Claude Trading Skills is a collection of Claude Code workflows for individual investors who want structured market analysis, charting, economic-calendar review, screening, trade planning, journaling, and risk management. It is designed for people using long-term investing, ETFs, dividend stocks, and disciplined swing trading, and the catalogue entries package these workflows as skills, agents, commands, settings, and instructions.
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 tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitorgit clone --depth 1 https://github.com/tradermonty/claude-trading-skillsWrote 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/tradermonty/claude-trading-skills/kanchi-dividend-review-monitor)<a href="https://agentmods.dev/skills/tradermonty/claude-trading-skills/kanchi-dividend-review-monitor"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/kanchi-dividend-review-monitor/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/tradermonty/claude-trading-skills/kanchi-dividend-review-monitor"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/kanchi-dividend-review-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 108 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00081 | $0.01183 |
| Opus 5 | $0.00041 | $0.00592 |
| Sonnet 5 | $0.00016 | $0.00237 |
| Haiku 4.5 | $0.00008 | $0.00118 |
Grade A, and why
kanchi-dividend-review-monitor 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 13d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- kanchi-dividend-review-monitor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kanchi Dividend Review Monitor
Overview
Detect abnormal dividend-risk signals and route them into a human review queue. Treat automation as anomaly detection, not automated trade execution.
When to Use
Use this skill when the user needs:
- Daily/weekly/quarterly anomaly detection for dividend holdings.
- Forced review queueing for T1-T5 risk triggers.
- 8-K/governance keyword scans tied to portfolio tickers.
- Deterministic
OK/WARN/REVIEWoutput before manual decision making.
Prerequisites
Provide normalized input JSON that follows:
references/input-schema.md
If upstream data is unavailable, provide at least:
tickerinstrument_typedividend.latest_regulardividend.prior_regular
Non-Negotiable Rule
Never auto-sell based only on machine triggers.
Always create WARN or REVIEW evidence for human confirmation first.
State Machine
OK: no action.WARN: add to next check cycle and pause optional adds.REVIEW: immediate human review ticket + pause adds.
Use references/trigger-matrix.md for trigger thresholds and actions.
Flat-dividend cadence caveat
When T6 is driven only by freeze_flag / latest regular dividend equal to prior regular dividend, treat it as a WARN for cadence confirmation, not as proof of dividend deterioration. Many quarterly dividend payers repeat the same dividend for several quarters between annual raise cycles. In reports, phrase this as “confirm next dividend-growth cadence / pause optional adds until checked” and avoid implying a cut or broken thesis unless T1/T2/T3/T4/T5 evidence also supports escalation.
Monitoring Cadence
- Daily:
- T1 dividend cut/suspension.
- T4 SEC filing keyword scan (8-K oriented).
- Weekly:
- T3 proxy credit stress checks.
- Quarterly:
- T2 coverage deterioration and T5 structural decline scoring.
Workflow
1) Normalize input dataset
Collect per ticker fields in one JSON document:
- Dividend points (latest regular, prior regular, missing/zero flag).
- Coverage fields (FCF or FFO or NII, dividends paid, ratio history).
- Balance-sheet trend fields (net debt, interest coverage, buybacks/dividends).
- Filing text snippets (especially recent 8-K or equivalent alert text).
- Operations trend fields (revenue CAGR, margin trend, guidance trend).
What ships with it
8 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.
- agents/openai.yaml 130 B
- references/input-schema.md 1.6 KB
- references/review-ticket-template.md 581 B
- references/trigger-matrix.md 2.1 KB
- requirements.txt 70 B
- scripts/build_review_queue.py 17 KB runs code
- scripts/tests/conftest.py 153 B runs code
- scripts/tests/test_build_review_queue.py 8.9 KB runs code
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.
- 13d ago First seen · 130 lines · 81 tokens per session scan A 20da33d7dc9c
kanchi-dividend-review-monitor is a skill published in the GitHub repository tradermonty/claude-trading-skills (2,813 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 1,183 once invoked, about $0.0004 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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