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BF flagrelease-entrance-flagos

Full FlagRelease pipeline orchestrator. Runs the complete LLM deployment, verification, and benchmarking pipeline for multi-chip GPU backends. Executes: install-stack → env-verify → model-verify → perf-test in sequence, passing state between steps and producing a final structured report. Assumes gpu-container-setup (Step 1) is already done — a running container with PyTorch + GPU access must exist.

ClawHub Agent Skills author: Flagos v1.0.0 MIT-0 3 files body ≈ 1 752 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 40/100 · Will not run — References files that are not bundled: scripts/collect_env_info.py, scripts/detect_network.py, scripts/select_flagtree_wheel.py

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
95
Quality 40%
63
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: scripts/collect_env_info.py, scripts/detect_network.py, scripts/select_flagtree_wheel.py
Tools and files w 18
0
Result and completion w 14
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash(*)
    allowed-tools: Bash(*) Read Edit Write Glob Grep WebSearch WebFetch AskUserQuestion Agent

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/collect_env_info.py
  • warning missing-ref reference to a missing file: scripts/detect_network.py
  • warning missing-ref reference to a missing file: scripts/select_flagtree_wheel.py
  • warning missing-ref reference to a missing file: scripts/validate_packages.py
  • warning missing-ref reference to a missing file: scripts/run_offline_inference.py
  • warning missing-ref reference to a missing file: scripts/test_serve_mode.py
  • warning missing-ref reference to a missing file: references/error-classification.md
  • warning missing-ref reference to a missing file: scripts/diff_analysis.py
  • warning missing-ref reference to a missing file: scripts/run_all_benchmarks.py
  • note frontmatter-key unknown frontmatter key "user-invokable"

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: scripts/collect_env_info.py, scripts/detect_network.py, scripts/select_flagtree_wheel.py
  • 0Tools and files. 9 referenced file(s) missing: scripts/collect_env_info.py, scripts/detect_network.py, scripts/select_flagtree_wheel.py
  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 31 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1752 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 401: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: clean
This skill openly describes a GPU-container LLM deployment pipeline, and its Docker, package install, model download, and benchmarking actions fit that purpose.
LLM: benign (medium) · 28 May 2026