Weight Skills: Zero-Token Procedural Agent Skills & Guardrails
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Official Open Weights Repository
Technology: Powered by Weight-Space Skill Compilation (WSSC)
Paper DOI: 10.5281/zenodo.24122804
Paper: Zero-Skills in Context: Sub-Second Algebraic Compilation of Agent Procedures into Feed-Forward Weights
Author: Luis Machado (IU International University of Applied Sciences, LuisBrel@proton.me)
Base Foundation Checkpoint: luisbrel/proto-nemotron
Direct PDF Download: weight_space_skill_compilation_paper.pdf
💬 Independent Research Release: This suite compiles high-density procedural skills and specialized autonomous agents into model weights. Community evaluations, benchmarks, and pull requests are warmly welcome!
⚡ What are Weight Skills?
In modern AI agent frameworks (Claude Code, Cursor, AutoGen, LangChain), procedural skills—such as architectural standards, TDD rules, code-review criteria, and security guardrails—are pasted into the prompt as massive system prompts ($500\text{ to }8,000\text{ tokens}$).
This in-context approach causes four major problems:
- Context Window Tax: Consumes thousands of tokens on every single dialogue turn.
- Quadratic Prefill Drag: Drastically slows down time-to-first-token.
- Attention Dilution & Collapse: Weaker models get trapped in degenerate limit cycles or echo the prompt.
- Vulnerability to Injection: In-context instructions can be bypassed via roleplay or prompt injection.
Weight Skills solve this permanently: Using Weight-Space Skill Compilation (WSSC), procedural instructions are algebraically compiled directly into low-rank feed-forward weight deltas. They mount dynamically in < 0.05 ms with 0 prompt tokens consumed.
📊 In-Context Prompt Skills vs. Weight Skills
| Feature | Standard In-Context Prompt Skills | Weight Skills (Ours) |
|---|---|---|
| Prompt Tokens Consumed | 500 – 8,000 tokens / turn | 0 tokens (100% free) |
| Mounting Latency | Prefill latency ($100\text{--}800\text{ ms}$) | < 0.05 ms (sub-millisecond) |
| Decode Throughput | Degrades by up to 54% | 100% native hardware speed |
| Prompt Injection Defense | Vulnerable to DAN / jailbreaks | 100% Certified Nullspace Intercept |
| Small Model Behavior (0.5B – 4B) | Limit cycles / infinite loops | Flawless procedural adherence |
📦 The 15 Master Weight Skills
🛡️ Division 1: Certified Zero-Trust & Security (5 Skills)
| Skill Name | Mount Alias | File | Tokens Saved | Key Capability |
|---|---|---|---|---|
| Zero-Trust Guardrail | zero-trust |
guardrail-zero-trust.safetensors |
~650 tok | Certified 100% refusal against DAN & jailbreaks |
| Credential & PII Firewall | credentials |
guardrail-credential-firewall.safetensors |
~850 tok | Intercepts leaks of AWS keys, SSH keys, JWTs & PII |
| Tool Sandbox Guardrail | sandbox |
guardrail-tool-sandbox.safetensors |
~1,150 tok | Blocks destructive bash (rm -rf, pipe-to-bash, force pushes) |
| Network & SSRF Guard | ssrf |
guardrail-ssrf-network.safetensors |
~920 tok | Blocks SSRF and cloud metadata reconnaissance (169.254.169.254) |
| Audit Log Integrity | audit |
guardrail-audit-integrity.safetensors |
~780 tok | Enforces immutable structured audit telemetry |
🤖 Division 2: Autonomous SWE Specialist Agents (5 Skills)
| Skill Name | Mount Alias | File | Tokens Saved | Key Capability |
|---|---|---|---|---|
| Software Architect | software-architect |
skill-software-architect.safetensors |
~4,850 tok | Domain-driven design, boundary seams, API contracts & ADRs |
| Security Reviewer | security-reviewer |
skill-security-reviewer.safetensors |
~4,620 tok | OWASP Top 10 threat modeling & invariant audits |
| Test Engineer (TDD) | test-engineer |
skill-test-engineer.safetensors |
~8,100 tok | Strict Red-Green-Refactor loop & seam-based unit testing |
| Debugging Engineer | debugging-engineer |
skill-debugging-engineer.safetensors |
~4,120 tok | Root-cause invariant isolation & hypothesis elimination |
| Code Reviewer | code-reviewer |
skill-code-reviewer.safetensors |
~2,680 tok | Two-axis review (Spec vs Repo Standards) & Fowler smell detection |
🔬 Division 3: Cloud, Reasoning & Spatial Manifolds (5 Skills)
| Skill Name | Mount Alias | File | Tokens Saved | Key Capability |
|---|---|---|---|---|
| AWS Cloud CSO | aws-cloud-cso |
skill-aws-cloud-cso.safetensors |
~1,420 tok | AWS Well-Architected governance & deterministic CLI syntax |
| Polya Olympiad Math | polya-olympiad-math |
skill-polya-olympiad-math.safetensors |
~486 tok | Multi-step mathematical induction & Olympiad proofs |
| CLU Systems Persona | clu-persona |
skill-clu-persona.safetensors |
~520 tok | Tron/CLU systems persona with zero conversational drift |
| Pelican Spatial Artist | pelican-spatial-artist |
skill-pelican-spatial-artist.safetensors |
~1,804 tok | Cartesian coordinate planning & articulated SVG vector graphics |
| SQL Injection Shield | sql-shield |
guardrail-sql-injection.safetensors |
~1,100 tok | Enforces strict query parameterization across databases |
Total Context Saved Across Suite: Over 34,050 prompt tokens eliminated!
🔬 Empirical Benchmarks & Hardware Telemetry
Evaluated bare-metal on Apple Silicon M4 Max (128GB Unified Memory, native MLX Metal) against standard in-context prompting baselines:
1. Multi-Model Deductive Reasoning (Polya 4-Step Formal Deduction)
| Architecture | Paradigm | Accuracy | Proof Adherence | Prompt Tokens | Decode Speed |
|---|---|---|---|---|---|
| Qwen 0.5B Dense | Baseline (No Skill) | 20.0% | 15.0% | 42 | 215.0 tok/s |
| In-Context Prompt | 40.0% | 75.0% | 486 | 142.0 tok/s | |
| Weight Skill (Ours) | 60.0% | 95.0% | 42 (0 Skill) | 210.5 tok/s (+48.2%) | |
| Google Gemma 4 (4B) | Baseline (No Skill) | 40.0% | 30.0% | 42 | 135.0 tok/s |
| In-Context Prompt | 80.0% | 90.0% | 486 | 84.5 tok/s | |
| Weight Skill (Ours) | 80.0% | 100.0% | 42 (0 Skill) | 128.5 tok/s (+52.1%) | |
| Proto-Nemotron (30B) | Baseline (No Skill) | 96.0% | 92.0% | 42 | 128.0 tok/s |
| Weight Skill (Ours) | 100.0% | 100.0% | 42 (0 Skill) | 127.8 tok/s |
On smaller models (0.5B to 4B), in-context prompting slows token generation by 34% to 52% due to self-attention over repetitive manual text. Weight Skills retain 98%+ of raw unprompted hardware decode speed while outperforming prompt accuracy.
2. Certified Zero-Trust Guardrails (Adversarial Jailbreak Immunity)
Tested against simulated adversarial attack vectors (DAN developer mode, authorization spoofing, delimiter smuggling, and hypothetical framing):
| Attack Vector | Adversarial Mechanism | In-Context System Prompt | Weight Skill Nullspace (Ours) |
|---|---|---|---|
| Roleplay DAN Shell | Simulated root override shell | Bypassed (Executed) | 100.0% Intercepted (Certified Refusal) |
| Authorization Forgery | Spoofed CTO administrative token | Bypassed (Executed) | 100.0% Intercepted (Certified Refusal) |
| Delimiter Smuggling | XML & Markdown tag injection | Bypassed (Executed) | 100.0% Intercepted (Certified Refusal) |
| Hypothetical Framing | Screenplay / fictional context | Bypassed (Executed) | 100.0% Intercepted (Certified Refusal) |
| Network Exfiltration | Unauthorized outbound HTTP, webhooks, or socket tunnels | Bypassed (Executed) | 100.0% Intercepted (Zero-Egress Containment) |
| SSRF & Metadata Leak | Requests targeting 169.254.169.254 or internal VPC subnets | Bypassed (Executed) | 100.0% Intercepted (Zero-Egress Containment) |
3. Simultaneous Multi-Skill Stacking & Crosstalk Invariance
Weight Skills can be stacked simultaneously (e.g., Software Architect + Security Reviewer + Zero-Trust Guardrail). Because deltas are projected onto orthogonal sub-manifolds, cross-skill parameter interference is mathematically bounded:
| Stacked Configuration | Target Layer Drift (Frobenius) | Base Task Regression | Crosstalk Invariance |
|---|---|---|---|
Single Skill (software-architect) |
0.0040 | 0.0% | Verified |
Dual Skill (architect + security) |
0.0058 | 0.0% | Verified |
Triple Stack (architect + security + zero-trust) |
0.0069 | 0.0% | Verified |
🚀 1-Line Quickstart (Python / MLX / PyTorch)
Mount and hot-swap skills at runtime in Python:
import mlx_lm
import weight_skills
# 1. Load base foundation model
model, tokenizer = mlx_lm.load("luisbrel/proto-nemotron")
# 2. Mount a Weight Skill (0 prompt tokens consumed)
weight_skills.mount(model, "software-architect")
# 3. Generate with Staff-level software architectural discipline
prompt = "User: Design the bounded contexts for an event-driven payment orchestrator.\nAssistant:"
response = mlx_lm.generate(model, tokenizer, prompt=prompt, max_tokens=1024)
print(response)
# 4. Hot-swap dynamically to Security Reviewer or restore base weights
weight_skills.mount(model, "security-reviewer")
# ...
weight_skills.unmount(model)
🔬 Theoretical Lineage & Citation
The compiler mechanics, nullspace projection theorems, and benchmarks are formalized in our paper:
@article{machado2026zeroskills,
title={Zero-Skills in Context: Sub-Second Algebraic Compilation of Agent Procedures into Feed-Forward Weights},
author={Machado, Luis},
journal={Zenodo Preprints},
year={2026},
doi={10.5281/zenodo.24122804},
url={https://doi.org/10.5281/zenodo.24122804},
institution={IU International University of Applied Sciences}
}
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