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 yennanliu/InvestSkill --skill learning-coachgit clone --depth 1 https://github.com/yennanliu/InvestSkillWrote 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/yennanliu/investskill/learning-coach)<a href="https://agentmods.dev/skills/yennanliu/investskill/learning-coach"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/learning-coach/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/yennanliu/investskill/learning-coach"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/learning-coach.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 | $0.00073 | $0.05588 |
| Opus 5.5 | $0.00029 | $0.02235 |
| Sonnet 5.5 | $0.00015 | $0.01118 |
| Haiku 4.5 | $0.00007 | $0.00559 |
Grade A, and why
learning-coach 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 4d 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Coach — Understand the Analysis You Just Got
⚠️ Data Verification — Do This Before Any Analysis
Before explaining anything, check the material you were handed:
- Do not re-fetch data — explain the figures as they appear in the pasted analysis, and flag any that look stale (as-of date older than 90 days), unsourced (no
Data & Sourcesheader), or internally inconsistent (a score that does not match its own signal band). Teaching from bad numbers teaches bad habits. - Confirm key figures — where the pasted analysis states a metric without its inputs (a P/E with no price or EPS), say so; do not invent the missing inputs to make the explanation tidier.
- State your data source — fill in the
Data & Sourcesheader (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output. - Flag stale data explicitly — if the pasted analysis carries a live-data warning or says
Retrieval: model memory, display this 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.
If the pasted analysis simply has no as-of date, do not assume it came from memory: write As of: unknown in the reproduced header, set Confidence to LOW, and warn that freshness cannot be verified — the figures may be current or stale, and the reader should find out which before acting.
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>
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.
- 4d ago Changed · +1 lines 3a88e492c8e9
- 21d ago First seen · 311 lines · 73 tokens per session scan A 0071cba30b11
learning-coach is a skill published in the GitHub repository yennanliu/InvestSkill (246 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 5,588 once invoked, about $0.0003 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 2026-09-25.
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