Borrowing it
Nothing to install: this file belongs to tusharagg1/aifolimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tusharagg1/aifolimizer/master/.claude/skills/employer-stock/SKILL.mdgit clone --depth 1 https://github.com/tusharagg1/aifolimizerWrote 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/tusharagg1/aifolimizer/employer-stock)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/employer-stock"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/employer-stock/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/tusharagg1/aifolimizer/employer-stock"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/employer-stock.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 | $0.00098 | $0.01471 |
| Opus 5 | $0.00049 | $0.00736 |
| Sonnet 5 | $0.00020 | $0.00294 |
| Haiku 4.5 | $0.00010 | $0.00147 |
Grade A, and why
employer-stock 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.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Employer Stock Deep Dive (concentration · vesting · tax · opportunity cost)
Goal
Decide hold / trim-gradual / trim-now / sell on employer or equity-plan stock, treating it as a distinct risk bucket. The core question is long-term: single-employer concentration is undiversified human + financial capital - salary AND a chunk of net worth ride the same company. Default skepticism toward large employer-stock weights; the burden is on KEEPING it, not selling.
Why this is its own skill
get_profile only sees Wealthsimple accounts. Employer equity plans live
OUTSIDE WS - the system is blind to them. So this skill MUST ask the user for
the plan data it cannot fetch.
Decision Memory Protocol (load first, log after)
Before the analysis, load prior decisions on this employer ticker so the verdict stays consistent across sessions:
mcp__aifolimizer__get_ticker_decision_history(ticker=TICKER, max_decisions=5) +mcp__aifolimizer__get_ticker_reflection(symbol=TICKER, n=3) +mcp__aifolimizer__get_cross_ticker_lessons(max_lessons=3). If a prior decision exists and this run flips it, state explicitly WHY (new data / catalyst / price); never silently contradict a logged decision.
After output, log the verdict: call mcp__aifolimizer__log_recommendation (skill="employer-stock", ticker, action, conviction, rationale, target_pct, stop_pct).
Stage 0 - Gather what the system can't see (ASK)
Request from the user (don't guess):
- Ticker + shares held in the plan, current plan value
- Cost basis / acquisition type: RSU (vests as ordinary income), ESPP (often 15% discount, look-back), open-market, options
- Vesting schedule + unvested amount + vest dates
- Account/tax wrapper the shares sit in (registered? taxable? US plan w/ 401k?)
- Annual salary from the SAME employer (to size human-capital concentration)
- Any blackout windows / trading restrictions / sale-after-vest rules
Then pull the public side:
get_profile+get_portfolio- total invested + whether the same ticker is ALSO held in WS (double-counting the bet)get_fundamentals(ticker) - yield, payout, growth, beta, analyst targetget_dividend_calendar(symbols=[ticker]) - ex-div / pay timing for hold-vs-sellget_dcf_valuation(US ticker) - intrinsic-value anchor (most US large-caps qualify)get_positioning_signals(symbols=[ticker]) - is the Street crowded / contrarianget_news_headlines(ticker) - live catalyst check
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.
- 11d ago First seen · 104 lines · 98 tokens per session scan A 05ee2519580e
employer-stock is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 9d ago), licensed MIT. It adds 98 tokens to every session and 1,471 once invoked, about $0.0005 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 2026-08-31.
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