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/kucherenko/petropowers/midstreamnpx skills add kucherenko/petropowers --skill midstreamgit clone --depth 1 https://github.com/kucherenko/petropowersWrote 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/kucherenko/petropowers/midstream)<a href="https://agentmods.dev/skills/kucherenko/petropowers/midstream"><img src="https://agentmods.dev/badge/skills/kucherenko/petropowers/midstream.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 5.1 | $0.00013 | $0.02092 |
| Opus 5 | $0.00006 | $0.01046 |
| Sonnet 5 | $0.00003 | $0.00418 |
| Haiku 4.5 | $0.00001 | $0.00209 |
Grade A, and why
midstream 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.
How it starts
The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Midstream Pipeline
Guide AI agents through pipeline transportation and storage operations.
Purpose
Support pipeline engineers in ensuring safe and efficient hydrocarbon transportation and storage.
Roles
- Pipeline Engineer - Pipeline design, hydraulics, materials
- Operations Manager - Daily operations, scheduling, nominations
- Integrity Engineer - Inspection, corrosion management, repairs
Data Types
| Data | Source | Frequency |
|---|---|---|
| Flow rates | SCADA | Real-time (1-min) |
| Pressure | SCADA | Real-time (1-min) |
| Temperature | SCADA | Real-time |
| ILI (Inline Inspection) | Smart pig | 5-year cycle |
| Leak detection | CPM/DDS | Real-time (continuous) |
| Product quality | Lab | Batch/sample |
Workflow
Phase 1: Transport Scheduling
- Receive nominations (shipper requests)
- Check pipeline capacity
- Schedule batches/products
- Optimize pump/compressor runs
- Confirm deliveries
Phase 2: Real-Time Monitoring
- Monitor pressure/flow at key points
- Track batch locations
- Detect anomalies (leaks, theft)
- Adjust operations as needed
- Communicate with control center
Phase 3: Integrity Management
- Plan ILI (smart pigging)
- Analyze inspection data
- Identify defects (corrosion, cracks, dents)
- Prioritize repairs
- Verify repairs
Phase 4: Leak Detection
- Monitor CPM (Computational Pipeline Monitoring)
- Analyze mass balance
- Check pressure/flow deviations
- Investigate anomalies
- Emergency response if confirmed
Domain Tasks
These tasks are handled by this skill:
- Pipeline hydraulics calculation
- Leak detection analysis
- ILI data interpretation
- Integrity assessment
Software Tasks
These tasks invoke petropowers:oil-gas-delegation:
- SCADA monitoring dashboard
- Pipeline scheduling system
- ILI data management platform
- Leak detection alerting system
Example Workflows
Pipeline Hydraulics
import math
def pressure_drop_liquid(flow_bpd, diameter_in, length_ft, viscosity_cp, density_api):
"""Calculate pressure drop for liquid pipeline (simplified)"""
# Convert units
q_bpm = flow_bpd / 1440 # bpd to bpm
d_ft = diameter_in / 12
# Velocity (ft/s)
area_sqft = math.pi * d_ft**2 / 4
velocity = q_bpm / 7.48 / area_sqft # ft/s
# Reynolds number
rho = 62.4 * (141.5 / (density_api + 131.5)) # lb/ft³
re = rho * velocity * d_ft / (viscosity_cp * 0.000672)
# Friction factor (Blasius for turbulent)
f = 0.079 / re**0.25 if re > 4000 else 64 / re
# Pressure drop (psi)
delta_p = 2 * f * (length_ft / d_ft) * rho * velocity**2 / (32.2 * 144)
return delta_p
# Example: 12" pipeline, 50,000 bpd, 100 miles
d_psi = pressure_drop_liquid(
flow_bpd=50000,
diameter_in=12,
length_ft=100 * 5280,
viscosity_cp=5,
density_api=35
)
print(f"Pressure drop: {d_psi:.0f} psi")
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 · 295 lines · 13 tokens per session scan A 1c3e09905df1
midstream is a skill published in the GitHub repository kucherenko/petropowers (11 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 2,092 once invoked, about $0.0001 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 2026-08-30.
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