Agent skill

Omh Jit Learn

by rlaope in rlaope/oh-my-hermes

[omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief…

MITAuto-check passedEducation

Install Omh Jit Learn

skills CLI
$ npx skills add rlaope/oh-my-hermes --skill omh-jit-learn -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rlaope/oh-my-hermes omh-jit-learn --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/omh-jit-learn .claude/skills/omh-jit-learn && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
omh-jit-learn
GitHub stars
3.2k
Token cost
~3.2k tokens
SKILL.md length
1,636 words
Files
1
Skills in repo
143
Repo updated
First seen
Licence
MIT

At a glance

[omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief…

  • Works in 6 steps: Review only the current conversation and… → Always ask at least one confirmation… → Reuse the deep-interview early-stop… → …
  • The user says: jit-learn
  • SKILL.md covers Why This Exists, Do Not Use When, Examples and Completion Checklist, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Omh Jit Learn is an agent skill from rlaope/oh-my-hermes. [omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief without popularity ranking. Use when the user says: jit-learn, learn next, learn now, blocker-specific learning target, highest-leverage learning target, immediate learning payoff, immediately applicable learning brief, source-backed learning brief.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education. The repository describes itself as: All in one plugin for Hermes Agent ⚚ the coding intelligence, a long-term memory system and model optimized workflow packages. The licence is MIT.

When your agent uses it

  • The user says: jit-learn
  • Blocker-specific learning target
  • Highest-leverage learning target
  • Immediate learning payoff

Example prompts

  • “/omh-jit-learn”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Review only the current conversation and reviewed or explicitly approved OMH context. Never claim access to hidden Hermes memory and never…
  2. Always ask at least one confirmation question before research, including when the request appears complete. Ask exactly one question per…
  3. Reuse the deep-interview early-stop discipline and its shared ceiling of 6 rounds. After the mandatory first answer, stop asking as soon…
  4. Confirm one target before research in exactly this semantic form: Learn X now so I can do/decide Y in context Z by T. Here T means the…
  5. Scope research around that target. Prefer primary, institutional, and credible practitioner sources; check authority, currency…
  6. Prepare the Markdown brief, then stop. Do not buy, download, enroll, subscribe, contact creators, bypass access controls, write…

What it can do on your machine

Read from SKILL.md and the folder at commit 41de9dc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Omh Jit Learn loads about 3.2k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 1,636 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~114
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rlaope/oh-my-hermes at commit 41de9dc, republished under its MIT licence (© rlaope). 1,636 words, ~3,232 tokens.

Download SKILL.mdSave it as .claude/skills/omh-jit-learn/SKILL.md (or your agent's skills folder).
name
omh-jit-learn
description
[omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief without popularity ranking. Use when the user says: jit-learn, learn next, learn now, blocker-specific learning target, highest-leverage learning target, immediate learning payoff, immediately applicable learning brief, source-backed learning brief.

Jit Learn

This is a Hermes-native jit-learn workflow skill.

Why This Exists

jit-learn exists to choose what is worth learning for the user's present problem and convert credible sources into an immediate application path, instead of returning a generic self-help shelf or a popularity list.

Do Not Use When

  • The user asks OMH to learn from workflow outcomes, missed routes, or evaluation traces; use workflow-learning.
  • The learning goal is already chosen and the user wants a multi-week syllabus, instructional sequence, or assessment plan; use curriculum-design.
  • The user supplied a paper, PDF, arXiv entry, or excerpt and wants it explained; use paper-learning.
  • The requested output is a typed source candidate inventory or acquisition status rather than a fitted learning brief; use source-finder.
  • The research question and target are already scoped and the user wants current facts, citations, or source synthesis rather than choosing what to learn; use research.

Examples

Good example:

  • Prompt: What should I learn next to solve my current onboarding blocker? Recommend books, podcasts, creators, and courses I can apply this week.
  • Expected behavior: Ask one confirmation question, confirm the immediate target, then prepare a source-backed four-section learning brief ranked by fit and time-to-first-value.
  • Why: The user needs target selection and immediate transfer, not a generic curriculum or popularity-ranked resource list.

Bad example:

  • Prompt: Design a six-week Python syllabus with weekly assessments.
  • Expected behavior: Route to curriculum-design because the target is already chosen and the requested output is a sequenced curriculum.
  • Why: Just-in-time target selection should not displace an explicit curriculum-design request.

Completion Checklist

  • At least one confirmation question was answered, no turn contained more than one question, and the shared interview ceiling was respected.
  • Urgency/trigger, current level, application window, and the target statement are explicit before research.
  • Every admitted recommendation is source-gated and popularity signals did not influence admission or rank.
  • Books, Podcasts, Creators, and Courses are present with complete fields or an honest empty-section reason.
  • Competing targets, filtered-out defaults, unresolved gaps, and one starting action are visible.
  • The final status says the brief is prepared and does not claim consumption, learning, application, progress, or blocker resolution.

Recovery Notes

  • If a required readiness dimension remains unclear, ask the one answer that most changes the target while the shared round budget remains.
  • If the shared interview ceiling is reached, proceed with explicit assumptions and gaps rather than asking another question.
  • If sources or links cannot be checked, leave the affected section empty with the retrieval reason instead of adding a generic recommendation.
  • If the target becomes a syllabus, supplied-paper explanation, source inventory, already-scoped research question, or OMH self-improvement request, preserve the sibling boundary and route accordingly.

Workflow Lane

  • Current lane: Research and company ops (product-docs, source-finder, web-research, research, model-optimization, inference-serving, model-finetuning, research-brief, +20 more) - research, signals, ops, and briefings.
  • If intent belongs to another lane, hand back to oh-my-hermes or name the adjacent workflow.
  • Shared product, routing, compatibility, and evidence rules: omh-routing/references/skill-common-rail.md.

Use When

Use when selecting the highest-leverage immediate learning target for an active blocker before preparing a source-backed Markdown brief for direct application.

Strong routing signals: `jit-learn`, `learn next`, `learn now`, `blocker-specific learning target`, `highest-leverage learning target`, `immediate learning payoff`, `immediately applicable learning brief`, `source-backed learning brief`, `학습 주제`, `도움 되는 학습 주제`, `당장 적용할 학습 목표`, `책 팟캐스트 크리에이터 강의 학습 브리프`

Catalog Metadata

Category: research Phase: learning-target Hermes role: researcher Quality tier: source-gated Reasoning demand: standard

Quality bar:

  • Resolve urgency/trigger, current level, and application window with one question per turn, while stopping early once all three are clear after the mandatory first answer.
  • Confirm one target in the form Learn X now so I can do/decide Y in context Z by T. before source research.
  • Prefer primary, institutional, and credible practitioner sources; rank by specific fit, authority, currency, time-to-first-value, and direct transfer rather than popularity.
  • Keep Books, Podcasts, Creators, and Courses visible even when no candidate passes, and explain every empty section instead of padding it.
  • For each admitted resource, state title, format, creator/publisher, link, source class, time to first value, specific fit, first application, and applicable link/access/currency caveats.
  • Close with competing targets considered, filtered-out defaults, unresolved gaps, and exactly one recommended starting action.

Handoff policy:

Keep reviewed-context interpretation, the bounded one-question-at-a-time interview, target selection, source research, and Markdown brief preparation in Hermes. Do not create a learner profile, take an external action, or claim that a recommendation was consumed, learned, applied, or resolved the blocker.

Required inputs:

  • reviewed context
  • urgency
  • current level
  • application window
  • time/format constraints

Expected outputs:

  • confirmed target statement: Learn X now so I can do/decide Y in context Z by T.
  • source-backed Markdown learning brief
  • Books section, including an explicit no-qualifying-candidate reason when empty
  • Podcasts section, including an explicit no-qualifying-candidate reason when empty
  • Creators section, including an explicit no-qualifying-candidate reason when empty
  • Courses section, including an explicit no-qualifying-candidate reason when empty
  • for every recommendation: title, format, creator/publisher, link, source class, time to first value, specific fit now, first application, and caveats
  • competing learning targets, filtered-out defaults, unresolved gaps, and one recommended next action

Artifact expectations:

  • prepared Markdown learning brief with observed source links and explicit retrieval gaps when a wrapper captures it

Safety rules:

  • Always ask at least one confirmation question before research, exactly one question per turn, even when the initial request appears complete.
  • Use the shared deep-interview ceiling of 6 rounds and its early-stop discipline; do not create a second interview budget.
  • Use only the current conversation and reviewed or explicitly approved OMH context; never claim hidden Hermes memory or create a persistent learner profile.
  • Admit recommendations only from primary, institutional, or credible practitioner evidence whose authority, currency, availability, and link can be checked; report retrieval gaps instead of inventing support.
  • Never use bestseller status, ratings, follower counts, charts, generic popularity, or unsupported reputation as admission or ranking evidence.
  • Do not purchase, download, enroll, subscribe, contact a creator, bypass a paywall, write to an external system, or imply any external action occurred.
  • A prepared brief is not evidence that the user consumed a resource, learned, made progress, applied the advice, or resolved the original blocker.
Show full SKILL.md (630 more words)Show less

Just-in-Time Learning Protocol

  1. Review only the current conversation and reviewed or explicitly approved OMH context. Never claim access to hidden Hermes memory and never create a learner profile.
  2. Always ask at least one confirmation question before research, including when the request appears complete. Ask exactly one question per turn. Resolve three readiness dimensions: urgency/trigger (why now), current level (what the user already knows or can do), and application window (where and by when this will be used), plus only practical constraints that change the recommendation. Record this evidence step as confirmation_asked.
  3. Reuse the deep-interview early-stop discipline and its shared ceiling of 6 rounds. After the mandatory first answer, stop asking as soon as the three readiness dimensions are clear. If the ceiling is reached, state assumptions and gaps instead of asking again.
  4. Confirm one target before research in exactly this semantic form: Learn X now so I can do/decide Y in context Z by T. Here T means the application deadline. When the initial request already supplies all readiness dimensions, use the mandatory first question to confirm this target so research can begin after one answer.
  5. Scope research around that target. Prefer primary, institutional, and credible practitioner sources; check authority, currency, availability, and links. Admit and rank by specific fit, authority, currency, time-to-first-value, and direct transfer. Never admit or rank from bestseller status, ratings, followers, charts, generic popularity, or unsupported reputation.
  6. Prepare the Markdown brief, then stop. Do not buy, download, enroll, subscribe, contact creators, bypass access controls, write externally, or imply those actions happened.

Learning Brief Contract

Start with the confirmed target statement and a short source-boundary note. Then render all four headings, even when empty:

  • ## Books
  • ## Podcasts
  • ## Creators
  • ## Courses

Under every heading, list only candidates that passed the source gate. If none passed, say why - for example, insufficient authority, stale or unavailable evidence, poor immediate fit, or an unresolved retrieval gap - rather than padding the section.

For every recommendation include:

  • Title
  • Format
  • Creator/Publisher
  • Link
  • Source class - primary, institutional, or credible practitioner
  • Time to first value
  • Why it fits - why it fits this user, target, level, and application window
  • First immediate application - the first concrete use in the user's present context
  • Link/currency caveat - link, availability, access, or currency limits when applicable

Close with ## Competing Targets Considered, ## Filtered Out, ## Gaps, and ## Next Action. Name the competing learning targets, generic defaults or resources rejected and why, unresolved evidence/context gaps, and exactly one recommended starting action.

The terminal state is learning_brief_prepared: the brief is prepared, not observed learning. Preparation does not prove source consumption, learning, progress, application, effectiveness, or resolution of the original blocker.

Runtime Evidence

Preferred harness for this skill: jit-learn.

sh
omh runtime record --skill jit-learn --harness jit-learn --status started

Record observed delegation results; otherwise return not_available or not_observed. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence.

  • Treat wrapper memory/context summaries as advisory local context, not proof of opaque Hermes memory reads or changes. Preserve workflow intent and stop conditions; verify before claiming completion. Reply in the user's own words and the host's own voice: its SOUL.md persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a stop condition or a decision the user owns ends the turn, offer the next action as a question rather than declaring what will not be done.

Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.

Shared product, compatibility, topology, memory, harness, and execution rules: omh-routing/references/skill-common-rail.md. Load it when applicable; otherwise name an unavailable capability.

© rlaope, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/omh-jit-learn of rlaope/oh-my-hermes.

Open the folder on GitHubat commit 41de9dc

Compare with similar skills

Omh Jit Learn next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Omh Jit Learn compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Omh Jit Learn this skillrlaope/oh-my-hermes3.2k—~3.2kAutomated safety check: PassMIT
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AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch66k—~2kAutomated safety check: PassMIT
Deep Reading Analystginobefun/deep-reading-analyst-skill3534 repos~3.6kAutomated safety check: PassMIT
OpenMAIC Setup and ExtensionTHU-MAIC/OpenMAIC40k—~1.7kAutomated safety check: NotesMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone

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Categories

Questions about Omh Jit Learn

What does Omh Jit Learn do?

[omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief…. Omh Jit Learn is an agent skill from rlaope/oh-my-hermes. [omh] Blocked on a project by a knowledge gap: just-in-time learning workflow: select and confirm an immediate learning target, research credible sources, and prepare an application-first brief without popularity ranking.

When should I use Omh Jit Learn?

Omh Jit Learn fits situations like: the user says: jit-learn; blocker-specific learning target; highest-leverage learning target; immediate learning payoff.

How do I install Omh Jit Learn in Claude Code?

Run `npx skills add rlaope/oh-my-hermes --skill omh-jit-learn -a claude-code`. Or copy the skill folder (skills/omh-jit-learn in rlaope/oh-my-hermes) into .claude/skills/omh-jit-learn in your project. Claude Code loads it when a task matches its description.

How do I install Omh Jit Learn in Codex?

Run `npx skills add rlaope/oh-my-hermes --skill omh-jit-learn -a codex`. Or copy the skill folder (skills/omh-jit-learn in rlaope/oh-my-hermes) into .agents/skills/omh-jit-learn in your project. Codex loads it when a task matches its description.

Can I use Omh Jit Learn in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rlaope/oh-my-hermes --skill omh-jit-learn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/omh-jit-learn, .gemini/skills/omh-jit-learn, .github/skills/omh-jit-learn and .opencode/skills/omh-jit-learn in your project.

What does Omh Jit Learn need to run?

SKILL.md names no scripts, command-line tools or credentials: Omh Jit Learn is instructions for the agent only. Our summary lists: Python 3.

Does Omh Jit Learn access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Omh Jit Learn safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Omh Jit Learn use?

Omh Jit Learn is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Omh Jit Learn use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Omh Jit Learn?

Skills that share tags, products or a category with Omh Jit Learn: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 66k stars), Deep Reading Analyst (ginobefun/deep-reading-analyst-skill, 353 stars) and OpenMAIC Setup and Extension (THU-MAIC/OpenMAIC, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Omh Jit Learn?

rlaope (a GitHub user) maintains it in rlaope/oh-my-hermes, which has 3,233 GitHub stars. The repository holds 143 skills in this directory. The repository was last updated on October 8, 2026.

Source: rlaope/oh-my-hermes on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.