agentsclimarketplace
Sign in

Agentic readiness

Skill CodeAlive-AI/ai-driven-development/skills/agentic-readiness

Audit and improve repositories for reliable agentic work across Codex and Codex App, Claude Code, and OpenCode. Use when reviewing AGENTS.md or CLAUDE.md quality and discovery, instruction routing in monorepos or meta-repos, agent settings, MCP configuration, skills, subagents, context budgets, or repository organization for coding agents.From its SKILL.md

Install
npx -y skills add CodeAlive-AI/ai-driven-development --skill agentic-readiness

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • runs commandsInstructs the agent to run 1 command, including `python scripts/audit_repo.py --root .`.

SKILL.md

3.7 KB, 693 tokens by cl100k_base, as published. Nobody here has run it

Agentic Readiness

Default to audit-only. Present findings and wait for approval unless the user explicitly asks to implement changes.

Audit workflow

  1. Read the repository's existing instruction chain before inspecting other files.
  2. Run from the repository root:
python scripts/audit_repo.py --root .

Add --include-user-scope only when the user explicitly wants personal Codex, Claude Code, and OpenCode configuration included. Never inspect credential stores or print secret values.

  1. Inspect the JSON report and verify findings against the actual build files, scripts, and repository layout. The script detects structural risks; it cannot prove that documented commands or architecture are current.
  2. Read the references needed for the task:
    • instruction-files.md for AGENTS.md/CLAUDE.md ownership, discovery, routing, compatibility, and CodeAlive lessons learned.
    • rubric.md for scoring and priority definitions.
    • checklist.md for the full cross-agent audit.
    • best-practices.md for settings, workflows, context, and safety beyond instruction files.
  3. Report evidence before recommendations.

Report shape

Keep the report concise:

  • Executive summary: readiness, strongest area, main failure mode, first action.
  • Repository profile: scale, languages/frameworks, Git/worktree shape.
  • Instruction topology: canonical file, compatibility shim, nested routing, active-chain caveats, context-budget risks.
  • Agent surfaces: Codex/Codex App, Claude Code, and OpenCode settings, MCP, skills, and subagents actually present.
  • Issues: P0 through P3 with file paths and evidence.
  • Recommendations: concrete edits and verification commands.

Do not penalize a repository for omitting agent-specific configuration it does not need. Do flag a claimed cross-agent setup that one of the named agents cannot discover.

Implementation workflow

When the user asks to apply changes:

  1. Confirm the requested scope from the conversation; do not ask again when it is already explicit.
  2. Preserve one source of truth. Prefer root AGENTS.md plus a regular CLAUDE.md containing @AGENTS.md when the same rules should serve all three agents.
  3. Put scoped rules near their target paths. Add routing indexes only where they prevent real discovery mistakes.
  4. Keep generated directories, dependencies, caches, secondary worktrees, and unrelated subtrees out of routing tables.
  5. Turn non-negotiable rules into hooks, linters, or CI checks; instruction files are guidance, not enforcement.
  6. Show the diff, rerun the audit and tests, then explain any remaining intentional gaps.

Large and multi-repository workspaces

For more than 3,000 tracked files, emphasize navigation, bounded routing, focused verification, and retrieval support rather than copying documentation into startup context.

For a directory containing multiple child Git repositories, treat each child as an independent instruction root. Codex builds its instruction chain once per run/session, so changing a command's working directory does not load the child repository's files. Recommend an explicit meta-repository routing rule and verify each child from a fresh session or by reading its chain before the first operation.

What ships with it: 7 files

53.5 KB alongside SKILL.md, 2 of them executable

agents/

references/

scripts/

Gives 0 of the 12 instructions most memory context skills give in 693 tokens

Counted across 754 of the 1,056 authors here whose files we hold, read 2026-09-06

  • Preserve existing content structurein 15 of 754, across 9 files
  • Front-load the leading wordin 14 of 754, across 10 files
  • Update existing entries instead of duplicatingin 14 of 754, across 7 files
  • Keep CLAUDE.md under one hundred linesin 14 of 754, across 12 files
  • Read CLAUDE.md at the project rootin 14 of 754
  • Keep each meaning in a single source of truthin 12 of 754, across 8 files
  • Redact sensitive information before committingin 11 of 754, across 4 files
  • Scan for all CLAUDE.md filesin 11 of 754, across 7 files
  • Use frontmatter for metadata on filesin 10 of 754, across 3 files
  • Repeat user interactions 10 timesin 10 of 754, across 4 files
  • Write the CLAUDE.md file into the target folderin 10 of 754, across 8 files
  • Use memlab to process snapshotsin 9 of 754, across 3 files

Said here and by no other author read

  • Default to audit-only
  • Present findings and wait for approval
  • Verify findings against build files
  • Report evidence before recommendations
  • Preserve one source of truth

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.