommended)

If user asks for ranking within this framework:

Valuation 40%
Quality 35%
Momentum/Revision 15%
Risk penalty 10%

Calibrate per sector and confidence.

### Fail-safe behavior

If data quality is low:

downgrade confidence
skip fragile modules (e.g., DCF)
deliver directional valuation only
list exact data needed for full valuation

### Trigger examples

"Value HPG with bull/base/bear and margin of safety."
"Compare VCB vs BID valuation and explain the thesis."
"Prepare a structured valuation memo with sensitivity table and risk register."
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: NDTChan
- Version: 1.0.3
## Source health
- Status: healthy
- Item download looks usable.
- Yavira can redirect you to the upstream package for this item.
- Health scope: item
- Reason: direct_download_ok
- Checked at: 2026-04-29T15:34:36.777Z
- Expires at: 2026-05-06T15:34:36.777Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/equity-valuation-framework)
- [Send to Agent page](https://openagent3.xyz/skills/equity-valuation-framework/agent)
- [JSON manifest](https://openagent3.xyz/skills/equity-valuation-framework/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/equity-valuation-framework/agent.md)
- [Download page](https://openagent3.xyz/downloads/equity-valuation-framework)