app-download-and-usage-data-for-consumer-companies SKILL.md skillA
app-download-and-usage-data-for-consumer-companies from himanshuj16/algo-trading-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add himanshuj16/algo-trading-skills/app-download-and-usage-data-for-consumer-companies@v1.3.0Pins v1.3.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- himanshuj16/algo-trading-skills
- Path
skills/app-download-and-usage-data-for-consumer-companies/SKILL.md- Versions recorded
- 5
- Latest version
- v1.3.0, committed Sep 4, 2026
- First version recorded
- Aug 7, 2026
- Size
- 11,100 bytes
- Repository stars
- 50
- License
- Apache-2.0
Use when turning mobile app engagement panels such as downloads and active users into a fundamental signal for consumer companies, with the panel normalisation and lag that makes the estimate point-in-time defensible.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
## When to Use Use this skill when processing mobile application alternative data (sourced from vendors such as Sensor Tower, data.ai, Apptopia, or Similarweb) to build fundamental engagement signals for consumer-facing public companies. While raw app downloads are often reported as "vanity metrics" by companies, the actual Daily Active Users (DAU) and Monthly Active Users (MAU) dictate long-term revenue viability. This engine calculates the "Stickiness Ratio" (DAU/MAU) and flags dangerous divergence between high marketing-driven downloads and failing user retention (the "Leaky Bucket" syndrome). Applicable scenarios: - Forecasting revenue durability for consumer-tech, gaming, ride-share, food-delivery, and streaming issuers ahead of earnings. - Constructing long/short alt-data baskets (overweight `is_world_class`, underweight `churn_risk_warning`). - Validating management commentary on "record downloads" against underlying engagement. ## When NOT to Use Do **not** use this skill when: …
Read the whole file at its exact version.
How to install
mdr add himanshuj16/algo-trading-skills/app-download-and-usage-data-for-consumer-companies@sha256:496f827e751b826cThe line at the top of this page pins the label v1.3.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_wkz2cpxoujhc5ueh)
1 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Aug 7, 2026 and Sep 4, 2026, about one every 7 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.3.0 latest | 2026-09-04 | 48bcc05 | 11,100 B | A | view · diff |
| v1.3.0 | 2026-09-03 | 8527a51 | 11,099 B | A | view · diff |
| v1.3.0 | 2026-09-03 | eb7108c | 10,976 B | A | view · diff |
| v1.2.0 | 2026-08-13 | 097e217 | 6,809 B | A | view · diff |
| v1.1.0 | 2026-08-07 | 89d1436 | 2,394 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (11100 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
himanshuj16/algo-trading-skills · 50 stars · license Apache-2.0 · pushed 2026-09-14 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_wkz2cpxoujhc5ueh GET https://markdownregistry.com/api/v1/resolve?ref=himanshuj16/algo-trading-skills/app-download-and-usage-data-for-consumer-companies GET https://markdownregistry.com/api/v1/blob/496f827e751b826c53f857cc0abfd677dfb979a77b5031c1d634f8245a0e71ad
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.