hermes-agent-skill-authoring

/home/avalon/.hermes/skills/software-development/hermes-agent-skill-authoring/SKILL.md · raw

Authoring Hermes-Agent Skills (in-repo)

Overview

There are two places a SKILL.md can live:

  1. User-local: ~/.hermes/skills/<maybe-category>/<name>/SKILL.md — personal, not shared. Created via skill_manage(action='create').
  2. In-repo (this skill is about this case): /home/bb/hermes-agent/skills/<category>/<name>/SKILL.md — committed, shipped with the package. Use write_file + git add. skill_manage(action='create') does NOT target this tree.

When to Use

Required Frontmatter

Source of truth: tools/skill_manager_tool.py::_validate_frontmatter. Hard requirements:

Peer-matched shape used by every skill under skills/software-development/:

---
name: my-skill-name               # lowercase, hyphens, ≤64 chars (MAX_NAME_LENGTH)
description: Use when <trigger>. <one-line behavior>.
version: 1.1.0
author: Hermes Agent
license: MIT
metadata:
  hermes:
    tags: [short, descriptive, tags]
    related_skills: [other-skill, another-skill]
---

version / author / license / metadata are NOT enforced by the validator, but every peer has them — omit and your skill sticks out.

Size Limits

Writing Quality Principles

A skill exists to make the agent's process more predictable. Predictability does not mean identical output every run; it means the agent reliably follows the same useful discipline.

Use these quality checks when writing or editing any skill:

  1. Optimize for process predictability. Ask: what behavior should change when this skill loads? If a line does not change behavior, cut it.
  2. Choose the right context load. A model-invoked Hermes skill pays for its description every turn. Keep descriptions focused on trigger classes and the skill's distinctive behavior. Put details in the body or linked references.
  3. Use an information hierarchy. Put always-needed steps in SKILL.md; put branch-specific or bulky reference material in references/, templates/, or scripts/ and point to it only when needed.
  4. End steps with completion criteria. Each ordered step should say how the agent knows it is done. Good criteria are checkable and, when it matters, exhaustive: "every modified file accounted for" beats "summarize changes."
  5. Co-locate rules with the concept they govern. Avoid scattering one idea across the file. Keep definition, caveats, examples, and verification near each other.
  6. Use strong leading words. Prefer compact concepts the model already knows — e.g. "tight loop," "tracer bullet," "root cause," "regression test" — over long repeated explanations. A good leading word saves tokens and anchors behavior.
  7. Prune duplication and no-ops. Keep each meaning in one source of truth. Sentence by sentence, ask whether the sentence changes agent behavior versus the default. If not, delete it rather than polishing it.
  8. Watch for premature completion. If agents tend to rush a step, first sharpen that step's completion criterion. Split the sequence only when later steps distract from doing the current step well.

Common quality failures:

Peer-Matched Structure

Every in-repo skill follows roughly:

# <Title>

## Overview
One or two paragraphs: what and why.

## When to Use
- Bulleted triggers
- "Don't use for:" counter-triggers

## <Topic sections specific to the skill>
- Quick-reference tables are common
- Code blocks with exact commands
- Hermes-specific recipes (tests via scripts/run_tests.sh, ui-tui paths, etc.)

## Common Pitfalls
Numbered list of mistakes and their fixes.

## Verification Checklist
- [ ] Checkbox list of post-action verifications

## One-Shot Recipes (optional)
Named scenarios → concrete command sequences.

Not every section is mandatory, but Overview + When to Use + actionable body + pitfalls are the minimum for the skill to feel like a peer.

Directory Placement

skills/<category>/<skill-name>/SKILL.md

Categories currently in repo (confirm with ls skills/): autonomous-ai-agents, creative, data-science, devops, dogfood, email, gaming, github, leisure, mcp, media, mlops/*, note-taking, productivity, red-teaming, research, smart-home, social-media, software-development.

Pick the closest existing category. Don't invent new top-level categories casually.

Workflow

  1. Survey peers in the target category: ls skills/<category>/ Read 2-3 peer SKILL.md files to match tone and structure.
  2. Check validator constraints in tools/skill_manager_tool.py if unsure.
  3. Draft with write_file to skills/<category>/<name>/SKILL.md.
  4. Validate locally: python import yaml, re, pathlib content = pathlib.Path("skills/<category>/<name>/SKILL.md").read_text() assert content.startswith("---") m = re.search(r'\n---\s*\n', content[3:]) fm = yaml.safe_load(content[3:m.start()+3]) assert "name" in fm and "description" in fm assert len(fm["description"]) <= 1024 assert len(content) <= 100_000
  5. Git add + commit on the active branch.
  6. Note: the CURRENT session's skill loader is cached — skill_view / skills_list will not see the new skill until a new session. This is expected, not a bug.

Cross-Referencing Other Skills

metadata.hermes.related_skills unions both trees (skills/ in-repo and ~/.hermes/skills/) at load time. You CAN reference a user-local skill from an in-repo skill, but it won't resolve for other users who clone the repo fresh. Prefer referencing only in-repo skills from in-repo skills. If a frequently-referenced skill lives only in ~/.hermes/skills/, consider promoting it to the repo.

Editing Existing In-Repo Skills

Common Pitfalls

  1. Using skill_manage(action='create') for an in-repo skill. It writes to ~/.hermes/skills/, not the repo tree. Use write_file for in-repo creation.

  2. Leading whitespace before ---. The validator checks content.startswith("---"); any leading blank line or BOM fails validation.

  3. Description too generic. Peer descriptions start with "Use when ..." and describe the trigger class, not the one task. "Use when debugging X" > "Debug X".

  4. Forgetting the author/license/metadata block. Not validator-enforced, but every peer has it; omitting makes the skill look half-finished.

  5. Writing a skill that duplicates a peer. Before creating, ls skills/<category>/ and open 2-3 peers. Prefer extending an existing skill to creating a narrow sibling.

  6. Expecting the current session to see the new skill. It won't. The skill loader is initialized at session start. Verify in a fresh session or via skill_view using the exact path.

  7. Letting skills accumulate sediment. A skill should get shorter or sharper over time. When adding a rule, remove the old wording it replaces; don't layer advice forever.

  8. Writing no-op prose. "Be careful," "be thorough," and "use best practices" rarely change model behavior. Replace with a checkable completion criterion or a stronger leading word.

  9. Linking to skills that don't exist in-repo. related_skills: [some-user-local-skill] works for you but breaks for other clones. Prefer only in-repo links.

Verification Checklist