1Password에 저장한 비밀번호와 API 키를 명령과 설정 템플릿에 사용합니다. CLI 설치와 인증부터 비밀 값 조회 및 환경 변수 전달까지 안내합니다.
- 1Password CLI 설치와 설정
- 저장된 API 키로 명령 실행
- 설정 템플릿에 비밀 값 삽입
Hermes Agent 저장소에서 배포할 스킬을 공통 규약에 따라 만들고 수정합니다. 저장 위치와 SKILL.md 구성부터 테스트, 관련 문서 갱신까지 다룹니다.
Author in-repo SKILL.md files: frontmatter and structure.
Hermes Agent 저장소에서 배포하는 스킬을 공통 작성 규약에 따라 만들고 수정합니다. 기본 포함 여부와 선택 설치 여부를 판단하고, 저장할 분류와 SKILL.md 앞부분의 YAML 설정, 본문 구성을 차례로 정리합니다. 지원 운영체제와 관련 스킬 확인, 테스트, 문서 생성, PR 작성 절차도 다룹니다.
재사용할 작업 절차를 Hermes Agent에 추가하거나 기존 스킬을 수정해 검토를 요청할 때 적합합니다. 설명 길이, 작성자 표기, 완료 여부를 확인할 수 있는 절차처럼 놓치기 쉬운 규약을 점검할 수 있습니다.
SKILL.md를 작성 규약에 따라 확인하고 테스트와 문서도 갱신해 주세요.”대상은 skills/와 optional-skills/ 아래이며, 개인용 ~/.hermes/skills/와는 생성 방법이 다릅니다. 규약의 기준은 대상 저장소의 AGENTS.md입니다. 검증에는 pytest 등을 사용합니다. 새 스킬은 생성한 세션의 로드된 스킬 목록에 반영되지 않습니다.
이 소개문은 공개된 SKILL.md를 바탕으로 AI(OpenAI Codex)가 작성했습니다. 정확한 사양은 아래 원문을 확인하세요.
AI가 SKILL.md의 내용만을 근거로 4가지 관점에서 판정했습니다. 스타 수나 인기도는 판정에 넣지 않았습니다.
총점은 각 항목을 A=25점, B=15점, C=5점으로 더한 값입니다. 2026년 10월 20일 기준으로 AI(OpenAI Codex)가 판정했습니다. 참고용이므로 설치 전에 원문도 확인해 주세요.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
There are two places a SKILL.md can live:
~/.hermes/skills/<maybe-category>/<name>/SKILL.md — personal, not shared. Created via skill_manage(action='create').skills/<category>/<name>/SKILL.md or optional-skills/<category>/<name>/SKILL.md inside the hermes-agent repo — committed, shipped with the package. Use write_file + git add. skill_manage(action='create') does NOT target this tree.In-repo skills must meet the repo's hardline authoring standards (see AGENTS.md, "Skill authoring standards (HARDLINE)" — that section is the source of truth; this skill is the operational walkthrough). Reviewers reject PRs that violate them, so meeting them up front is cheaper than a salvage pass later.
skills/ or optional-skills/ (use patch for small edits, write_file for rewrites; skill_manage still works for patch on in-repo skills, but not for create)~/.hermes/skills/ (just use skill_manage)skills/<category>/) — daily-driver behavior, broadly useful across many user types, low footprint. Hard bar: you can say "a user will load this in 5+ sessions per month" with a straight face.optional-skills/<category>/) — niche, vertical-specific (blockchain, gaming, finance, one app), recurring-job/task skills, or anything heavy. Installed via hermes skills install official/<category>/<skill>.When in doubt, optional. Promoting later is easy; demoting is churn. "Would be useful to anyone who ever needs this" is an optional-tier argument, not a bundled one.
Pick the category by what the tool IS, not what it feels like (an AI-agent CLI goes in autonomous-ai-agents/ even if it "feels productivity"). Confirm existing categories with search_files(pattern='*', target='files', path='skills') and don't invent new top-level categories casually.
No router / index / hub skills. A skill whose core content is a routing table pointing at sibling skills adds an indirection hop and duplicates the siblings' own When to Use triggers. If the skill would be empty without "load skill X instead" pointers, don't write it — the catalog and each sibling's triggers already do that job.
Validator source of truth: tools/skill_manager_tool.py::_validate_frontmatter. Validator hard requirements:
--- as the first bytes (no leading blank line).\n---\n before the body.name field present.description field present (validator ceiling 1024 chars — but see the repo hardline below, which is much stricter).---.Repo-standard shape (all fields expected, even where the validator doesn't enforce them):
---
name: my-skill-name # lowercase, hyphens, ≤64 chars (MAX_NAME_LENGTH)
description: Concise capability statement, under sixty chars.
version: 0.1.0 # semver; new skills start at 0.1.0
author: Real Name (github-handle), Hermes Agent
license: MIT
platforms: [linux, macos, windows] # audit, don't guess — see Platform Gating
metadata:
hermes:
tags: [Short, Descriptive, Tags]
related_skills: [other-in-repo-skill]
---
description rules (HARDLINE — the validator's 1024 is NOT the standard):, wrap it in double quotes or YAML parses it as a mapping and the docs generator crashes. Quotes don't count toward the 60.Good: Track named companies for material news with cited digests.
Bad: Use when a user asks to monitor named competitors or companies for product launches, pricing changes, funding, ... (240 chars — rejected in review)
author rulesBen Barclay (benbarclay), Hermes Agent.author: Hermes Agent alone for contributed skills — credit the human, not the tool, even (especially) when an agent drafted the text.Teknium (teknium1), Hermes Agent.related_skills rules~/.hermes/skills/.search_files(pattern='<name>', target='files', path='skills') (and optional-skills/).platforms: gates loading by host OS. Set it from what the skill's prose and scripts actually invoke:
| Skill uses only… | platforms: |
|---|---|
| Hermes tools + stdlib Python + cross-platform CLIs | [linux, macos, windows] |
bash pipelines, grep/awk/sed chains, heredocs | [linux, macos] |
osascript, defaults, pmset | [macos] |
apt/systemctl//proc | [linux] |
POSIX-only signals to search for in scripts/: fcntl, termios, pty, os.fork, os.killpg, signal.SIGKILL, os.kill(pid, 0) liveness checks, hardcoded /tmp /proc /etc. Default posture: fix cross-platform first (tempfile.gettempdir(), pathlib.Path, psutil.pid_exists); gate narrower only when the dependency is genuinely platform-bound, and say why in ## Pitfalls.
MAX_SKILL_CONTENT_CHARS), but target ~100 lines for a simple skill, ~200 for a complex one. Peer skills sit at 8-14k chars.references/*.md, templates/, or scripts/ — pointed to from SKILL.md, not inlined.scripts/ and reference it by path.# <Skill> Skill
2-3 sentence intro: what it does, what it doesn't do, dependency stance.
## When to Use — bulleted triggers (+ "Don't use for:" counter-triggers)
## Prerequisites — exact env vars, installs, API key sourcing
## How to Run — canonical invocation through the `terminal` tool
## Quick Reference — flat command list, no narration
## Procedure — numbered steps, each with a checkable completion criterion
## Pitfalls — known limits, things that look broken but aren't
## Verification — how to prove the skill worked
Not every section applies to every skill (a pure-procedure task skill may have no Quick Reference), but When to Use + actionable body + Pitfalls + Verification are the minimum. Cut marketing intros, "Setup Check" no-ops, and re-explanations of env vars already in Prerequisites.
When the skill needs a capability, name the proper Hermes tool in backticks: terminal, read_file, write_file, patch, search_files, web_search, web_extract, browser_navigate, vision_analyze, delegate_task, cronjob. Do NOT name shell utilities the agent already has wrapped (grep → search_files, cat → read_file, sed/awk → patch, find/ls → search_files target='files'). A CLI-wrapper skill should frame invocations as terminal(command="{tool} ...", timeout=...) — bare shell prose ("run foo --version") is a review-blocking non-conformance. If the skill depends on an MCP server, name it and document setup in Prerequisites.
Write repo-relative paths (skills/..., tools/skill_manager_tool.py). A /home/<you>/... path baked into a committed skill breaks for every other user and is an instant review flag.
A skill exists to make the agent's process more predictable — the agent reliably follows the same useful discipline.
tests/skills/test_<skill>_skill.py — stdlib + pytest + unittest.mock only, no live network. Run via scripts/run_tests.sh tests/skills/test_<skill>_skill.py -q. (The generic tests/tools/test_skill_manager_tool.py passing proves nothing about YOUR skill.)python website/scripts/generate-skill-docs.py, then apply scope discipline — the generator rewrites EVERY auto-gen page. git checkout -- everything that isn't yours; the final diff must show only your SKILL.md, your one per-skill docs page, a one-line catalog row, and a one-line website/sidebars.ts insertion (verify with search_files(pattern='<your-slug>', path='website/sidebars.ts') — exactly one hit, or the page is an orphan)..env.example (only if the skill needs new env vars): one clearly delimited commented block; touch nothing else in the file.search_files(target='files') and read 2-3 peer SKILL.md files to match tone and structure. Prefer extending an existing skill over creating a narrow sibling.write_file to skills/<category>/<name>/SKILL.md (or optional-skills/...).import re, pathlib
from ruamel.yaml import YAML
content = pathlib.Path("skills/<category>/<name>/SKILL.md").read_text()
assert content.startswith("---")
m = re.search(r'\n---\s*\n', content[3:])
fm = YAML(typ="safe").load(content[3:m.start()+3])
assert "name" in fm and "description" in fm
assert len(fm["description"]) <= 60, f"description {len(fm['description'])} chars — hardline is 60"
assert fm["description"].endswith(".")
assert "platforms" in fm
assert len(content) <= 100_000
Also verify every related_skills entry exists in-repo.skill_view / skills_list will not see the new skill until a new session. This is expected, not a bug.skill_manage(action='patch', ...) works on in-repo skills, as does patch.write_file the whole SKILL.md.write_file to references/, templates/, or scripts/ under the skill dir.skill_manage(action='create') for an in-repo skill. It writes to ~/.hermes/skills/, not the repo tree. Use write_file.platforms:, author format, tests, or docs — review does.author: Hermes Agent on a contributed skill. Credit the human first.---. Validation fails on any leading blank line or BOM.related_skills pointing at skills that don't exist in-repo (user-local, planned, or in a sibling PR).optional-skills/)skills/<category>/<name>/SKILL.md or optional-skills/<category>/<name>/SKILL.md---, closes with \n---\nname, description, version, author, license, platforms, metadata.hermes.{tags, related_skills} all presentauthor credits the human contributor firstplatforms: audited against actual prose/scripts, not copied from a siblingrelated_skills entry resolves in-repotests/skills/test_<skill>_skill.py pass under scripts/run_tests.shgit add + commit on the intended branch; PR opened아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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1Password에 저장한 비밀번호와 API 키를 명령과 설정 템플릿에 사용합니다. CLI 설치와 인증부터 비밀 값 조회 및 환경 변수 전달까지 안내합니다.
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기존 PyTorch 학습 코드를 적은 수정으로 여러 GPU나 컴퓨터에 맞게 구성합니다. 혼합 정밀도, 메모리 절약 설정과 학습 실행 방법을 안내합니다.
Hermes에서 Actual Computer로 자신의 기기에 AI 모델을 실행하도록 연결을 설정합니다. 중계 서비스와 로컬 연결, 모델 선택, 응답 확인 및 오류 점검을 다룹니다.
기술에 익숙하지 않고 쉽게 불만을 느끼는 사용자로 앱을 시험합니다. 혼란스러운 조작과 이탈 원인을 찾고, 개선 과제와 개인적 불만을 나눠 수정안을 정리합니다.
두 에이전트가 각각 수정한 브랜치의 Git 충돌을 양쪽 변경 내용과 의도에 따라 중립적으로 해결합니다. 충돌별 판단 이유를 기록하고 빌드나 테스트로 통합 결과를 확인합니다.