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/bnet47/codexicon/production-readinessnpx skills add bnet47/codexicon --skill production-readinessgit clone --depth 1 https://github.com/bnet47/codexiconWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bnet47/codexicon/production-readiness)<a href="https://agentmods.dev/skills/bnet47/codexicon/production-readiness"><img src="https://agentmods.dev/badge/skills/bnet47/codexicon/production-readiness.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00034 | $0.00667 |
| Opus 5 | $0.00017 | $0.00333 |
| Sonnet 5 | $0.00007 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
production-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 4d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production readiness
Announce: "I'm using production-readiness to test whether this project can fail safely in production."
1. Set the boundary
Identify the release, environments, users, sensitive data, external systems, irreversible actions, and explicit launch criteria. Treat the workflow as read-only unless the user also asks for fixes. Never deploy, rotate credentials, change production, or accept risk on the user's behalf.
Read AGENTS.md, the approved charter/spec, relevant architecture and data model, deployment/CI configuration, manifests and lockfiles, then agent_docs/security.md and agent_docs/operations.md when present. Unknown evidence is a gap, not a pass. Never open credential-bearing files.
2. Build the evidence matrix
Assess only applicable surfaces and cite the file, command, test, dashboard, runbook, or owner that proves each conclusion:
- Security: trust boundaries, authentication, authorization, tenancy isolation, input validation, secret handling, outbound access, auditability, threat and abuse cases.
- Data and privacy: classification, minimization, retention/deletion, encryption, migrations, compatibility, backups, restore evidence, rollback, and regulatory obligations.
- Supply chain: locked dependencies, vulnerability audit, CI permissions, immutable actions, artifact integrity, update ownership, and third-party failure exposure.
- Reliability: health behavior, timeouts, retries, idempotency, degradation, observability, SLOs, alert ownership, incident response, and dependency failure modes.
- Scale and cost: capacity evidence, concurrency, rate limits, quotas, load shedding, abuse controls, and cost ceilings.
- Release safety: staging parity, migration order, rollout and rollback, feature controls, smoke tests, ownership, and decision points.
Do not demand controls that do not fit the system. Explain why an item is not applicable.
3. Verify
Run the project-specific checks plus the canonical gates when available:
What ships with it
1 file 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.
- 4d ago First seen · 50 lines · 34 tokens per session scan A e4e2c8c7e55c
production-readiness is a skill published in the GitHub repository bnet47/codexicon (5 stars, last pushed 5d ago), licensed MIT. It adds 34 tokens to every session and 667 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
当用户需要对PDF文件进行任何操作时，请使用此技能。包括从 PDF 中读取或提取文本/表格、合并多个 PDF、拆分 PDF、旋转页面、添加水印、创建新PDF、填写PDF表单、加密/解密 PDF、提取图片，以及对扫描版 PDF 进行 OCR 使其可搜索。如果用户提到 .pdf 文件或要求生成 PDF，请使用此技能。.
make_plan
For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via listagents and chatwithagent, emphasizing that the plan is executed by the requester, not by the consulted Agent.
oma-scholar
Scholarly research companion using Knows sidecar spec (.knows.yaml). Generates, validates, reviews, queries, and compares structured research-paper sidecars, and fetches them from knows.academy. Use for academic literature search, survey synthesis, paper authoring assistance, and peer review with token-efficient…
oma-hwp
Convert HWP / HWPX / HWPML files to Markdown using kordoc. Extracts text, headings, tables, lists, images, footnotes, and hyperlinks. Use for Korean word processor files (Hangul), government documents, and AI-ready data preparation.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.