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 cintia09/CodeNook --skill device-detectgit clone --depth 1 https://github.com/cintia09/CodeNookWrote 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/cintia09/codenook/device-detect)<a href="https://agentmods.dev/skills/cintia09/codenook/device-detect"><img src="https://agentmods.dev/badge/skills/cintia09/codenook/device-detect/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/cintia09/codenook/device-detect"><img src="https://agentmods.dev/badge/skills/cintia09/codenook/device-detect.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 | $0.00003 | $0.00775 |
| Opus 5.5 | $0.00001 | $0.00310 |
| Sonnet 5.5 | $0.00001 | $0.00155 |
| Haiku 4.5 | $0.00000 | $0.00077 |
Grade A, and why
device-detect 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 2mo 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
device-detect — plugin-shipped skill (development plugin)
Role
Enumerate every file / directory under <target-dir> that might be
an execution-environment hint, classify each by a generic bucket, and
emit a JSON envelope. The skill is deliberately liberal — it does
not decide what kind of device or simulator the target is, only what
markers exist. Classification is the test-planner's job, and it does
that by:
- Searching workspace memory for a matching environment record:
<codenook> knowledge search "test-environment target=<basename>". - If memory is silent, asking the user to identify the environment.
CLI
detect.py --target-dir <dir> [--json]
Detection (generic markers, no device-type hard-coding)
Marker pattern under <target> |
Bucket |
|---|---|
pyproject.toml / setup.py / pytest.ini |
local-python |
package.json |
local-node |
go.mod |
local-go |
Any .codenook-test-env* / .test-env* file |
recorded-env |
Any dot-config file matching *.cfg / *.toml / *.yaml at the root that does not match a known software runner |
unknown-config |
Any scripts/run-*-tests.sh |
custom-runner |
| (none of the above) | unknown |
The buckets are intentionally generic (local-*, recorded-env,
unknown-config, custom-runner, unknown) — the specific device
or simulator type (ADB / QEMU / SSH-into-board / JTAG / network
fixture / …) is never decided here. That decision belongs to the
calling role + memory + user.
JSON envelope (--json)
{
"target": "src/",
"buckets": ["local-python", "custom-runner"],
"primary": "local-python",
"markers": {
"local-python": ["pyproject.toml"],
"custom-runner": ["scripts/run-board-tests.sh"]
},
"memory_search_hint": "test-environment target=foo"
}
What ships with it
1 file 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.
- 2mo ago First seen · 84 lines · 3 tokens per session scan A f4e7377d333a
device-detect is a skill published in the GitHub repository cintia09/CodeNook (5 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 775 once invoked, about $0.0000 per session on Opus 5.5. 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.
device-detect compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| device-detectcintia09/CodeNook | Skill | 5 | 1mo ago | MIT | A | 3 | Claude Code, Codex |
| incident-response-skillagentfront/frontmcp | Skill | 148 | today | Apache-2.0 | A | 24 | Claude Code, Codex |
| directory-based-skillagentfront/frontmcp | Skill | 148 | today | Apache-2.0 | A | 28 | Claude Code, Codex |
| parameterized-skillagentfront/frontmcp | Skill | 148 | today | Apache-2.0 | A | 21 | Claude Code, Codex |
| basic-inline-skillagentfront/frontmcp | Skill | 148 | today | Apache-2.0 | A | 19 | Claude Code, Codex |
| project-scaffoldchainlesschain/chainlesschain | Skill | 11 | today | none | A | 25 | Claude Code |
| handler-test-skillchainlesschain/chainlesschain | Skill | 11 | today | none | A | 8 | Claude Code |