By Vilius Vystartas | May 2026
I run a lot of AI agents. Not chatbots — autonomous agents. Cron jobs that monitor my infrastructure every hour. Self-improvers that analyze past sessions and encode learnings. Delegated coders that build features while I sleep. Together they load from a library of 153 reusable skills — structured procedures that tell an agent how to do something specific, from sending iMessages to debugging SPFx builds.
The system worked fine when I had 20 skills and one agent. It started breaking when the numbers climbed.
The Problem That Forced My Hand
Here's the setup: each skill lives as a SKILL.md file in ~/.hermes/skills/. When an agent loads a skill and discovers it's broken, missing steps, or out of date, it records the problem in a shared skill_gaps.jsonl file. Later, I review the gaps and fix the skills.
This is fine when one agent writes to the file at a time.
It stops being fine when three autonomous agents — say, a 2am cron job, a self-improvement loop, and a code review agent — all try to write to the same JSONL file within the same second.
Concurrent writes collide. Lines get truncated. Data vanishes.
I lost track of which skills needed fixing. Agents kept loading broken skills silently because the gap reporting was unreliable. Worse, I had no search — finding "that one skill about PyPI releases" meant grepping a directory tree and hoping the frontmatter was consistent.
The flat-file approach doesn't scale past a few dozen skills. I had 153.
What I Built: Skill Forge
Skill Forge is a SQLite-backed skill registry with quality gates, full-text search, and concurrent-safe writes. It replaces the broken JSONL pipeline with atomic transactions. It doesn't move your skills — it indexes them in place.
Think of it as pip for agent skills, but local-first, with validation before installation.
$ forge status
Skill Forge Registry Status
===========================
Database: ~/.hermes/skill-forge/forge.db
Total skills: 153
By category:
mlops: 12 devops: 8 creative: 15
career: 3 research: 7 (uncategorized): 108
Quality checks run: 306
Skills with failures: 0 ✓
Why SQLite?
Three reasons:
WAL mode — multiple agents can read and write simultaneously without locking each other out. Each agent gets its own connection with foreign-key enforcement. When two agents register different skills at the same time, both succeed. Atomic transactions, no corrupted state.
FTS5 — full-text search over name, category, description, and body content. Finding "that skill about PyPI release classifiers" is
forge search "pypi classifier"— instant, ranked results.Single file —
forge.dbin~/.hermes/skill-forge/. No server process. No configuration. Backs up withforge export. Portable.
Quality Gates That Catch Real Problems
Before Skill Forge, broken skills went undetected until an agent loaded them mid-task and hit a wall. Now every skill runs through two validation passes:
Frontmatter validator — catches missing YAML, absent required fields (name/description/version), and invalid semver strings. A skill with version: "latest" gets flagged. One with version: "1.2.3" passes.
Structure validator — checks for required sections: a description block, trigger conditions, and usage steps. A skill that's just a title and a broken shell command fails. One with proper ## Trigger, ## Steps, and ## Pitfalls sections passes.
The first run on my 153 skills: 102 passed, 51 flagged. The flagged ones weren't bugs — they were real quality issues I'd been ignoring. Skills missing version numbers. Skills with no trigger conditions. Skills where the "Steps" section was one garbled paragraph.
I fixed 38 of them that afternoon. The other 13 are low-priority and tagged for later.
CLI Commands That Match the Workflow
Ten commands, each solving a specific pain point:
forge import-hermes # First run: scan ~/.hermes/skills/, register everything
forge register <path> # Add a single skill
forge validate [--name <n>] # Run quality gates on all or one skill
forge search <query> # FTS5 over name + description + body
forge list [--category <cat>] # Filtered listing
forge status # Health overview
forge inspect <name> # Full detail + quality check history
forge prune # Remove stale entries (skill file deleted from disk)
forge export [-o <file>] # JSON dump for backups or analysis
forge watch [--once] [--interval <s>] # Auto-reimport on changes
The watch command is the cron workhorse. Drop this in a 30-minute cron job:
forge watch --once
It scans the skills directory, detects new/modified files (content hash, not timestamp), registers new ones, re-registers changed ones (version bump), and marks deleted skills as stale. One pass, everything synced.
Architecture
The stack is deliberately minimal — Python 3.11, Click for the CLI, SQLite for storage, PyYAML for frontmatter parsing. No web framework, no message queue, no cloud dependency.
CLI (forge) ← Click entry point
├── registry (SQLite + WAL) ← skill index + metadata
├── importer ← scan ~/.hermes/skills/ → register
├── validator ← frontmatter + structure checks
└── FTS5 index ← full-text search
Storage: ~/.hermes/skill-forge/forge.db (single file)
Skills: ~/.hermes/skills/ (unchanged — indexed in place)
Skills stay as flat SKILL.md files. Forge indexes them, validates them, searches them, and tracks their history — but it never moves or modifies them. Your existing automation continues working. Forge adds a layer on top.
Tests and Quality
89 tests. Full suite runs in 0.26 seconds. Covers registry CRUD, importer (Hermes scanner + content-change detection), validators (frontmatter + structure, edge cases like empty files and missing YAML delimiters), CLI integration (prune, export, watch), and concurrent-write scenarios.
What I Learned
SQLite with WAL mode solves the concurrent-agent problem cleanly. You don't need Postgres or Redis for this. Connection-level pragmas (PRAGMA journal_mode=WAL, PRAGMA foreign_keys=ON) and atomic transactions are enough when your write volume is hundreds per hour, not thousands per second.
Quality gates catch real problems, not theoretical ones. 51 of my 153 skills had issues I didn't know about — missing versions, malformed frontmatter, empty sections. Agents were loading these skills silently. The validator turned invisible problems into visible ones.
Content-aware sync matters. My first import skipped files that already existed in the registry by path. This meant I missed skills that had been modified but not renamed. Switching to content-hash comparison caught 12 modified skills on the next import.
Get It
- Repo: github.com/vystartasv/skill-forge
- License: MIT
- Stack: Python 3.11+, Click, SQLite + FTS5, PyYAML
git clone https://github.com/vystartasv/skill-forge
cd skill-forge
pip install -e ".[dev]"
forge import-hermes
forge status
If you're running autonomous AI agents with persistent skill libraries — or if you're building agent infrastructure and wondering how to manage the growing pile of procedures — I'd love feedback on the schema design and quality gate approach.
