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/guidance-tracker/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/guidance-tracker)<a href="https://agentmods.dev/skills/daloopa/investing/guidance-tracker"><img src="https://agentmods.dev/badge/skills/daloopa/investing/guidance-tracker.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.02224 |
| Opus 5 | $0.00008 | $0.01112 |
| Sonnet 5 | $0.00003 | $0.00445 |
| Haiku 4.5 | $0.00002 | $0.00222 |
Grade A, and why
guidance-tracker 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 8d 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:
- guidance-tracker — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Track management guidance accuracy 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.
Follow these steps:
1. Company Lookup
Look up the company by ticker using discover_companies. Capture:
company_idlatest_calendar_quarter— anchor for all period calculations below (see../data-access.mdSection 1.5)latest_fiscal_quarter- Firm name for report attribution (default: "Daloopa") — see
../data-access.mdSection 4.5
2. Discover Guidance Series
Search for series with keywords like "guidance", "outlook", "estimate", "forecast", "target" to find all available guidance metrics. Common guidance series include:
Financial guidance:
- Revenue guidance (quarterly and/or annual)
- EPS guidance
- Operating income / margin guidance
- EBITDA guidance
- Segment-level revenue guidance
- CapEx guidance
- Free Cash Flow guidance
Operational KPI guidance — many companies guide on KPIs, and tracking these beats/misses is often more informative than financial guidance:
- Subscriber / user count guidance (e.g., "we expect to add X million subscribers")
- Unit shipment guidance (e.g., "iPhone units", "deliveries")
- ARPU / ASP guidance
- Same-store sales guidance
- GMV / bookings guidance
- Net revenue retention guidance
- Store openings / closings guidance
- Production volume / capacity guidance
Search explicitly for KPI-specific guidance series using terms like "subscriber guidance", "unit guidance", "ARPU guidance", "same-store sales outlook", "deliveries forecast", "bookings target". These are separate from financial guidance and often reside in different series.
3. Pull Guidance Data
Calculate 8+ quarters backward from latest_calendar_quarter. Pull all discovered guidance series for those periods.
4. Pull Actual Results
For each guidance metric, pull the corresponding actual result series for the same periods.
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.
- 8d ago First seen · 148 lines · 15 tokens per session scan A b132c54fef7d
guidance-tracker is a skill published in the GitHub repository daloopa/investing (487 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 2,224 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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.