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 junbolian/AdmitOR --skill tsp_mtz_formulationgit clone --depth 1 https://github.com/junbolian/AdmitORWrote 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/junbolian/admitor/tsp_mtz_formulation)<a href="https://agentmods.dev/skills/junbolian/admitor/tsp_mtz_formulation"><img src="https://agentmods.dev/badge/skills/junbolian/admitor/tsp_mtz_formulation/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/junbolian/admitor/tsp_mtz_formulation"><img src="https://agentmods.dev/badge/skills/junbolian/admitor/tsp_mtz_formulation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00041 | $0.02808 |
| Opus 5 | $0.00020 | $0.01404 |
| Sonnet 5 | $0.00008 | $0.00562 |
| Haiku 4.5 | $0.00004 | $0.00281 |
Grade A, and why
TSP_MTZ_Formulation_Skill 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 today.
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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow 1 (Integer Programming with MTZ)
Modeling stage
Strategy Overview
This workflow models the TSP as a Mixed-Integer Program (MIP) using binary arc selection variables and integer position variables for subtour elimination via the Miller-Tucker-Zemlin (MTZ) constraints. It is a direct, portable formulation suitable for general-purpose MIP/CP-SAT solvers.
Step 1 - Define Sets and Parameters
- Define a set of nodes to be visited, typically indexed from 0 to N-1.
- Define a cost matrix parameter,
cost[i][j], representing the travel cost from node i to node j. Ensure the matrix is square and self-loop costs are set to a large value or zero as appropriate.
Step 2 - Create Decision Variables
- Create binary decision variables
x[i][j]for each ordered pair of distinct nodes.x[i][j] = 1indicates the tour includes the arc from i to j. - Create integer position variables
u[i]for each node, representing its order in the tour. Bound them appropriately (e.g., 0 to N-1).
Step 3 - Formulate Degree Constraints
- For each node i, add a constraint ensuring exactly one outgoing arc:
sum_{j != i} x[i][j] == 1. - For each node j, add a constraint ensuring exactly one incoming arc:
sum_{i != j} x[i][j] == 1.
Step 4 - Implement MTZ Subtour Elimination
- For all pairs of nodes i, j (where i and j are not the designated start node), add the MTZ constraint:
u[i] - u[j] + N * x[i][j] <= N - 1. - Fix the position of the start node to break symmetry:
u[start_node] = 0.
Step 5 - Define the Objective Function
- Formulate the objective to minimize total travel cost:
minimize sum_{i,j} cost[i][j] * x[i][j].
Formulation Template
{
"sets": [
"nodes: list of node indices (e.g., [0, 1, ..., N-1])"
],
"parameters": [
"cost: a |nodes| x |nodes| matrix of travel costs"
],
"decision_variables": [
"x[i][j]: binary, 1 if arc i->j is used",
"u[i]: integer, position of node i in the tour"
],
"objective": {
"sense": "min",
"expression": "sum_{i in nodes} sum_{j in nodes} cost[i][j] * x[i][j]"
},
"constraints": [
"single_departure(i): sum_{j in nodes, j != i} x[i][j] == 1, for all i",
"single_arrival(j): sum_{i in nodes, i != j} x[i][j] == 1, for all j",
"mtz(i,j): u[i] - u[j] + |nodes| * x[i][j] <= |nodes| - 1, for all i,j where i != start_node, j != start_node, i != j",
"start_position: u[start_node] == 0",
"no_self_loop: x[i][i] == 0, for all i"
]
}
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.
- today First seen · 234 lines · 41 tokens per session scan A e4b62c11c30f
TSP_MTZ_Formulation_Skill is a skill published in the GitHub repository junbolian/AdmitOR (41 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 2,808 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 2026-09-17.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…