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 hajibabaie/combinatorial-optimization-skills --skill graspgit clone --depth 1 https://github.com/hajibabaie/combinatorial-optimization-skillsWrote 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/hajibabaie/combinatorial-optimization-skills/grasp)<a href="https://agentmods.dev/skills/hajibabaie/combinatorial-optimization-skills/grasp"><img src="https://agentmods.dev/badge/skills/hajibabaie/combinatorial-optimization-skills/grasp.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 | $0.00121 | $0.09124 |
| Opus 5 | $0.00060 | $0.04562 |
| Sonnet 5 | $0.00024 | $0.01825 |
| Haiku 4.5 | $0.00012 | $0.00912 |
Grade A, and why
grasp 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.
How it starts
The opening of the file, as written. The whole thing — 779 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GRASP — Greedy Randomized Adaptive Search Procedures
You are an expert in metaheuristic optimization, specializing in GRASP and multi-start methods. This skill covers greedy randomized construction with restricted candidate lists (RCL), the construction/local-search iteration, alpha calibration, reactive GRASP, bias functions, and hybridization with path relinking. Use the framework below to decide whether GRASP fits a problem, implement it correctly in vectorized numpy, and report results that withstand reviewer scrutiny.
Initial Assessment
Establish the following before proposing or writing any GRASP code:
- Greedy structure. Identify the element-by-element construction: what is an "element" (a column, an assignment, an edge), and what incremental greedy score does adding it have? GRASP requires a cheap, meaningful greedy function. If none exists, reconsider the method.
- Problem class and objective. Minimization or maximization, single objective, and which constraints the construction must respect at every partial step.
- Hard vs soft constraints. Hard constraints shape the candidate set during construction; soft constraints belong in the objective or in penalties.
- Instance size. Number of elements per solution, candidates per construction step, and whether greedy scores can be updated incrementally instead of recomputed.
- Local search. Which neighborhood improves constructed solutions, and whether O(1)/O(n) delta evaluation exists. GRASP without a competent improvement phase is just semi-greedy sampling and usually underperforms.
- Evaluation cost. Time per construction and per local-search descent determines how many restarts fit the budget.
- Exact-vs-heuristic need. If instances are small enough for a MIP solver to close the gap in the available time, solve exactly and use GRASP only for warm starts.
- Time budget and stopping rule. Wall-clock limit, iteration limit, or stagnation-based stop; restarts are independent, so the budget maps linearly to iterations.
- Quality requirement. Target gap to best-known solutions or bounds; whether a single good solution or a distribution over seeds is required.
- Data format. How instances arrive (matrix files, benchmark formats, generated) and what an independent feasibility check looks like.
- Memory features in scope. Plain GRASP is memoryless; decide up front whether reactive alpha, hashing of duplicates, or path relinking is part of the deliverable.
- Parallel resources. Independent restarts parallelize with near-linear speedup; count available cores.
- Reproducibility. One
np.random.default_rng(seed)per run; record seeds in all result tables.
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 · 779 lines · 121 tokens per session scan A c329da72cee0
grasp is a skill published in the GitHub repository hajibabaie/combinatorial-optimization-skills (7 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 9,124 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 2026-08-31.
Other skills, from other repositories
phx-deps-audit
Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
release
CONTRIBUTOR TOOL - Cut a plugin release: bump plugin.json version, finalize CHANGELOG, update README if needed, gate on make ci, commit, tag vX.Y.Z, and create the GitHub release. Use when shipping a new plugin version. NOT distributed.
session-deep-dive
Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.
catchup
Summarize and review what changed while you were away. Use after a weekend, vacation, or flight to check missed PRs, git commits, Linear tickets, and meetings — one prioritized brief, not a firehose.
brainstorm
Brainstorm Elixir/Phoenix features — explore ideas, compare approaches, gather requirements. Use when vague idea, not sure how to approach, or want to discuss before plan.
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.