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/ghabix/spinecodex/code-review-contextnpx skills add GhabiX/SpineCodex --skill code-review-contextgit clone --depth 1 https://github.com/GhabiX/SpineCodexWhat 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 | $0.00007 | $0.00142 |
| Opus 5 | $0.00003 | $0.00071 |
| Sonnet 5 | $0.00001 | $0.00028 |
| Haiku 4.5 | $0.00001 | $0.00014 |
Grade A, and why
code-review-context 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.
This is a copy
100% identical to code-review-context — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Codex maintains a context (history of messages) that is sent to the model in inference requests.
- No history rewrite - the context must be built up incrementally.
- Avoid frequent changes to context that cause cache misses.
- No unbounded items - everything injected in the model context must have a bounded size and a hard cap.
- No items larger than 10K tokens.
- Highlight new individual items that can cross >1k tokens as P0. These need an additional manual review.
- All injected fragments must be defined as structs in
core/contextand implement ContextualUserFragment trait
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 · 13 lines · 7 tokens per session scan A 0af80b6388cc
code-review-context is a skill published in the GitHub repository GhabiX/SpineCodex (124 stars, last pushed 6d ago), licensed Apache-2.0. It adds 7 tokens to every session and 142 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to code-review-context, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
codex-autoresearch
Run autonomous, measurable experiments in a Git repository: change one hypothesis, verify a numeric metric, keep improvements, and revert failures. Use when the user wants Codex to keep iterating toward a numeric target in the foreground or as a detached background run. Do not use for ordinary one-shot coding…
map-wayfind
Decision-frontier wayfinding: build and work a durable map of open design decisions BEFORE planning, for large or foggy efforts where /map-plan would force premature decomposition. Use when a task is too big or too vague to decompose — many unknowns, tangled decisions, or "I'm not even sure what to build yet" — and…
clipboard
Copy text to clipboard with optional rich formatting. Triggers on "copy to clipboard", "copy that", "pbcopy", "copy formatted", "copy rich text".
neo4j-modeling-skill
Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with…
alphafold-database
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
auto-test-project
当用户明确要求"测试项目"、"运行 auto-test-project"或"进行项目级测试"时使用。对完整项目进行多轮 A 轮批判性测试 + B 轮质量检查，系统化发现、记录、修复问题。⚠️ 不适用：用户只是想优化功能（应直接修改）、只是询问项目问题（应直接回答）、没有明确"测试"意图。.