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 agentmods add skills/daloopa/investing/updatenpx skills add daloopa/investing --skill updategit clone --depth 1 https://github.com/daloopa/investingWrote 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/daloopa/investing/update)<a href="https://agentmods.dev/skills/daloopa/investing/update"><img src="https://agentmods.dev/badge/skills/daloopa/investing/update.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.01116 |
| Opus 5 | $0.00006 | $0.00558 |
| Sonnet 5 | $0.00002 | $0.00223 |
| Haiku 4.5 | $0.00001 | $0.00112 |
Grade A, and why
update 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 3d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Update existing coverage for the company specified by the user: $ARGUMENTS
Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.
This skill refreshes existing deliverables with the latest quarterly data, highlights what changed, and re-renders both outputs.
Phase 1 — Load Existing Context
Check for existing context files in reports/.tmp/:
reports/.tmp/{TICKER}_context.json(research note context)reports/.tmp/{TICKER}_model_context.json(model context)
If neither exists, tell the user: "No existing coverage found for {TICKER}. Run /initiate {TICKER} first to create initial coverage." and stop.
Read the existing context(s) to understand what periods and data were previously gathered.
Phase 2 — Identify New Data
Look up the company using discover_companies. Capture company_id, latest_calendar_quarter (anchor for all period calculations — see ../data-access.md Section 1.5), and latest_fiscal_quarter. Note the firm name for report attribution (default: "Daloopa") — see ../data-access.md Section 4.5.
Compare to the periods in existing context. Determine which new quarters need to be pulled.
If no new quarters are available, tell the user: "Coverage is already current through {latest_period}. No new data to update." and stop.
Phase 3 — Pull Fresh Data
Pull data for ALL periods (not just new ones) to ensure consistency:
- Full Income Statement, Balance Sheet, Cash Flow
- Segments, KPIs, Guidance
- Share count, buyback activity
This refreshes the entire dataset, catching any Daloopa revisions to prior quarters.
Phase 4 — Market Data Refresh
Get current prices, trading multiples, and risk-free rate (see ../data-access.md Section 2).
Also refresh peer multiples if comps data exists in context.
Phase 5 — Re-run Projections
With updated historical data, re-run projections. If a projection engine is available (see ../data-access.md Section 5), use it. Otherwise project manually.
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.
- 3d ago First seen · 105 lines · 12 tokens per session scan A 7a29449e6c41
update is a skill published in the GitHub repository daloopa/investing (486 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 1,116 once invoked, about $0.0001 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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