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CD 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 Claude Code author: Flagos v1.0.0 MIT-0 3 files body ≈ 1 752 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

Full FlagRelease pipeline orchestrator.

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

ProcedureDockerInfrastructuretype 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
C
82/100
safety, quality, tests
Safety 60%
95
Quality 40%
63
Run on models
none yet
Process rating
D
40/100
Unfinished process
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: suspicious
This skill is a deployment orchestrator with legitimate goals, but it delegates execution to unbundled sibling skills and grants broad command, file, network, and agent authority that cannot be fully reviewed from this package.
LLM: suspicious (high) · 16 Sept 2026