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 locational-marginal-pricesgit 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/locational-marginal-prices)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/locational-marginal-prices"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/locational-marginal-prices/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/locational-marginal-prices"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/locational-marginal-prices.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 | $0.00046 | $0.01207 |
| Opus 5 | $0.00023 | $0.00603 |
| Sonnet 5 | $0.00009 | $0.00241 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
locational-marginal-prices 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 8d 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:
- locational-marginal-prices — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Locational Marginal Prices (LMPs)
LMPs are the marginal cost of serving one additional MW of load at each bus. In optimization terms, they are the dual values (shadow prices) of the nodal power balance constraints.
LMP Extraction from CVXPY
To extract LMPs, you must:
- Store references to the balance constraints
- Solve the problem
- Read the dual values after solving
import cvxpy as cp
# Store balance constraints separately for dual extraction
balance_constraints = []
for i in range(n_bus):
pg_at_bus = sum(Pg[g] for g in range(n_gen) if gen_bus[g] == i)
pd = buses[i, 2] / baseMVA
# Create constraint and store reference
balance_con = pg_at_bus - pd == B[i, :] @ theta
balance_constraints.append(balance_con)
constraints.append(balance_con)
# Solve
prob = cp.Problem(cp.Minimize(cost), constraints)
prob.solve(solver=cp.CLARABEL)
# Extract LMPs from duals
lmp_by_bus = []
for i in range(n_bus):
bus_num = int(buses[i, 0])
dual_val = balance_constraints[i].dual_value
# Scale: constraint is in per-unit, multiply by baseMVA to get $/MWh
lmp = float(dual_val) * baseMVA if dual_val is not None else 0.0
lmp_by_bus.append({
"bus": bus_num,
"lmp_dollars_per_MWh": round(lmp, 2)
})
LMP Sign Convention
For a balance constraint written as generation - load == net_export:
- Positive LMP: Increasing load at that bus increases total cost (typical case)
- Negative LMP: Increasing load at that bus decreases total cost
Negative LMPs commonly occur when:
- Cheap generation is trapped behind a congested line (can't export power)
- Adding load at that bus relieves congestion by consuming local excess generation
- The magnitude can be very large in heavily congested networks (thousands of $/MWh)
Negative LMPs are physically valid and expected in congested systems — they are not errors.
Reserve Clearing Price
The reserve MCP is the dual of the system reserve requirement constraint:
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.
- 8d ago First seen · 140 lines · 46 tokens per session scan A c48851e863af
locational-marginal-prices is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 1,207 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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