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 skills add OutlineDriven/outline-driven-development --skill automatic-cybernetic-flow-designgit clone --depth 1 https://github.com/OutlineDriven/outline-driven-developmentWrote 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/outlinedriven/outline-driven-development/automatic-cybernetic-flow-design)<a href="https://agentmods.dev/skills/outlinedriven/outline-driven-development/automatic-cybernetic-flow-design"><img src="https://agentmods.dev/badge/skills/outlinedriven/outline-driven-development/automatic-cybernetic-flow-design.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00032 | $0.00881 |
| Opus 5 | $0.00016 | $0.00441 |
| Sonnet 5 | $0.00006 | $0.00176 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
automatic-cybernetic-flow-design 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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- automatic-cybernetic-flow-design — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automatic cybernetic flow design
Contract
| Field | Bound contract |
|---|---|
| Trigger | User wants a cybernetic flow design document specifying sensors, actuators, feedback paths, delays, and oscillation risk for an interactive system. |
| Authority | Reversible local: writes only the single named local design document; rollback is deleting or overwriting that file. No remote mutation. |
| Side effect | A cybernetic flow design document at the named local path. No source code, runtime, or remote mutation. |
| Done | A design document with sections for sensors, actuators, feedback, delay, oscillation, a wiring diagram, and a dynamic-routing note, written to the named local file. |
Inputs
Required:
- The interactive system description: what state it controls, what it observes, and what actions it can take.
- The primary control objective: the state the system tries to hold or steer.
- Observable signals (sensors) and available actions (actuators).
- Per-path delay classifications: fixed, variable, or bounded with the bound.
- The named local output file path.
Optional:
- Known latency budgets, stability requirements, existing feedback paths, or constraints on sensors and actuators.
- Gain and timing data for oscillation analysis. If absent, the path is marked under-specified.
Procedure
-
Intake and validate the system description and control objective. Confirm the system is named, the control objective is stated, and the output file path is supplied. Done when: the system, control objective, and output file path are confirmed.
-
Enumerate sensors and actuators. For each sensor, name the quantity measured and its source. For each actuator, name the effect and its range. State the count of each. Done when: every sensor and actuator is listed with its quantity, source or effect, and range.
-
Map feedback paths. For each path, specify which sensor(s) drive which actuator(s), the comparison that generates the error signal, and the direction of correction. Allow many-to-one and coordinated relationships; a single sensor may drive multiple actuators and multiple sensors may converge on one actuator. Done when: every feedback path names its sensors, actuators, error comparison, and correction direction.
What ships with it
1 file 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.
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.
- yesterday Changed · -26 tokens per session 530a6789dbfb
- 4d ago First seen · 54 lines · 58 tokens per session scan A e9a3a8224151
automatic-cybernetic-flow-design is a skill published in the GitHub repository OutlineDriven/outline-driven-development (52 stars, last pushed 2d ago), licensed Apache-2.0. It adds 32 tokens to every session and 881 once invoked, about $0.0002 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 2026-09-03.
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publish-branch
Use when asked to publish the checked-out branch: commit and push it on whatever branch it is, the default branch included. Not for creating branches, PRs, force pushes, or pushing any other branch; when the request excludes the default branch, use commit-push-current.
drill
Use when a concept needs practising rather than explaining: run a scaffolded exercise from worked example to independent problem, quiz the learner, run spaced recall over what they cleared, or probe for the gaps blocking what they want next. For explanation, use explain-concept; for an end-to-end build, use capstone.