Borrowing it
Nothing to install: this file belongs to daloopa/investing. 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/daloopa/investing/main/.claude/skills/update/SKILL.mdgit 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/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/daloopa/investing/update"><img src="https://agentmods.dev/badge/skills/daloopa/investing/update.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Memory Poisoning · line 71 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 76 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00012 | $0.01116 |
| Opus 5.5 | $0.00005 | $0.00446 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo ago First seen · 105 lines · 12 tokens per session scan A 7a29449e6c41
update is a skill published in the GitHub repository daloopa/investing (488 stars, last pushed 8d ago), licensed Apache-2.0. It adds 12 tokens to every session and 1,116 once invoked, about $0.0000 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 2026-08-30.
update compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| updatedaloopa/investing | Skill | 488 | 8d ago | Apache-2.0 | A | 12 | Claude Code |
| dcf-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 14 | Claude Code, Codex |
| comps-analysisNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 13 | Claude Code, Codex |
| lbo-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 19 | Claude Code, Codex |
| lbo-modelanthropics/financial-services | Skill | 41k | 9d ago | Apache-2.0 | A | 57 | Claude Code |
| 3-statement-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 18 | Claude Code, Codex |
| merger-modelNousResearch/hermes-agent | Skill | 261k | today | MIT | A | 18 | Claude Code, Codex |