position-ladder

position-ladder is a skill for Claude Code from yennanliu/InvestSkill. It costs 44 tokens per session (6,728 once invoked), scanned A, original, MIT.

A stock-trading planning framework for building a position in stages, managing share counts and cost basis, and deciding when to exit if the investment thesis breaks.

In plain words
What is it for?
Use it to plan entry prices, share limits, trim-and-rebuy cycles, lot accounting, wash-sale checks, and thesis-based exit rules for a stock position.
Why use it?
It organizes gradual buying and trimming while accounting for tax rules such as wash sales, so reported returns and average costs are not misleading. It requires current market data before analysis.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: positional $N argument.

Part of the us-stock-analysis plugin — 34 skills shipped together

Good fit Use it to plan entry prices, share limits, trim-and-rebuy cycles, lot accounting, wash-sale checks, and thesis-based exit rules for a stock position.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yennanliu/investskill/position-ladder
Install

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.

Any agent
npx skills add yennanliu/InvestSkill --skill position-ladder
Clone the repo
git clone --depth 1 https://github.com/yennanliu/InvestSkill

Made for: Claude Code.

Or install us-stock-analysis, the plugin that ships this one along with the rest of its 34 skills.

Wrote 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.

agentmods badge for position-ladder

README.md
[![agentmods](https://agentmods.dev/badge/skills/yennanliu/investskill/position-ladder/github.svg)](https://agentmods.dev/skills/yennanliu/investskill/position-ladder)
Your own site
<a href="https://agentmods.dev/skills/yennanliu/investskill/position-ladder"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/position-ladder/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.

agentmods 80×15 button for position-ladder

Your own site · 80×15
<a href="https://agentmods.dev/skills/yennanliu/investskill/position-ladder"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/position-ladder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,728 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.06728
Opus 5.5 $0.00018 $0.02691
Sonnet 5.5 $0.00009 $0.01346
Haiku 4.5 $0.00004 $0.00673

Measured 11d ago against content hash a317ed9670be, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

position-ladder 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 11d 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.

plugins/us-stock-analysis/skills/position-ladder/SKILL.md · 430 lines

How it starts

The opening of the file, as written. The whole thing — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Position Ladder — Staged Entry & Cost-Basis Management

⚠️ Data Verification — Do This Before Any Analysis

Before running any analysis, always retrieve the latest market data for the ticker:

  1. Fetch current price — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data.
  2. Confirm key figures — recent earnings, revenue, key ratios (P/E, P/S, etc.) as applicable to this skill.
  3. State your data source — fill in the Data & Sources header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output.
  4. Flag stale data explicitly — if live data is unavailable, display this warning before proceeding:

⚠️ Live data unavailable. The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.

Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.


📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the Data & Accuracy page and the first thing result-validator looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>
  • Retrieval: model memory must be paired with Confidence: LOW — memory is a placeholder until confirmed against a primary source.
  • Mixed sources: list each with its own as-of date rather than blending them.
  • Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
  • Data the user pasted is reported as pasted by user; do not upgrade its confidence beyond what the user's own source supports.

Read the full file on GitHub · 430 lines

Changes

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.

  1. 11d ago Changed · +1 lines a317ed9670be
  2. 28d ago Changed · +18 lines c49615672b23
  3. 1mo ago First seen · 411 lines · 44 tokens per session scan A 8e7fe40de3d3

Subscribe to this mod's changes

position-ladder is a skill published in the GitHub repository yennanliu/InvestSkill (253 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 6,728 once invoked, about $0.0002 per session on Opus 5.5. 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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