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/broomva/skills/agentic-control-kernelnpx skills add broomva/skills --skill agentic-control-kernelgit clone --depth 1 https://github.com/broomva/skillsWrote 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/broomva/skills/agentic-control-kernel)<a href="https://agentmods.dev/skills/broomva/skills/agentic-control-kernel"><img src="https://agentmods.dev/badge/skills/broomva/skills/agentic-control-kernel.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 | $0.00211 | $0.01826 |
| Opus 5 | $0.00105 | $0.00913 |
| Sonnet 5 | $0.00042 | $0.00365 |
| Haiku 4.5 | $0.00021 | $0.00183 |
Grade A, and why
agentic-control-kernel 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 3d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Control Kernel
A purely knowledge-based metalayer that unifies six subsystems into a single installable skill for any project:
| Layer | Source / Crates | Role |
|---|---|---|
| Governance | control-metalayer-loop | Setpoints, sensors, gates, policy, profiles |
| Improvement | autoany_core + autoany-aios + autoany-lago |
EGRI microkernel, Arcan execution, Lago ledger |
| Orchestration | symphony-orchestrator + symphony-arcan |
Poll/dispatch/worker/reconcile via Arcan HTTP |
| Runtime | Life (arcan, lago, autonomic, praxis, spaces) |
Agent sessions, event journal, homeostasis, networking |
| Protocol | aios-protocol |
Canonical types — shared vocabulary across all crates |
| Episodic Memory | knowledge-graph-memory | Conversation logs -> Obsidian bridge |
| Consciousness | agent-consciousness | Three-substrate persistent context |
| QA/Actuation | gstack | Headless browser, workflow skills |
| Control Kernel | this skill | Plant interface, safety shields, typed schemas, multi-rate hierarchy |
Core Law
Do not grant an agent more mutation freedom than your evaluator can reliably judge. In control terms: do not let the LLM's action space exceed what your runtime monitors, safety filters, and evaluators can certify.
Quick Start
1. Bootstrap a project
python3 scripts/control_kernel_init.py <repo-path> [--profile governed] [--runtime arcan] [--ledger lago]
This installs into the target repo:
.control/policy.yaml— control-systems-aware setpointsschemas/— state, action, trace, evaluator JSON schemasMETALAYER.md— control loop definition with plant/shield/estimator sections- Harness gates wired to
make smoke,make check,make control-audit
2. Define the plant interface
Edit .control/plant.yaml with typed state and action schemas for your system.
See references/plant-interface.md for the full API spec.
3. Wire safety shields
What ships with it
21 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/templates/problem-spec.control.yaml 2.7 KB
- references/architecture.md 6.3 KB
- references/consciousness-stack.md 4.8 KB
- references/deep-research-report.md 41 KB
- references/egri-for-controllers.md 5.9 KB
- references/failure-modes.md 4.8 KB
- references/integration-map.md 2.4 KB
- references/multi-rate-hierarchy.md 3.3 KB
- references/orchestration-patterns.md 5.4 KB
- references/plant-interface.md 5.5 KB
- references/safety-shields.md 4.9 KB
- references/world-models.md 3.4 KB
- schemas/.gitkeep 0 B
- schemas/action.schema.json 2.1 KB
- schemas/egri-event.schema.json 2.5 KB
- schemas/evaluator.schema.json 3.4 KB
- schemas/state.schema.json 2.7 KB
- schemas/trace.schema.json 4.2 KB
- scripts/control_kernel_init.py 9.0 KB runs code
- scripts/conversation-bridge-hook.sh 783 B runs code
- scripts/conversation-history.py 39 KB runs code
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.
- 3d ago First seen · 145 lines · 211 tokens per session scan A fa1ed3d4a169
agentic-control-kernel is a skill published in the GitHub repository broomva/skills (3 stars, last pushed 3d ago), licensed MIT. It adds 211 tokens to every session and 1,826 once invoked, about $0.0011 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 2026-08-31.
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EU AI Act High-Risk Obligations
PASS/FAIL/N/A checklist for the high-risk AI obligations under Articles 8-15 of the EU AI Act, plus the quality management system, conformity assessment, registration, post-market monitoring, and incident reporting.
AWS Naming And Tagging
AWS naming convention and mandatory tags — resource-type abbreviations, region codes, and owner/cost-center/environment/workload tags for cost attribution.
Azure Naming And Tagging
CAF-aligned Azure naming convention and mandatory tags — resource-type abbreviations, region codes, and owner/cost-center/environment/workload tags for cost attribution.
EU AI Act Risk Classification
Decision tree for classifying an AI system into the EU AI Act risk tier (unacceptable/high/limited/minimal) and identifying the actor role (provider/deployer/importer/distributor).
EU AI Act Transparency
Checklist for the Article 50 transparency obligations — chatbot AI disclosure, machine-readable marking of synthetic content, emotion-recognition/biometric-categorisation notice, and deepfake/public-interest-text labelling.
governance
MyACE project-specific governance rules. Data classification, ADR process, documentation maintenance, dependency compliance, and audit trail. Load at session start for any work on this project.