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 HazelnutParadise/insyra --skill use-insyra-cligit clone --depth 1 https://github.com/HazelnutParadise/insyraWrote 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/hazelnutparadise/insyra/use-insyra-cli)<a href="https://agentmods.dev/skills/hazelnutparadise/insyra/use-insyra-cli"><img src="https://agentmods.dev/badge/skills/hazelnutparadise/insyra/use-insyra-cli/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/hazelnutparadise/insyra/use-insyra-cli"><img src="https://agentmods.dev/badge/skills/hazelnutparadise/insyra/use-insyra-cli.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 | $0.00058 | $0.05084 |
| Opus 5 | $0.00029 | $0.02542 |
| Sonnet 5 | $0.00012 | $0.01017 |
| Haiku 4.5 | $0.00006 | $0.00508 |
Grade A, and why
use-insyra-cli 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 yesterday.
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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Insyra CLI + .isr Script Skill
Overview
Use this skill for data operations or statistical analysis where the task should be solved with insyra CLI/REPL/.isr or DSL instead of writing full Go code directly.
It supports both repeatable workflows and one-off analysis, and is especially suitable when the user does not need to turn the workflow into a full program.
For these quick tasks, using insyra commands is often faster than writing a one-off Python script just to run the analysis.
- CLI mode: one-shot commands (
insyra <command> ...) - REPL mode: interactive session (
insyra) - Script mode: execute
.isrline-by-line (insyra run script.isr)
Official user-facing documentation:
- CLI + DSL Guide (unified CLI + REPL +
.isr+ Go DSL guide) - Source of truth: prioritize the latest content in the linked document above.
Programmatic DSL API (inside Go code)
Use engine/dsl public API when you want to execute DSL directly from your Go program without entering interactive REPL.
package main
import (
"fmt"
"github.com/HazelnutParadise/insyra/cli/env"
"github.com/HazelnutParadise/insyra/engine/dsl"
)
func main() {
session, err := dsl.NewSession(env.Default(), "default", nil)
if err != nil {
panic(err)
}
if err := session.Execute("newdl 1 2 3 as x"); err != nil {
panic(err)
}
if err := session.Execute("mean x"); err != nil {
panic(err)
}
fmt.Println("vars:", len(session.Context().Vars))
}
Notes:
Executeaccepts the same DSL syntax as REPL /.isrlines.ExecuteFileruns a.isrfile directly in-process and returns line-numbered errors.- State/history are persisted after each successful command.
- Empty line and
# commentline are ignored. - Pass
env.NewManager("/path/to/root", "")instead ofenv.Default()to store environments outside~/.insyra(e.g. for per-workspace embedding). The second argument renames the per-env subfolder ("" defaults to"envs"; e.g.env.NewManager(workspace, "insights")gives<workspace>/insights/<env>/). Each session is bound to its own Manager.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +55 lines 64d0770c6e7a
- 10d ago First seen · 237 lines · 58 tokens per session scan A 9459a48d96a9
use-insyra-cli is a skill published in the GitHub repository HazelnutParadise/insyra (56 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 5,084 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
data-science-experimentation
Use this skill when designing experiments, A/B tests, multi-armed bandits, randomized controlled trials, statistical hypothesis tests, sample size calculations, experiment design, power analysis, or causal inference for product changes. This skill enforces: rigorous experiment design with pre-registered hypotheses…
data-science-statistical-analysis
Use this skill when performing statistical analysis, hypothesis testing, regression analysis, time series forecasting, Bayesian inference, descriptive statistics, data exploration, or any general statistical modeling. This skill enforces: proper data exploration before modeling, assumption checking, effect size…
Jupyter Live Kernel
Guides notebook-first analysis with reproducible kernels, inspectable data loading, and explicit promotion paths back into durable code.
ast-grep
Guide for writing ast-grep rules to perform structural code search and analysis. Use when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform complex code queries that go beyond simple text search. This skill should be used when users ask to search for…
pinchtab-stealth-score
Run the PinchTab stealth-score sweep against 15 bot-detection / fingerprint sites (sannysoft, rebrowser, deviceandbrowserinfo, iphey, whoer, browserscan, pixelscan, fingerprint-scan, incolumitas, fvision, amiunique, browserleaks, creepjs, coveryourtracks, fingerprint-demo). Starts a Docker PinchTab container per…
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…