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/dr-code/tessera/codex-reviewnpx skills add dr-code/tessera --skill codex-reviewgit clone --depth 1 https://github.com/dr-code/tesseraWhat 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 | $0.00676 |
| Opus 5 | $0.00017 | $0.00338 |
| Sonnet 5 | $0.00007 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
codex-review 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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/codex-review — Independent GPT Code Review
Usage
/codex-review — reviews staged changes (git diff --staged)
/codex-review HEAD~N — reviews last N commits
/codex-review [file ...] — reviews specific files
Description
Get an independent GPT code review via Codex CLI. Claude evaluates each GPT finding (agree, correct, or dismiss), adds severity levels, and presents a structured actionable review.
Instructions
Phase 0: Determine Scope
- No args: run
git diff --staged. If nothing staged, rungit diff HEAD~1for the last commit. HEAD~Npattern: rungit diff HEAD~N- File args: read the specified files directly
- If nothing found: ask the user what to review
Phase 1: Get Context (if tessera available)
1. graph_continue (mandatory first call)
2. graph_impact(changed_files=[...list of modified files...])
→ shows which parts of the codebase depend on what's being changed
3. graph_read each modified file
→ gives Claude full current file content before sending anything to GPT
4. graph_action_summary
→ surfaces locked architectural decisions; flag if review would violate any
Phase 2: Send to GPT
For diffs up to ~200 lines, send in one call. For larger diffs, send one file at a time.
codex exec "Independent code review. Context: <TASK_OR_DESCRIPTION>. Code/diff to review: <CODE_OR_DIFF>. Review for: (1) bugs and logic errors, (2) security vulnerabilities (injection, auth bypass, data exposure), (3) performance issues, (4) missing error handling, (5) readability and naming. Format each finding as: [BUG|SECURITY|PERF|STYLE|MISSING]: file:line — issue — suggested fix. End with VERDICT: approved | approved-with-notes | needs_revision"
Phase 3: Claude Evaluates
For each GPT finding, Claude:
- Confirms the finding is real (not a false positive)
- Assigns severity: CRITICAL / HIGH / MED / LOW
- Notes if the issue is already handled elsewhere in the codebase
- Adds any findings GPT missed
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 · 69 lines · 34 tokens per session scan A 8c00712f58db
codex-review is a skill published in the GitHub repository dr-code/tessera (1 stars, last pushed 21d ago), licensed MIT. It adds 34 tokens to every session and 676 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.
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