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/specnpx skills add bnet47/codexicon --skill specgit clone --depth 1 https://github.com/bnet47/codexiconWhat 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 | $0.00023 | $0.00284 |
| Opus 5 | $0.00012 | $0.00142 |
| Sonnet 5 | $0.00005 | $0.00057 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
spec 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 yesterday.
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.
What it actually says
Spec
Announce: "I'm using spec to make the requirement executable before implementation."
- Read only the relevant project guidance and existing decisions.
- Ask targeted questions only for missing behavior, constraints, or acceptance evidence. Skip questions when local context resolves them.
- Save
agent_docs/briefs/[YYYY-MM-DD]-[slug].md, adding-v2,-v3, and so on rather than overwriting:
# Spec: [Name]
**Date:** [YYYY-MM-DD]
**Status:** Approved
## Request
[Precise restatement of the requested outcome.]
## Behavior
[What changes, for whom, and the important interaction or data flow.]
## Acceptance criteria
- [ ] [Observable and testable result.]
## Out of scope
- [Explicit exclusion.]
## Constraints
- [Compatibility, security, performance, or platform requirement.]
## Verification
- `[exact command or inspection]` — [what it proves]
Before saving, remove placeholders, subjective criteria, and implementation detail that is not required by the request or an accepted ADR.
Offer $write-plan for multi-step implementation. Do not commit or push unless separately asked.
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.
- yesterday First seen · 43 lines · 23 tokens per session scan A 0524bd7460ea
spec is a skill published in the GitHub repository bnet47/codexicon (5 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 284 once invoked, about $0.0001 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
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.
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。.
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.
architecture-diagram
Dark-themed SVG architecture/cloud/infra diagrams as HTML.