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/zts0hg/codexspec/codexspec-onboardnpx skills add Zts0hg/codexspec --skill codexspec-onboardgit clone --depth 1 https://github.com/Zts0hg/codexspecWrote 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/zts0hg/codexspec/codexspec-onboard)<a href="https://agentmods.dev/skills/zts0hg/codexspec/codexspec-onboard"><img src="https://agentmods.dev/badge/skills/zts0hg/codexspec/codexspec-onboard.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 | $0.00024 | $0.02110 |
| Opus 5 | $0.00012 | $0.01055 |
| Sonnet 5 | $0.00005 | $0.00422 |
| Haiku 4.5 | $0.00002 | $0.00211 |
Grade A, and why
codexspec:onboard 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Onboarding
Language Preference
Read .codexspec/config.yml. Two independent language controls apply (each falls back to language.output, then English):
- Interaction language (
language.interaction): language for all conversation with the user — questions, explanations, status messages, andcodexspecCLI terminal output. - Document language (
language.document): language for generated artifact files (the profile records).
Converse in the interaction language and author artifacts in the document language. Apply the project's translation standard to both: translate by meaning (not word-for-word), keep English for terms with no good native equivalent, and write as if originally in that language. Exception: evidence.facts records a verbatim code observation (path + snippet) and MUST NOT be translated.
User Input
the text after the $codexspec:onboard skill mention
Role and Operating Model
onboard is the cold-start / bulk counterpart to distill. Where distill writes the project profile incrementally from interaction, onboard scans an existing codebase once and batch-writes the reusable knowledge that is implicit in the code and not already recorded accessibly into the shared store .codexspec/profile/. It exists to bootstrap a brownfield project's profile so accumulated project knowledge is grounded immediately, instead of only after enough work has flowed through distill.
onboard is read-only on the codebase and write-only to .codexspec/profile/: it never modifies source, tests, git state, or the constitution. It is a standalone, user-invoked command — not an SDD pipeline stage: it has no auto-next successor and no automatic hook, and it leaves no persistent document beyond the profile records (no map, no walkthrough — those, if ever wanted, belong to a separate explain).
Prerequisite & Scaffold
Before scanning:
- If
.codexspec/is absent, the project is not codexspec-initialized. Stop and direct the user to runcodexspec init. Do not scaffold a whole project. - If
.codexspec/is present but the profile store is missing, ensure the canonical scaffold — the six category directories.codexspec/profile/{constraints,conventions,pitfalls,decisions,strategies,runbooks}/(matching whatcodexspec initproduces) — before writing. - git is not required. onboard runs on a plain directory.
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 Changed · +3 tokens per session 40fc6b659cad
- 4d ago First seen · 89 lines · 21 tokens per session scan A 4a8908383905
codexspec:onboard is a skill published in the GitHub repository Zts0hg/codexspec (5 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 2,110 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
test-assess
Test agentready assess against real GitHub repositories to validate assessor changes. Selects repos relevant to the change being tested, clones them to a temp directory, runs the local checkout's assessor, reports results and output locations, then cleans up. Use when testing a new or modified assessor, verifying a…
implement-type-annotations
Add comprehensive type hints to Python/TypeScript code to improve IDE support, catch errors early, and enable better AI code understanding.
setup-claude-md
Create comprehensive CLAUDE.md files with tech stack, standard commands, repository structure, and boundaries to optimize repositories for AI-assisted development.
python-anti-patterns
Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.
python-background-jobs
Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.
python-design-patterns
Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when…