Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/techygarg/lattice/skill-forgenpx skills add techygarg/lattice --skill skill-forgegit clone --depth 1 https://github.com/techygarg/latticeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/techygarg/lattice/skill-forge)<a href="https://agentmods.dev/skills/techygarg/lattice/skill-forge"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/skill-forge.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00139 | $0.01835 |
| Opus 5 | $0.00069 | $0.00918 |
| Sonnet 5 | $0.00028 | $0.00367 |
| Haiku 4.5 | $0.00014 | $0.00184 |
Grade A, and why
skill-forge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lattice Skill Forge
Core responsibility: Create the right files with the right structure for a new Lattice skill.
Input: A description of what the skill should do and when it should trigger.
Output: One or more skill files written to the correct path:
- Atom →
skills/atoms/{name}/SKILL.md+skills/atoms/{name}/references/defaults.md - Molecule →
skills/molecules/{name}/SKILL.md - Refiner →
skills/refiners/{name}/SKILL.md+skills/refiners/{name}/assets/template.md
How to verify this skill did its job:
- All required files exist at the correct paths
- Folder name matches
name:frontmatter exactly - All tier-required sections are present in correct order
- No placeholder content — all sections contain real, specific content
Step 1: Understand intent and select tier
Ask the user: "What should this skill do, and when should it trigger? Describe it briefly."
From the description, determine the tier:
| The skill... | Tier |
|---|---|
| Enforces ONE principle with a checklist and anti-pattern scan | Atom |
| Orchestrates multiple atoms into a multi-step workflow | Molecule |
Runs a guided interview to produce a .lattice/standards/*.md file |
Refiner |
State your read and confirm with the user. Get explicit tier agreement before proceeding.
Step 2: Requirements alignment (molecules and refiners only)
Before writing a single line of SKILL.md, agree on the design.
Check knowledge-base/ for an existing requirements doc:
ls knowledge-base/ | grep -i {name}
If found → read it, summarise key design decisions, confirm they still reflect intent.
If not found → resolve these questions through conversation before writing:
For a molecule:
- Which atoms does it compose? (Read
skills/atoms/to see what exists.) - Is it generative (produces code/artifacts, linear flow) or planning/interactive (produces living documents, confirmation gates at each phase)?
- What does it write to
.lattice/? Which subfolder? (Must be a named subfolder, never the root.) - What is the session resume behavior — how does it handle an interrupted session?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 156 lines · 139 tokens per session scan A 11181474be40
skill-forge is a skill published in the GitHub repository techygarg/lattice (183 stars, last pushed 5d ago), licensed MIT. It adds 139 tokens to every session and 1,835 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
xiaoyaoclaw-workspace-initializer
OpenClaw workspace initialization & standardization. Sets up a proper agent home: standard directory structure (projects/tasks/outputs/knowledge/scripts/ memory/tmp) + WORKSPACE.md rules + multi-agent config safety (config.patch, never config.apply) + memory log. Use when an agent enters a new/empty workspace root, or…
learn
Search, install, update, and rate AI agent skills from agentskill.sh (100,000+ skills). Use when the user asks to find skills, install extensions or plugins, discover new capabilities, check what skills are available, or says "how do I do X" when a skill might help. Also handles listing installed skills, checking for…
review-skill
Review and improve AI agent skills (SKILL.md files) against best practices from the Agent Skills specification and Anthropic's authoring guidelines. Scores skills on 10 quality dimensions, identifies specific issues, and rewrites problem areas. Use when creating, editing, auditing, or improving agent skills. Triggers…
building-chatgpt-apps
Guides creation of ChatGPT Apps with interactive widgets using OpenAI Apps SDK and MCP servers. Use when building ChatGPT custom apps with visual UI components, embedded widgets, or rich interactive experiences. Covers widget architecture, MCP server setup with FastMCP, response metadata, and Developer Mode…
memory-systems
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.
scaffolding-openai-agents
Builds AI agents using OpenAI Agents SDK with async/await patterns and multi-agent orchestration. Use when creating tutoring agents, building agent handoffs, implementing tool-calling agents, or orchestrating multiple specialists. Covers Agent class, Runner patterns, function tools, guardrails, and streaming…