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 mphinance/alpha-skills --skill kanchi-dividend-review-monitorgit clone --depth 1 https://github.com/mphinance/alpha-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/mphinance/alpha-skills/kanchi-dividend-review-monitor)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/kanchi-dividend-review-monitor"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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/mphinance/alpha-skills/kanchi-dividend-review-monitor"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/kanchi-dividend-review-monitor.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 | $0.00081 | $0.00943 |
| Opus 5.5 | $0.00032 | $0.00377 |
| Sonnet 5.5 | $0.00016 | $0.00189 |
| Haiku 4.5 | $0.00008 | $0.00094 |
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 1mo 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 — 125 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.
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).
Use references/input-schema.md for field definitions
and sample payload.
2) Run the rule engine
Run:
python3 skills/kanchi-dividend-review-monitor/scripts/build_review_queue.py \
--input /path/to/monitor_input.json \
--output-dir reports/
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
- README.md 4.0 KB
- references/input-schema.md 1.3 KB
- references/review-ticket-template.md 581 B
- references/trigger-matrix.md 1.6 KB
- scripts/build_review_queue.py 15 KB runs code
- scripts/tests/conftest.py 153 B runs code
- scripts/tests/test_build_review_queue.py 7.5 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.
- 1mo ago First seen · 125 lines · 81 tokens per session scan A 50ed1b5a1cc6
kanchi-dividend-review-monitor is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 81 tokens to every session and 943 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 2026-09-14.
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