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/cacr92/wereply/codexnpx skills add cacr92/WeReply --skill codexgit clone --depth 1 https://github.com/cacr92/WeReplyWrote 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/cacr92/wereply/codex)<a href="https://agentmods.dev/skills/cacr92/wereply/codex"><img src="https://agentmods.dev/badge/skills/cacr92/wereply/codex.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 | $0.00042 | $0.02737 |
| Opus 5 | $0.00021 | $0.01368 |
| Sonnet 5 | $0.00008 | $0.00547 |
| Haiku 4.5 | $0.00004 | $0.00274 |
Grade A, and why
codex 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 3d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex CLI Integration
Overview
Execute Codex CLI commands and parse structured JSON responses. Supports file references via @ syntax, multiple models, and sandbox controls.
When to Use
- Complex code analysis requiring deep understanding
- Large-scale refactoring across multiple files
- Automated code generation with safety controls
Fallback Policy
Codex is the primary execution method for all code edits and tests. Direct execution is only permitted when:
- Codex is unavailable (service down, network issues)
- Codex fails twice consecutively on the same task
When falling back to direct execution:
- Log
CODEX_FALLBACKwith the reason - Retry Codex on the next task (don't permanently switch)
- Document the fallback in the final summary
Usage
Mandatory: Run every automated invocation through the Bash tool in the foreground with HEREDOC syntax to avoid shell quoting issues, keeping the timeout parameter fixed at 7200000 milliseconds (do not change it or use any other entry point).
codex-wrapper - [working_dir] <<'EOF'
<task content here>
EOF
Why HEREDOC? Tasks often contain code blocks, nested quotes, shell metacharacters ($, `, \), and multiline text. HEREDOC (Here Document) syntax passes these safely without shell interpretation, eliminating quote-escaping nightmares.
Foreground only (no background/BashOutput): Never set background: true, never accept Claude's "Running in the background" mode, and avoid BashOutput streaming loops. Keep a single foreground Bash call per Codex task; if work might be long, split it into smaller foreground runs instead of offloading to background execution.
Simple tasks (backward compatibility): For simple single-line tasks without special characters, you can still use direct quoting:
codex-wrapper "simple task here" [working_dir]
Resume a session with HEREDOC:
codex-wrapper resume <session_id> - [working_dir] <<'EOF'
<task content>
EOF
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.
- 3d ago First seen · 334 lines · 42 tokens per session scan A e027f80aee75
codex is a skill published in the GitHub repository cacr92/WeReply (6 stars, last pushed 7mo ago), licensed MIT. It adds 42 tokens to every session and 2,737 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…