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 glebis/claude-skills --skill redgit clone --depth 1 https://github.com/glebis/claude-skillsWrote 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/glebis/claude-skills/red)<a href="https://agentmods.dev/skills/glebis/claude-skills/red"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/red/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/glebis/claude-skills/red"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/red.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 | $0.00152 | $0.00871 |
| Opus 5.5 | $0.00061 | $0.00348 |
| Sonnet 5.5 | $0.00030 | $0.00174 |
| Haiku 4.5 | $0.00015 | $0.00087 |
Grade A, and why
red 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
confide:red — residual re-identification risk check
A defensive audit of YOUR OWN already-redacted output. It does not score against ground truth and is not a benchmark. It surfaces, qualitatively, what an attacker could still do — mapped to GDPR Art-29: singling-out, linkability, inference.
GUARDRAILS — read before running
- Run only on the user's own redacted output. If asked to de-anonymize or re-identify third-party / non-consented data, refuse.
- Report risk categories and counts only — never produce a step-by-step re-identification recipe or guess the hidden values.
- Local attacker by default. Enable the cloud/LLM inference probe (
--inference) only on synthetic or explicitly consented data. - Absence of a finding ≠ safety. A weak local detector/attacker is a FLOOR, not a ceiling. Always tell the user human review is still required.
- This pairs with confide:anon — run
redafter redacting, on the redacted file.
What it checks
- Singling-out (deterministic, offline — the load-bearing signal): re-run
detect_regex(+detect_natashaif available) on the redacted text. Anything they still find is a surviving identifier the redaction missed. Counts by type. - Linkability (multi-file): given a folder, compare every file pair for shared surviving quasi-identifiers and flag potentially linkable pairs (count + types only).
- Inference (LLM, optional, opt-in): prompt the local attacker model
(
cfg.red_attacker_model) for the attribute categories it could still infer (profession, location type, age band, …). Degrades gracefully if no model. WARN the user it under-reports (floor, not ceiling).
Risk tier rule
- HIGH — any DIRECT identifier survives (EMAIL, PHONE, URL, ID, PERSON).
- MEDIUM — only QUASI identifiers survive (LOCATION, ORG, DATE, AGE, PROFESSION, MEDICATION), or linkable pairs exist across files.
- LOW — no surviving identifiers found (still NOT a guarantee).
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 · 68 lines · 152 tokens per session scan A 4d61c00007a7
red is a skill published in the GitHub repository glebis/claude-skills (409 stars, last pushed yesterday), licensed MIT. It adds 152 tokens to every session and 871 once invoked, about $0.0006 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 2026-08-30.
red compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| redglebis/claude-skills | Skill | 409 | yesterday | MIT | A | 152 | Claude Code |
| tastemakercodeswithroh/tastemaker | Skill | 524 | 26d ago | MIT | A | 202 | Claude Code |
| fdesuboss87/FDEOps | Skill | 857 | 17d ago | MIT | A | 125 | Claude Code, Codex |
| board-memosuboss87/FDEOps | Skill | 857 | 17d ago | MIT | A | 31 | Claude Code, Codex |
| briefsuboss87/FDEOps | Skill | 857 | 17d ago | MIT | A | 32 | Claude Code, Codex |
| business-casesuboss87/FDEOps | Skill | 857 | 17d ago | MIT | A | 29 | Claude Code, Codex |
| earn-trustsuboss87/FDEOps | Skill | 857 | 17d ago | MIT | A | 26 | Claude Code, Codex |