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 tellang/triflux --skill tfx-analysisgit clone --depth 1 https://github.com/tellang/trifluxWrote 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/tellang/triflux/tfx-analysis)<a href="https://agentmods.dev/skills/tellang/triflux/tfx-analysis"><img src="https://agentmods.dev/badge/skills/tellang/triflux/tfx-analysis/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/tellang/triflux/tfx-analysis"><img src="https://agentmods.dev/badge/skills/tellang/triflux/tfx-analysis.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 | $0.00076 | $0.01989 |
| Opus 5 | $0.00038 | $0.00994 |
| Sonnet 5 | $0.00015 | $0.00398 |
| Haiku 4.5 | $0.00008 | $0.00199 |
Grade A, and why
tfx-analysis scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf http://127.0.0.1:27888/status >/dev/null && \ How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tfx-analysis — Code/Architecture Analysis (Deep by Default)
ARGUMENTS 처리: ARGUMENTS 에
--quick포함 → Quick 모드. 그 외 → Deep 모드 (기본).
AI makes completeness near-free. 기본은 Claude(아키텍처) + Codex(보안/성능) + Antigravity(UX/문서화) Tri-Debate 합의. 빠른 단일 CLI 분석은
--quickopt-out.
모드 분기 (첫 단계)
ARGUMENTS 에 --quick 포함 → Quick 모드 (Codex 단일).
그 외 → Deep 모드 (기본, 3-CLI Tri-Debate).
Deep 모드 (기본)
전제조건 프로브 및 Tier Degradation
진입 즉시 실행 — 10초 내 가시적 출력 보장. 빈 stdout + exit 0 금지.
curl -sf http://127.0.0.1:27888/status >/dev/null && \
codex --version 2>/dev/null && \
agy --version 2>/dev/null
| Tier | 조건 | 실행 방식 |
|---|---|---|
| Tier 1 | Hub + Codex + Antigravity 전부 | auto multi 3-CLI (tmux/psmux 리드면 interactive) |
| Tier 2 | Codex 또는 Antigravity 중 하나만 | 가용 CLI + Claude Agent |
| Tier 3 | Hub 또는 필요 CLI 불가 | Claude Agent only (consensus 미적용) |
Tier 3 시:
⚠ [Tier 3] multi 실행 환경 미충족 — single-model 모드 (consensus 미적용)
누락: {missing} | 권장: Hub + Codex + Antigravity 설치
또는 /tfx-analysis --quick 사용
HARD RULES
codex exec직접 호출 및 deprecated Gemini CLI 직접 호출 금지- Codex/Antigravity →
Bash("tfx multi ...")만. teammate mode는 생략해auto기본값을 쓴다. - Claude →
Agent(run_in_background=true) - Bash + Agent 동시 호출
모델 역할
| Model | 역할 | 강점 |
|---|---|---|
| Claude Opus (architect) | 아키텍처/설계 | 레이어, SOLID, 확장성 |
| Codex (security-engineer) | 구현/보안 | 복잡도, OWASP, 기술 부채 |
| Antigravity (ux-engineer) | UX/문서화 | DX, 접근성, 네이밍 |
EXECUTION
Step 1: 범위 파싱
- target: 파일/디렉토리/주제
- scope: 전체 + 하위
- focus_areas: 아키텍처, 보안, 성능, DX
Step 2: 3-CLI 독립 분석 (Anti-Herding) — Bash + Agent 동시 호출
Agent (Claude 아키텍처):
Agent(
subagent_type="oh-my-claudecode:architect",
model="opus",
run_in_background=true,
name="arch-analyst",
prompt="소프트웨어 아키텍트로서 분석하라. 대상: {target}. 렌즈: 아키텍처, SOLID, 응집도/결합도, 확장성, 테스트 용이성. JSON: { findings: [...], architecture_diagram: '...', health_score: 0-100 }"
)
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.
- 8d ago First seen · 194 lines · 76 tokens per session scan A 28c418afbb35
tfx-analysis is a skill published in the GitHub repository tellang/triflux (7 stars, last pushed 6d ago), licensed MIT. It adds 76 tokens to every session and 1,989 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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