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 Terryc21/radar-suite --skill radar-suite-axis-classificationgit clone --depth 1 https://github.com/Terryc21/radar-suiteWrote 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/terryc21/radar-suite/radar-suite-axis-classification)<a href="https://agentmods.dev/skills/terryc21/radar-suite/radar-suite-axis-classification"><img src="https://agentmods.dev/badge/skills/terryc21/radar-suite/radar-suite-axis-classification/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/terryc21/radar-suite/radar-suite-axis-classification"><img src="https://agentmods.dev/badge/skills/terryc21/radar-suite/radar-suite-axis-classification.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 Excessive Agency · line 50 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00077 | $0.06141 |
| Opus 5 | $0.00039 | $0.03070 |
| Sonnet 5 | $0.00015 | $0.01228 |
| Haiku 4.5 | $0.00008 | $0.00614 |
Grade A, and why
radar-suite-axis-classification 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 — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Radar Suite — Axis Classification Framework
Every radar in the suite invokes this skill before emitting findings. This is the verification gate and the coaching engine. Findings that do not pass the gate are rejected.
Inheritance Note (this is a framework skill, not an audit skill)
This skill inherits radar-suite-core.md for shared schema definitions (Issue Rating Table format, Handoff YAML schema) but does NOT run core's interactive protocols (Session Setup, Pre-Scan Startup, Known-Intentional Suppression, Pattern Reintroduction Detection). Those protocols apply within the audit skills that invoke this framework, not at this skill's level.
This skill is invoked programmatically by audit skills during finding emission; it never runs standalone. There is no /radar-suite-axis-classification slash command, no setup interview, no phases, no progress banner. A radar reads this skill's spec, follows the Invocation Protocol below to classify and coach its candidate findings, then writes them to its own handoff YAML with the required axis + coaching fields.
If you're reading this skill expecting a runnable command, you want a sibling radar instead (/data-model-radar, /ui-path-radar, /roundtrip-radar, /time-bomb-radar, /ui-enhancer-radar, /capstone-radar).
What This Skill Does
Three things, in order:
- Classify every candidate finding on three axes (with axis_3 splitting into two sub-labels, giving four total schema values — see § The Three Axes below): does it break user-visible behavior, is it correct code that is hard to read, or is it dead or unjustified code?
- Verify the classification against a checklist of concrete evidence checks before the finding can be emitted.
- Coach with a mandatory
better_approachsection that cites a real file:line pattern from the audited codebase (not generic advice).
Any radar can invoke this skill. The skill itself does not scan code directly — it provides the framework, the checklist, and the schema gate that each radar uses before writing its handoff YAML.
What ships with it
4 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.
- 9d ago First seen · 456 lines · 77 tokens per session scan A 4314e3b16e5a
radar-suite-axis-classification is a skill published in the GitHub repository Terryc21/radar-suite (20 stars, last pushed 11d ago), licensed Apache-2.0. It adds 77 tokens to every session and 6,141 once invoked, about $0.0004 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
workflow-audit
Systematic UI workflow auditing for SwiftUI applications. Discovers entry points, traces user flows, detects dead ends and broken promises, audits data wiring, evaluates from user perspective. Triggers: "workflow audit", "audit flows", "find dead ends", "check navigation".
plan
Epic decomposition into trackable, right-sized tasks. Three modes — audit-aware (codebase-audit reports), workflow-audit-aware (handoff.yaml with pre-rated findings), standalone (from scratch). Light convention scanning for projects without CLAUDE.md.
ttb-skill-audit
Code audits for TTBaseUIKit apps: performance, accessibility, localization. FCR compliance scoring.
rust-intel
Hard rules for writing Rust in code that already compiles and passes tests but is silently broken, slow, or semver-fragile. Load this BEFORE writing any Rust code. Targets bugs that survive rustc, clippy, and cargo test but fail in production or rot the codebase. Covers async, unsafe, FFI, concurrency, crypto…
swift-review
Review Swift/iOS code: SwiftUI, Combine, UIKit, App Store guidelines and iOS architecture.
rust-intel
Hard rules for writing Rust in code that already compiles and passes tests but is silently broken, slow, or semver-fragile. Load this BEFORE writing any Rust code. Targets bugs that survive rustc, clippy, and cargo test but fail in production or rot the codebase. Covers async, unsafe, FFI, concurrency, crypto…