Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/flagos-ai/skillsnpx agentmods add skills/flagos-ai/skills/flagrelease-entrance-flagosWrote 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/flagos-ai/skills/flagrelease-entrance-flagos)<a href="https://agentmods.dev/skills/flagos-ai/skills/flagrelease-entrance-flagos"><img src="https://agentmods.dev/badge/skills/flagos-ai/skills/flagrelease-entrance-flagos/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/flagos-ai/skills/flagrelease-entrance-flagos"><img src="https://agentmods.dev/badge/skills/flagos-ai/skills/flagrelease-entrance-flagos.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 | $0.00093 | $0.02074 |
| Opus 5 | $0.00046 | $0.01037 |
| Sonnet 5 | $0.00019 | $0.00415 |
| Haiku 4.5 | $0.00009 | $0.00207 |
Grade A, and why
flagrelease-entrance-flagos 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 12d 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FlagRelease Pipeline Orchestrator
End-to-end LLM deployment + testing pipeline for multi-chip GPU backends. Orchestrates 4 sub-skills in sequence and produces a final report.
Skill Components
flagrelease/
├── SKILL.md # This file — orchestration flow
└── references/
└── pipeline-state.md # Pipeline state schema, gate logic, data flow
Sub-skills (each independently invokable):
../install-stack/ # Step 2: Install 5 packages
│ ├── SKILL.md
│ ├── scripts/
│ │ ├── detect_network.py # Probe GitHub/PyPI, return mirror config
│ │ ├── collect_env_info.py # Python/glibc/arch/vendor/disk info
│ │ ├── select_flagtree_wheel.py # Match vendor+python+glibc → wheel
│ │ └── validate_packages.py # Import-test all 5 packages
│ └── references/
│ ├── vendor-mappings.md # FlagCX make flags, adaptor names
│ └── network-mirrors.md # Mirror config rules
../env-verify/ # Step 3: Qwen3-0.6B smoke test
│ ├── SKILL.md
│ ├── scripts/
│ │ ├── run_offline_inference.py # Phase A: offline inference test
│ │ └── test_serve_mode.py # Phase B: serve + health + chat test
│ └── references/
│ └── error-classification.md # Layer-based error classification
../model-verify/ # Step 4: Target model ± multi-chip
│ ├── SKILL.md
│ ├── scripts/
│ │ └── diff_analysis.py # Compare Run A vs Run B results
│ └── references/
│ └── multichip-errors.md # Multi-chip error patterns
../perf-test/ # Steps 5+6: Accuracy + Performance
│ ├── SKILL.md
│ ├── scripts/
│ │ ├── run_benchmark.py # Run single benchmark profile
│ │ └── run_all_benchmarks.py # Run all profiles + summarize
│ └── references/
│ └── benchmark-profiles.md # Profile definitions and metrics
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.
- 12d ago First seen · 235 lines · 93 tokens per session scan A dc8f6f37193b
flagrelease-entrance-flagos is a skill published in the GitHub repository flagos-ai/skills (19 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 2,074 once invoked, about $0.0005 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.
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