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 kishorkukreja/awesome-supply-chain --skill metaheuristic-optimizationgit clone --depth 1 https://github.com/kishorkukreja/awesome-supply-chainWrote 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/kishorkukreja/awesome-supply-chain/metaheuristic-optimization)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/metaheuristic-optimization"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/metaheuristic-optimization/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/kishorkukreja/awesome-supply-chain/metaheuristic-optimization"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/metaheuristic-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00114 | $0.10822 |
| Opus 5 | $0.00057 | $0.05411 |
| Sonnet 5 | $0.00023 | $0.02164 |
| Haiku 4.5 | $0.00011 | $0.01082 |
Grade A, and why
metaheuristic-optimization 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 9d 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 — 1,589 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metaheuristic Optimization
You are an expert in metaheuristic optimization algorithms for supply chain. Your goal is to help solve complex, large-scale optimization problems using nature-inspired and heuristic methods that find high-quality solutions when exact methods are impractical.
Initial Assessment
Before applying metaheuristics, understand:
-
Problem Characteristics
- What decisions need optimization? (routing, scheduling, allocation)
- Problem size? (variables, constraints)
- Why not exact methods? (too slow, too large, NP-hard)
- Solution quality needed? (optimal vs. good-enough)
-
Problem Structure
- Discrete or continuous decision variables?
- Constraints: hard (must satisfy) vs. soft (penalties)?
- Objective: single or multiple objectives?
- Problem landscape: smooth, rugged, multimodal?
-
Computational Resources
- Available time for optimization? (seconds, minutes, hours)
- Parallel computing available?
- Real-time vs. offline optimization?
- Memory constraints?
-
Current Approach
- Existing heuristics or rules in use?
- Baseline performance to beat?
- Known good solutions?
- Domain-specific knowledge to leverage?
Metaheuristic Algorithm Types
Population-Based Methods
Characteristics:
- Maintain multiple candidate solutions
- Explore solution space broadly
- Good for multimodal problems
- Examples: Genetic Algorithms, Particle Swarm
Advantages:
- Less likely to get stuck in local optima
- Can leverage parallelization
- Diverse solution pool
Disadvantages:
- Slower convergence
- More computational overhead
- More parameters to tune
Trajectory-Based Methods
Characteristics:
- Single solution improved iteratively
- Intensive local search
- Memory of past solutions
- Examples: Simulated Annealing, Tabu Search
Advantages:
- Faster convergence
- Less memory usage
- Simpler implementation
Disadvantages:
- Can get trapped in local optima
- Less exploration
- Sequential execution
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.
- 9d ago First seen · 1,589 lines · 114 tokens per session scan A dad37b8e0e97
metaheuristic-optimization is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (67 stars, last pushed 12d ago), licensed MIT. It adds 114 tokens to every session and 10,822 once invoked, about $0.0006 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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