Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/codealive-ai/ai-driven-development/agentic-readinessnpx skills add CodeAlive-AI/ai-driven-development --skill agentic-readinessgit clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-developmentWhat 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 | $0.00076 | $0.00779 |
| Opus 5 | $0.00038 | $0.00390 |
| Sonnet 5 | $0.00015 | $0.00156 |
| Haiku 4.5 | $0.00008 | $0.00078 |
Grade A, and why
agentic-readiness 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 2d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Readiness
Default to audit-only. Present findings and wait for approval unless the user explicitly asks to implement changes.
Audit workflow
- Read the repository's existing instruction chain before inspecting other files.
- 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.
- 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.
- Read the references needed for the task:
- instruction-files.md for
AGENTS.md/CLAUDE.mdownership, 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.
- instruction-files.md for
- 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.
What ships with it
7 files 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.
- 2d ago First seen · 58 lines · 76 tokens per session scan A 86c7032122ae
agentic-readiness is a skill published in the GitHub repository CodeAlive-AI/ai-driven-development (131 stars, last pushed 4d ago), licensed MIT. It adds 76 tokens to every session and 779 once invoked, about $0.0004 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.
Other skills, from other repositories
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map-resume
Resume an interrupted MAP workflow from .map/ /stepstate.json checkpoint. Use when returning after context exhaustion, /clear, or a session crash mid-workflow. Do NOT use to start new work; use map-plan or map-efficient.
map-skill-eval
Evaluate a /map- skill's trigger accuracy and cost. Use when asked to measure skill trigger accuracy, run an eval-set, or check token/duration cost via mapify skill-eval. Do NOT use to plan or implement; use map-plan or map-efficient.
create-workflows
Creates and revises portable declarative AI workflow files using Markdown frontmatter, DAG nodes, provider IDs, artifact references, imports, reusable inputs, findings artifacts, and review loops. Use when asked to author, improve, modularize, or validate .task.md workflows for CLI-based multi-agent workflow…
intuitive-doc
Create and maintain an intuitive human documentation surface for AI-agent-developed repos. Use when humans should only need README.md, ARCHITECTURE.md, STATUS.md, and docs/human/ while planning logs, generated docs, retrospectives, ADR detail, and implementation evidence stay in AI-agent-only folders.
skill-runner
Run a bounded development task through named skills in an isolated, tmux-backed Codex or Claude session. Use for durable or artifact-sensitive worker phases, supervised skill runs, and post-run skill evaluation.