fit-hidden-markov-model SKILL.md skillA
fit-hidden-markov-model from pjt222/agent-almanac is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add pjt222/agent-almanac/fit-hidden-markov-model@v1.0Pins v1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- pjt222/agent-almanac
- Path
i18n/caveman-lite/skills/fit-hidden-markov-model/SKILL.md- Versions recorded
- 4
- Latest version
- v1.0, committed Aug 17, 2026
- First version recorded
- Apr 19, 2026
- Size
- 13,863 bytes
- Repository stars
- 43
- License
- MIT
Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.
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
# Fit Hidden Markov Model Fit a hidden Markov model (HMM) to sequential observation data using the Baum-Welch expectation-maximization algorithm, decode the most likely hidden state sequence via Viterbi, and select the optimal number of hidden states through information criteria. ## When to Use - You observe a sequence of emissions but the underlying generative states are not directly observable - You suspect your data is generated by a system that switches between a finite number of regimes - You need to segment a time series into latent phases (e.g., market regimes, speech phonemes, biological sequence annotation) - You want to compute the probability of an observed sequence under a generative model - You need the most likely sequence of hidden states given observations (decoding) - You are comparing models with different numbers of hidden states for the best complexity-fit trade-off ## Inputs ### Required | Input | Type | Description | |---|---|---| | `observations` | sequence/matrix | Observed data sequence (univariate or multivariate) | | `n_hidden_states` | integer | Number of hidden states to fit (or a range for model selection) | …
Read the whole file at its exact version.
How to install
mdr add pjt222/agent-almanac/fit-hidden-markov-model@sha256:3e289a584d4d074dThe line at the top of this page pins the label v1.0, 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_ozcbfmahxkvu3yhf)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 4 versions of this file, between Apr 19, 2026 and Aug 17, 2026, about one every 40 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0 latest | 2026-08-17 | a784899 | 13,863 B | A | view · diff |
| v1.0 | 2026-06-05 | 75ded7a | 13,834 B | A | view · diff |
| v1.0 | 2026-04-24 | 0b91b04 | 13,874 B | A | view · diff |
| v1.0 | 2026-04-19 | e334ebd | 13,929 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 (13863 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
pjt222/agent-almanac · 43 stars · license MIT · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ozcbfmahxkvu3yhf GET https://markdownregistry.com/api/v1/resolve?ref=pjt222/agent-almanac/fit-hidden-markov-model GET https://markdownregistry.com/api/v1/blob/3e289a584d4d074d433999cc536bef788729c1b54bea89064b597f173b874012
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.