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/benchflow-ai/benchflow/integral-action-designnpx skills add benchflow-ai/benchflow --skill integral-action-designgit clone --depth 1 https://github.com/benchflow-ai/benchflowWrote 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/benchflow-ai/benchflow/integral-action-design)<a href="https://agentmods.dev/skills/benchflow-ai/benchflow/integral-action-design"><img src="https://agentmods.dev/badge/skills/benchflow-ai/benchflow/integral-action-design.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 | $0.00016 | $0.00323 |
| Opus 5 | $0.00008 | $0.00161 |
| Sonnet 5 | $0.00003 | $0.00065 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
integral-action-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 5d 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.
This is a copy
100% identical to integral-action-design — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Integral Action for Offset-Free Control
Why Integral Action?
MPC with model mismatch may have steady-state error. Integral action eliminates offset by accumulating error over time.
Implementation
u_I = gamma * u_I - c_I * dt * (T - T_ref)
u_total = u_mpc + u_I
Where:
- u_I: Integral control term
- gamma: Decay factor (0.9-0.99)
- c_I: Integral gain (0.1-0.5 for web systems)
- dt: Timestep
Tuning Guidelines
Integral gain c_I:
- Too low: Slow offset correction
- Too high: Oscillations, instability
- Start with 0.1-0.3
Decay factor gamma:
- gamma = 1.0: Pure integral (may wind up)
- gamma < 1.0: Leaky integrator (safer)
- Typical: 0.9-0.99
Anti-Windup
Limit integral term to prevent windup during saturation:
u_I = np.clip(u_I, -max_integral, max_integral)
For R2R Systems
Apply integral action per tension section:
for i in range(num_sections):
u_I[i] = gamma * u_I[i] - c_I * dt * (T[i] - T_ref[i])
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.
- 5d ago First seen · 51 lines · 16 tokens per session scan A e3368f03c7c5
integral-action-design is a skill published in the GitHub repository benchflow-ai/benchflow (340 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 323 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to integral-action-design, differing in 0 lines, and is treated as a copy.
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