comps-analysis skillA
comps-analysis is agent-read markdown (skill) from ginlix-ai/langalpha: Comparable company analysis: peer set, operating metrics, valuation multiples, statistics and an implied value. Triggers on comps, trading comparables, how does it trade against peers, peer benchmarking, what multiple should it get..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Comparable Company Analysis A comps table is arithmetic anyone can do and data discipline almost nobody does. The multiples are the easy half: the work is in which companies belong in the set, whether each number measures the same thing over the same period, and how old it is. The steps below build the table in the order those questions have to be answered. Evidence labels, source tiers, staleness, the readiness posture and the intake limits: `.agents/skills/research-conventions/SKILL.md`, read before the first figure enters the table. Period labels, per-company LTM windows, NTM as four quarterly estimates, and one common base date for every comparative return are binding here: `.agents/skills/research-conventions/references/market-data-rules.md`, read before the first pull. Step 3 adds no rule of its own and says only which of those period cases a comps table selects. ## Step 1: Frame the table Four questions decide what gets built. How many to ask, in what shape, and what to do when no answer comes back: `.agents/skills/research-conventions/references/intake.md`. 1. **Format**: their template, or ours …
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How to install
mdr add ginlix-ai/langalpha/comps-analysis@git:20260915.99ad826mdr add ginlix-ai/langalpha/comps-analysis@sha256:05fef10b744cb233Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_4q4k4bib6fqrazmw)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260915.99ad826 latest | 2026-09-15 | 99ad826 | 25,659 B | A | view · diff |
| git:20260826.8da358a | 2026-08-26 | 8da358a | 24,463 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 (25659 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
ginlix-ai/langalpha · 1,774 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_4q4k4bib6fqrazmw GET https://markdownregistry.com/api/v1/resolve?ref=ginlix-ai/langalpha/comps-analysis GET https://markdownregistry.com/api/v1/blob/05fef10b744cb2339b32ee6515d8cf2ae0e2b81a09ab5c008268f3c7f1573225
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