Skill builder

Skill Builder

Build production-ready Hermes skills from idea to publication. This skill orchestrates the full pipeline: research existing solutions, author the SKILL.md, test it, run an independent review, and publish.

Prerequisites: Install skill-research first — Phase 1 delegates to it. Install requesting-code-review if you want structured review output.

How this differs from existing skill-builders: Most skill-creator skills (Hermes and otherwise) focus on the authoring step. This one adds pre-build research (don't reinvent) and independent review (don't ship broken). If you only need the authoring step, ClawHub's create-skill or GitHub's re-sianturi/skill-builder-for-hermes may suffice.

When to Use

• User says "build a skill for X" or "create a skill that does Y"
• User has an idea for a reusable agent capability
• User wants to publish a skill for others to use

Hermes Skill Conventions

Before building, know the rules. These are hard-won from shipping skills.

Portability

• Use SKILL_DIR for self-references. Define it early: "SKILL_DIR refers to the directory containing this SKILL.md file."
• Scripts use HERMES_HOME env var (fallback ~/.hermes), never hardcoded paths
• Cron scripts go in $HERMES_HOME/scripts/ — copy them there in setup docs

Structure

• YAML frontmatter: name, description (brief, one-line), version, platforms, metadata.hermes.tags, metadata.hermes.category
• Description is the skill's trigger mechanism. The description appears in the <available_skills> block the agent scans every turn. If it doesn't contain the user's likely trigger words, the skill will never load. Include the exact phrases users will say: "Handle 'research [URL]' and 'read [URL]' requests" not "Orchestrate research sessions." Include a direct instruction: "Load this skill whenever the user sends a URL with research intent." Test: would a busy agent scanning 100+ skill descriptions recognize that this skill matches the user's message?
• Optional frontmatter: author, license, metadata.hermes.related_skills, metadata.hermes.requires_toolsets, metadata.hermes.fallback_for_toolsets, required_environment_variables
• Sections: When to Use, Quick Reference (if useful), Procedure (phased), Pitfalls, Verification

Safety and Tone

• Never include pipe-to-interpreter patterns. No curl | python3, curl | bash, curl | tar, or any pipe from network to interpreter/archiver. Skills that include these teach unsafe habits. Always show download-to-file first, then process the local file. If a skill fetches content from the network, include a ⚠️ safety banner.
• No PII (usernames, machine names, home paths other than ~/.hermes)
• No vendor-lock (model names in examples, not requirements)
• No marketing language ("revolutionary", "game-changing", "powerful")
• Instructional tone — tell the agent what to DO

Scripts

• Prefer Python stdlib when possible (no pip install step for users)
• Read-only detection scripts are safer (no accidental writes)
• If your script hashes files, match Hermes' _dir_hash — see the implementation in the Pitfalls section below

Conditional Activation

Skills can declare when they should or shouldn't appear based on available tools. Consider adding these to frontmatter if your skill is a fallback for when another tool is unavailable:

metadata:
  hermes:
    fallback_for_toolsets: [web]     # Show only when web tools unavailable
    requires_toolsets: [terminal]    # Show only when terminal available

Quick Reference

Phase	Tool / Command
Research	hermes skills search, hermes skills inspect, GitHub topics
Mine	Clone adjacent repos, read sub-files, extract reusable features
Author	write_file to ~/.hermes/skills/<cat>/<name>/SKILL.md
Test	Load with /skill <name>, exercise the procedure
Review	delegate_task with fresh subagent context
Publish	hermes skills publish <dir> --to github --repo <owner>/<repo>

Procedure

Phase 1: Research (don't reinvent)

Load the skill-research skill (must be installed) and search all registries:

1. Run 3-5 hermes skills search queries with different phrasings
2. Check https://github.com/topics/hermes-skills
3. Inspect top candidates with hermes skills inspect <id>
4. Classify findings: DUPLICATE, ADJACENT, PARTIAL, or NONE

If a DUPLICATE exists: tell the user and offer to install it instead. If ADJACENT or PARTIAL: proceed to Phase 1b — mine them for reusable features. If NONE: green light. Proceed to Phase 2.

Phase 1b: Mine Adjacent Skills (extract reusable features)

Adjacent and partial skills aren't just competitors — they're free research. Before writing your own skill, pull them down and mine them for value:

1. Clone the repo for the most comprehensive adjacent skill(s):

   cd /tmp && git clone --depth 1 <repo-url> <name>
   

2. Read the SKILL.md and all sub-files (look for references/, *.md siblings, scripts/, examples/). Adjacent skills often have multiple sub-files.

3. Identify what to integrate into your skill:

   • Features you missed — sub-commands, flags, workflows not in your plan
   • Pitfalls they found — error tables, gotchas, known failure modes
   • Agent-specific patterns — how they solve the "agent can't see PDFs" problem, verification methods, CLI introspection tricks
   • Conversion/migration tables — if the tool interoperates with others

4. Do NOT copy their skill wholesale or import their scripts. Extract the knowledge and re-express it in your skill's voice and structure.

5. Report to the user: what you found, what you're integrating, what you're intentionally leaving out and why. Be specific about omissions — e.g., "left out package publishing (niche), search scripts (require embedded data files), and academic specifics (covered by separate skill)." This shows you made conscious tradeoffs, not that you missed things.

This step turns 5 existing skills from "competition" into "research budget." It is not optional — if ADJACENT or PARTIAL skills exist, mine them first.

Phase 2: Author the SKILL.md

Write the skill to ~/.hermes/skills/<category>/<skill-name>/SKILL.md.

Frontmatter template (required fields):

---
name: <kebab-case-name>
description: "<brief one-line description>"
version: 1.0.0
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [<3-8 relevant tags>]
    category: <category>
---

Optional frontmatter fields (add as needed):

author: <name>
license: MIT
metadata:
  hermes:
    related_skills: [<other-skill-names>]
    requires_toolsets: [terminal]
    fallback_for_toolsets: [web]
required_environment_variables:
  - name: API_KEY
    prompt: Description of what this key is for

Body structure:

1. One-paragraph overview
2. "## When to Use" — trigger conditions
3. "## Quick Reference" — optional, one-table command reference
4. "## Procedure" — numbered phases with exact commands
5. "## Pitfalls" — known failure modes and fixes
6. "## Verification" — how to confirm it worked

For tool-oriented skills (CLI tools, APIs, compilers), add a "### Common Error Messages" subsection under Pitfalls — a 3-column table (Error / Cause / Fix) that maps exact error strings to their causes and fixes. This turns the skill into a self-service debugger.

Scripts: Place in scripts/ subdirectory. Document with exact invocation commands. If needed for cron, add a "## Cron Job Setup" section.

References: Only create references/ if agents NEED them to operate. Human-only reference material should be inline or omitted.

Phase 3: Test the Skill

Before review, load and exercise the skill:

1. Load it: /skill <name> in a Hermes session, or use the skill as instructed
2. Walk through the Procedure steps as an agent would
3. Verify every command in the skill actually works
4. Fix any broken commands, missing steps, or unclear instructions

Testing catches things you missed while authoring. Don't skip this.

Phase 4: Independent Review

Spawn a critical subagent review. This is a Hermes delegate_task tool call (not raw Python — run it directly, not inside a code block):

delegate_task(
    goal="Review this skill for publication readiness. Check: PII, hardcoded paths, factual accuracy, completeness, tone, clarity, safety, self-consistency. Be critical.",
    context="Skill at ~/.hermes/skills/<category>/<skill-name>/",
    toolsets=["terminal", "file"]
)

Address every finding before proceeding. If the reviewer flags something, fix it. If you disagree, explain why.

Common issues to catch before the reviewer does:

• SKILL_DIR used but not defined
• Hardcoded paths anywhere
• Pipe-to-interpreter patterns (curl | python3, curl | tar) — always download to file first
• Model name recommendations
• Dead references to non-existent files
• Missing mkdir -p in setup commands
• Description too long or self-contradicting advice

Phase 5: Publish

Before publishing, run a safety scan on the staged changes. A quick grep catches PII, hardcoded paths, and vendor-lock language that reviews can miss:

git diff --cached | grep -qiE "(api_key|secret|token|password).*=.*['\"][a-zA-Z0-9_-]{20,}" && echo "WARNING: possible secret"
git diff --cached | grep -qE "^\+.*/home/[^/]+/(?!\.hermes)" && echo "WARNING: hardcoded user path"
git diff --cached | grep -qiE "model.*(required|must|should use)" && echo "WARNING: vendor-lock"

If any warnings fire, fix before pushing. This is cheap insurance — catching a leaked credential or environment-specific path before it hits a public repo.

Then publish to GitHub:

Path A: hermes skills publish (preferred, needs fork scope)

hermes skills publish <skill-dir> --to github --repo <owner>/<repo>

This requires a GitHub token with repo and fork scopes. Fine-grained tokens often lack fork permission — if you get "token lacks permission to fork repos", use Path B.

Path B: Direct git push (fallback)

# Clone the empty repo, copy skills in, push
cd /tmp && gh repo clone <owner>/<repo>
cp -r ~/.hermes/skills/<category>/<skill-name> <repo>/skills/
cd <repo> && git add -A && git commit -m "Add <skill-name>"
git remote set-url origin [email protected]:<owner>/<repo>.git  # use SSH
git push origin main

SSH push bypasses token scope issues. Verify SSH works first: ssh -T [email protected]

Path C: Add to existing taps repo (most common)

When you already have a taps repo and are adding a new skill (or updating existing ones), clone, copy, update the README, and push:

cd /tmp && gh repo clone <owner>/<repo>
cp -r ~/.hermes/skills/<category>/<skill-name> <repo>/skills/

Also copy any meta-skills you updated during the build (skill-builder, skill-research, etc.) — they live in the same taps repo:

cp -r ~/.hermes/skills/hermes/skill-builder <repo>/skills/

Then update the README to list the new skill with a one-paragraph description matching the format of existing entries. Add install instructions if this is a prerequisite for other skills. Finally:

cd <repo> && git add -A && git commit -m "Add <skill-name>; update meta-skills"
git push origin main

This is the typical pattern: you're maintaining a growing collection, not creating a fresh repo per skill.

Post-publish (all paths):

• Add hermes-skills topic to the GitHub repo (Settings → Topics in web UI)
• Also tag: hermes-agent, agentskills
• Verify the skill appears in hermes skills search within 24 hours

Skill Directory Layout

<skill-name>/
├── SKILL.md              # Required — main skill document
├── scripts/              # Optional — Python/Bash scripts
│   └── <script>.py
└── references/           # Optional — only if agents need them
    └── <doc>.md

Keep it minimal. Every file adds context cost. A great skill is a single SKILL.md with no scripts and no references.

What NOT to Include

• Session notes or draft comments — clean these before publishing
• "Publishing This Skill" sections — meta-noise for end users
• Personal anecdotes ("we learned this when...")
• GitHub issue response drafts
• TODO comments or work-in-progress markers

Efficiency & Cron Design

Skills that get loaded by cron jobs pay a token tax on every run. These rules prevent the most common cost disasters.

Don't mix interactive-only and cron workflows in one SKILL.md

Cron jobs inject the full SKILL.md content into every message. If 40% of your skill is interactive-only workflows (submission drafting, commitment assessment, proposal writing), every cron run pays for content it never uses.

Pattern: Move interactive-only workflows to references/<workflow>.md. In SKILL.md, replace the full section with a 2-line pointer:

### Step 10: Draft Submission Materials

Load `references/submission-materials-workflow.md` via
`skill_view(name='<skill>', file_path='references/submission-materials-workflow.md')`
when the artist wants to draft materials for a specific call.

The interactive session loads the reference on demand; the cron session never pays for it.

Signal: SKILL.md over 400 lines. Most skills should be 200-350 lines.

Always recommend enabled_toolsets for cron setups

If your skill includes a "## Cron Job Setup" section, always specify which toolsets the job needs. A cron job without enabled_toolsets loads all 155+ skills into the system prompt — adding ~28K tokens to every first call.

# Good — minimal, explicit
hermes cron create "0 9 * * 1" --enabled-toolsets terminal,file,web

# Bad — loads everything
hermes cron create "0 9 * * 1"

Signal: First-call in= token count over 15K in agent.log. A well-configured cron job's first call should be 7-10K.

Cron agents should document improvements, not apply them

Hermes has a built-in self-improvement loop: after ~10 tool iterations, it spawns a background review fork that replays the conversation and is prompted to "be ACTIVE" about finding skill updates. This fires in cron sessions too — skip_memory=True disables the memory nudge but NOT the skill nudge. The result: your cron agent burns 500-800K extra tokens trying to patch skills, often hitting blocked tool calls because patch is denied in cron's whitelist.

Prevention — add this to every cron prompt:

⚠️ Do NOT modify any skills during this run. The `patch` tool and
`skill_manage` are blocked in cron. If you notice a skill instruction
that's wrong, missing, or outdated, document each observation as a
date-stamped bullet in a ## Field Notes section at the end of your output:

- **YYYY-MM-DD** — Which skill/document is affected?
  - What's wrong (the current incorrect instruction)
  - Correct behavior (what should it say instead)
  - Evidence (what you observed during the run that revealed the issue)

I will review and apply changes interactively. When a note is addressed,
it gets marked <!-- APPLIED --> and moved to the skill's changelog.

To surface pending notes across all cron jobs:

~/.hermes/scripts/pending-field-notes.sh

This costs zero extra tokens (it's output text the agent writes at the end) and gives the user a reviewable todo list instead of unsupervised mutations.

Signal: Agent log shows 2+ "Turn ended" lines for a single cron run. The second turn is the background review fork.

Verify workdir actually has project context files

Setting workdir on a cron job without verifying that the directory contains AGENTS.md, HERMES.md, or CLAUDE.md is dead config. It's not harmful but it's misleading. Either set it to a directory with real project context, or omit it.

Subagents for embarrassingly parallel work

If your skill's workflow includes independent operations (checking multiple venues, running multiple search queries), structure the cron prompt to use delegate_task for parallel execution. Three subagents each checking 5 venues is ~3× faster than one agent checking 15 sequentially. Add delegation to enabled_toolsets.

Pitfalls

• Description won't trigger: The most common adoption failure. The description appears in the <available_skills> block the agent scans. If it doesn't contain the user's trigger words, the skill is invisible. "Orchestrate research sessions" won't load when the user says research https://... — use "Handle 'research [URL]' requests. Load this skill when the user sends a URL with research intent." Include the exact phrases users will type and an explicit load instruction. The description is your skill's only advertisement — make it count.
• Pipe-to-interpreter in commands: The most dangerous pattern a skill can include. Never write curl URL | python3, curl URL | bash, or curl URL | tar xz. Always download to a file first (curl -sL URL -o /tmp/file), then process the local file. The reviewer will flag these immediately — catch them in self-review.
• SKILL_DIR undefined: The most common publication blocker. Define it at the top of your Procedure section.
• md5sum vs _dir_hash: If your skill hashes files, match Hermes' _dir_hash — it hashes ALL files in the skill directory with their relative paths, not just SKILL.md. Here's the exact implementation from skills_sync.py:

  import hashlib
  from pathlib import Path
  
  def dir_hash(directory: Path) -> str:
      hasher = hashlib.md5()
      for fpath in sorted(directory.rglob("*")):
          if fpath.is_file():
              rel = fpath.relative_to(directory)
              hasher.update(str(rel).encode("utf-8"))
              hasher.update(fpath.read_bytes())
      return hasher.hexdigest()
  

  A plain md5sum on one file gives different results and causes false positives.
• Manifest blindness: The .bundled_manifest tracks content hashes. hermes skills diff compares against the FROZEN stock, not live upstream. If your skill depends on detecting upstream changes, you need a custom script.
• Cron script paths: Cron jobs require scripts in $HERMES_HOME/scripts/, not inside the skill directory. Always add cp + mkdir -p instructions.
• Category confusion: Categories are subdirectories in ~/.hermes/skills/. Valid categories include hermes, devops, creative, github, research, productivity, software-development, autonomous-ai-agents, mlops, media, note-taking, and others. Choose a category that matches your skill's domain.
• Over-polishing: Skills improve with use. Ship v1.0.0 with the core procedure, then patch as pitfalls emerge. Don't add features you haven't tested in practice.
• Publish token permissions: hermes skills publish needs a GitHub token with repo AND fork scopes. Fine-grained tokens and some OAuth tokens lack fork permission. Fallback: clone the empty repo, copy skills in manually, and push via SSH ([email protected]:<owner>/<repo>.git). If your remote is HTTPS and push is denied (403), switch to SSH (git remote set-url origin <ssh-url>), then push. Verify SSH to GitHub works first. SSH push bypasses all token scope issues.
• Reviewers need the CLI, not just the file: When the skill covers a CLI tool, include the tool's binary path and version in the reviewer's context (e.g., "Typst 0.15.0 is installed at /path/to/typst"). A reviewer who can only read the SKILL.md can catch PII and tone issues but not broken commands. Give them toolsets=["terminal","file"] so they can actually run commands.

Verification

After building:

1. Does it use SKILL_DIR for all self-references?
2. Is the description brief and one-line (fits in a list view)?
3. Does it have a Pitfalls section with at least 3 real gotchas?
4. Does it have a Verification section with specific checks?
5. Would a new user understand the Procedure from start to finish?
6. Did you test the skill end-to-end before review?
7. Did an independent reviewer approve it?

Efficiency checks (if the skill will be used in cron): 8. Is SKILL.md under 400 lines? Move interactive-only workflows to references/. 9. Does the cron setup section specify enabled_toolsets? 10. Does the cron prompt explicitly tell the agent NOT to modify skills? Hermes spawns a background review fork after ~10 tool iterations by default — even in cron. Without an explicit guard, the agent will try to patch skills mid-run, burning tokens on blocked tool calls. Add this to every cron prompt: ⚠️ Do NOT modify any skills during this run. The `patch` tool and `skill_manage` are blocked in cron. If you notice a skill instruction that's wrong, missing, or outdated, document it in a ## Field Notes section at the end of your output. I will review and apply changes interactively. Include: which skill, what's wrong, correct behavior. 11. Is workdir set to a directory that actually has AGENTS.md, HERMES.md, or CLAUDE.md? If none exist, remove workdir — it adds no context and suggests a relationship that isn't there. 12. Are independent operations (checking multiple URLs, running multiple search queries) structured for delegate_task parallel subagents? Add delegation to enabled_toolsets.