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AC codex-orchestrator

Methodical end-to-end software delivery orchestrator for Codex CLI with dual project modes (greenfield for new builds, brownfield for existing systems) and dual execution modes (autonomous and gated). Use when users want full lifecycle delivery with strict stage gates, progress tracking, per-step manual/automated testing, continuous docs updates, change-impact management, and a reusable AGENTS.md workflow for any coding agent.

modbender/skill-library-mcp Agent Skills author: modbender MIT 19 files body ≈ 2 219 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Methodical end-to-end software delivery orchestrator for Codex CLI with dual project modes (greenfield for new builds, brownfield for existing systems) and…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmenttype 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 56/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (codex-orchestrator) differs from the folder (codex-conductor)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 95 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 2219 tokens
    • 100Progress reporting. Reports progress
    • low 13 top-level sections: this looks like several domains in one skill

    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 430: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 95 items
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (9 of 9)
    • +3All 9 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.