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 legendtkl/agentic-skill-router --skill skill-042git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/legendtkl/agentic-skill-router/skill-042)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-042"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-042/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/legendtkl/agentic-skill-router/skill-042"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-042.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 | $0.00054 | $0.01089 |
| Opus 5.5 | $0.00022 | $0.00436 |
| Sonnet 5.5 | $0.00011 | $0.00218 |
| Haiku 4.5 | $0.00005 | $0.00109 |
Grade A, and why
skill-042 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 1mo 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.
This is a copy
89% identical to jax-skills — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements for Outputs
General Guidelines
Arrays
- All arrays MUST be compatible with JAX (
jnp.array) or convertible from Python lists. - Use
.npy,.npz, JSON, or pickle for saving arrays.
Operations
- Validate input types and shapes for all functions.
- Maintain numerical stability for all operations.
- Provide meaningful error messages for unsupported operations or invalid inputs.
JAX Skills
1. Loading and Saving Arrays
load(path)
Description: Load a JAX-compatible array from a file. Supports .npy and .npz.
Parameters:
path(str): Path to the input file.
Returns: JAX array or dict of arrays if .npz.
import jax_skills as jx
arr = jx.load("data.npy")
arr_dict = jx.load("data.npz")
save(data, path)
Description: Save a JAX array or Python array to .npy.
Parameters:
- data (array): Array to save.
- path (str): File path to save.
jx.save(arr, "output.npy")
2. Map and Reduce Operations
map_op(array, op)
Description: Apply elementwise operations on an array using JAX vmap. Parameters:
- array (array): Input array.
- op (str): Operation name ("square" supported).
squared = jx.map_op(arr, "square")
reduce_op(array, op, axis)
Description: Reduce array along a given axis. Parameters:
- array (array): Input array.
- op (str): Operation name ("mean" supported).
- axis (int): Axis along which to reduce.
mean_vals = jx.reduce_op(arr, "mean", axis=0)
3. Gradients and Optimization
logistic_grad(x, y, w)
Description: Compute the gradient of logistic loss with respect to weights. Parameters:
- x (array): Input features.
- y (array): Labels.
- w (array): Weight vector.
grad_w = jx.logistic_grad(X_train, y_train, w_init)
Notes:
- Uses jax.grad for automatic differentiation.
- Logistic loss: mean(log(1 + exp(-y * (x @ w)))).
4. Recurrent Scan
rnn_scan(seq, Wx, Wh, b)
Description: Apply an RNN-style scan over a sequence using JAX lax.scan. Parameters:
- seq (array): Input sequence.
- Wx (array): Input-to-hidden weight matrix.
- Wh (array): Hidden-to-hidden weight matrix.
- b (array): Bias vector.
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.
- 1mo ago First seen · 152 lines · 54 tokens per session scan A 99e2dbca60dc
skill-042 is a skill published in the GitHub repository legendtkl/agentic-skill-router (6 stars, last pushed 4mo ago), licensed MIT. It adds 54 tokens to every session and 1,089 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to jax-skills, differing in 3 lines, and is treated as a copy.
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