SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill pid-controllergit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/skillsbench/pid-controller)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/pid-controller"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/pid-controller/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/pid-controller"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/pid-controller.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00039 | $0.00640 |
| Opus 5.5 | $0.00016 | $0.00256 |
| Sonnet 5.5 | $0.00008 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
pid-controller 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- pid-controller — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PID Controller Implementation
Overview
A PID (Proportional-Integral-Derivative) controller is a feedback control mechanism used in industrial control systems. It continuously calculates an error value and applies a correction based on proportional, integral, and derivative terms.
Control Law
output = Kp * error + Ki * integral(error) + Kd * derivative(error)
Where:
error= setpoint - measured_valueKp= proportional gain (reacts to current error)Ki= integral gain (reacts to accumulated error)Kd= derivative gain (reacts to rate of change)
Discrete-Time Implementation
class PIDController:
def __init__(self, kp, ki, kd, output_min=None, output_max=None):
self.kp = kp
self.ki = ki
self.kd = kd
self.output_min = output_min
self.output_max = output_max
self.integral = 0.0
self.prev_error = 0.0
def reset(self):
"""Clear controller state."""
self.integral = 0.0
self.prev_error = 0.0
def compute(self, error, dt):
"""Compute control output given error and timestep."""
# Proportional term
p_term = self.kp * error
# Integral term
self.integral += error * dt
i_term = self.ki * self.integral
# Derivative term
derivative = (error - self.prev_error) / dt if dt > 0 else 0.0
d_term = self.kd * derivative
self.prev_error = error
# Total output
output = p_term + i_term + d_term
# Output clamping (optional)
if self.output_min is not None:
output = max(output, self.output_min)
if self.output_max is not None:
output = min(output, self.output_max)
return output
Anti-Windup
Integral windup occurs when output saturates but integral keeps accumulating. Solutions:
- Clamping: Limit integral term magnitude
- Conditional Integration: Only integrate when not saturated
- Back-calculation: Reduce integral when output is clamped
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.
- 1mo ago First seen · 88 lines · 39 tokens per session scan A d274422849bf
pid-controller is a skill published in the GitHub repository benchflow-ai/skillsbench (1,865 stars, last pushed 9d ago), licensed Apache-2.0. It adds 39 tokens to every session and 640 once invoked, about $0.0002 per session on Opus 5.5. 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.
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