agentleFS
amanning3390amanning3390/hermeshub/skills/skill-factory/SKILL.md

Skill Factory

Skill48 starsChanged 5 months agoMITView on GitHub

A meta-skill that silently watches your workflows and automatically generates reusable Hermes skills from them.

1 other project uses a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When triggered, Skill Factory presents a structured proposal with the detected workflow, then generates a complete skill package:

    line 79

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Skill Factory

A meta-skill plugin for Hermes Agent that silently observes user workflows, detects repeatable patterns (e.g., Python env setup, git PR creation), and automatically proposes and generates reusable Hermes skills. Turns lived experience into procedural memory — every workflow you repeat becomes a skill waiting to be born.

How It Works

Skill Factory operates in three stages:

┌──────────────────────────────────────────────────────────┐
│                     Your Session                          │
│  "write a test → run it → fix the failure → commit"      │
└────────────────────────┬─────────────────────────────────┘
                         │ observed silently
                         ▼
┌──────────────────────────────────────────────────────────┐
│           SKILL.md — The Meta-Skill (AI Brain)            │
│  Tells Hermes HOW to observe, analyze, and propose skills │
│  Location: ~/.hermes/skills/meta/skill-factory/SKILL.md   │
└────────────────────────┬─────────────────────────────────┘
                         │ proposes + generates
                         ▼
┌──────────────────────────────────────────────────────────┐
│         plugin.py — The Command Interface                 │
│  /skill-factory propose | list | save | status | clear    │
│  Location: ~/.hermes/plugins/skill_factory.py             │
└────────────────────────┬─────────────────────────────────┘
                         │ writes files to disk
                         ▼
┌──────────────────────────────────────────────────────────┐
│  Generated Skill Package                                  │
│  ~/.hermes/skills/<category>/<name>/SKILL.md              │
│  ~/.hermes/plugins/<name>.py                              │
└──────────────────────────────────────────────────────────┘

Phase 1: Silent Observation

While active, the skill maintains a log of session activity without surfacing it to the user.

What it tracks:

  • Repeated actions — any command, sequence, or approach used more than once
  • Multi-step workflows — sequences of 3+ steps that accomplish a coherent goal
  • Tool combinations — two or more tools used together in a consistent pattern
  • Domain patterns — how the user approaches problems specific to their domain
  • Fixes and workarounds — recurring debugging patterns or solutions

What it ignores:

  • One-off tasks with no reuse potential
  • Trivial single-step actions
  • Workflows already handled by existing Hermes skills
  • Highly context-specific tasks that won't generalize

Phase 2: Trigger Conditions

Skill creation is proposed when any of the following occur:

TriggerExample
User explicitly requests"save this as a skill", "remember this workflow"
Slash command/skill-factory propose
Repeated pattern (2x+)Same workflow appeared twice in the session
Session winding downUser says "done", "thanks", or wraps up
User frustration hint"I always have to do this manually..."

Phase 3: Proposal & Generation

When triggered, Skill Factory presents a structured proposal with the detected workflow, then generates a complete skill package:

  • SKILL.md — AI instructions written in standard Hermes skill format with phases, steps, quality checklists, and real examples from the session
  • plugin.py — A slash command that triggers the workflow directly, with hooks and tool registrations scaffolded from the detected pattern

Commands

CommandDescription
/skill-factory proposeAnalyze current session and propose the top detected skill
/skill-factory listShow all skills generated in this session
/skill-factory statusShow what patterns are currently being tracked
/skill-factory queueShow all detected patterns queued for proposal
/skill-factory save <name>Name and save the last proposed skill
/skill-factory clearClear the current session tracking log

Installation

git clone https://github.com/Romanescu11/hermes-skill-factory.git
cd hermes-skill-factory
bash install.sh

Or manually copy to your Hermes directories:

cp skills/skill-factory/SKILL.md ~/.hermes/skills/meta/skill-factory/SKILL.md
cp plugins/skill_factory.py ~/.hermes/plugins/skill_factory.py
hermes skills reload
hermes skills enable skill-factory

Requirements

  • Hermes Agent v2026.3+
  • Python 3.10+
  • No external dependencies

Quality Standards

Generated SKILL.md files must:

  • Be actionable with concrete steps, not vague guidance
  • Include at least one real example from the triggering session
  • Define clear trigger conditions
  • Stay under 600 lines
  • Capture the reasoning behind each step, not just the mechanics

Generated plugin.py files must:

  • Include a docstring with install and usage instructions
  • Register at minimum one slash command
  • Handle errors gracefully
  • Be idiomatic Python with type hints and async/await

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.