JevCompass

Give Codex a map of the tools and skills it already has.

A useful skill can be buried in your setup. JevCompass discovers installed candidates locally, narrows them with a reviewed catalog, and offers a short suggestion when a task has a clear match. It works with Codex Desktop and CLI hooks, without replacing your agent or gating everyday commands.

• Start fast: try explicit advice from the terminal before installing any hooks.
• Keep control: suggestions never run tools, grant permissions, or override required checks.
• Choose your privacy level: local advice works without a key; optional Jev ranking sees only coarse candidate metadata through OpenRouter.

Get started · See real output · Understand the boundary · Check the evidence · Contribute

Quick start

Requirements: Python 3.11 or newer, pipx, and a Codex installation with hooks enabled. Install the published package on PyPI:

pipx install jevcompass
jevcompass doctor

Start with jevcompass doctor to see which representative tasks have enough locally available candidates for a recommendation. Before hook installation it may exit nonzero because the hooks are not registered yet; the capacity report is still useful. Then try a manual request only when the report shows a useful choice for that task; for example, run jevcompass recommend --category coding --domain python when coding_python reports decision candidates or local candidates. A clean profile may report low signal skip or silent, in which case that request can correctly return no recommendation.

To install from source instead, clone this repository and run pipx install . from the checkout. For a reproducible release, use pipx install jevcompass==0.1.19; check releases for the current verified version. The 0.1.20 source candidate suppresses a generic shell-only package-docs suggestion seen in a 0.1.19 paired trial; a task-specific installed skill remains eligible. Until 0.1.20 is published and registry-tested, 0.1.19 remains the verified pin. The latest source on main may contain unreleased changes.

The recommendation is environment-dependent: it can show local advice, an unranked shortlist, or no recommendation. The manual command uses explicit category/domain metadata and never takes a task prompt.

To enable automatic advice, first inspect what would change, then install:

jevcompass install --dry-run
jevcompass install
jevcompass doctor

What changes: jevcompass install backs up an existing Codex hooks.json and registers two advisory hooks. Review and trust the registrations in Codex's /hooks screen, then start a fresh session. The default config is under ~/.codex; set CODEX_HOME to use a different Codex home. A successful doctor check confirms local registration, not that Codex loaded or delivered advice. Recent hook metrics are matched to this Codex profile using a short local hash; records without a profile marker do not count as evidence for a fresh profile.

Optional skills for everyday coding

Experimental and explicitly opt-in: versions before 0.1.16 do not include this command. If doctor reports coding_python: low signal skip, you can preview the optional bundled skills before deciding whether to install them:

jevcompass skills install --dry-run

The dry run does not install anything, and installing skills does not guarantee a recommendation. Install only if you choose to:

jevcompass skills install
jevcompass doctor

The installer copies only jevcompass-focused-tests and jevcompass-regression-review into your Codex skill directory. With the default profile it uses ~/.agents/skills; with CODEX_HOME set it uses that profile's skills/ directory. It never changes hooks, executes scripts, calls Jev, or overwrites a different skill with the same name. Identical repeats are no-ops. Read the bundled SKILL.md files before enabling them, and start a fresh Codex session if they do not appear. Remove those two directories yourself after checking their contents if you no longer want the skills.

Suggestions for these skills are conditional on the task matching their guidance in SKILL.md; a suggestion is not a requirement. This remains an experiment with no demonstrated speed or quality benefit. In one source-built C05 pair, the agent read the suggested create-plan skill, but the blinded scores tied 6/7. This single result does not establish causality or effectiveness. That C05 pair did not test Desktop. Separate named-role Desktop worker smokes confirmed advice delivery and one actual focused-skill read after opt-in installation, without measuring a speed or quality gain. The skills and installer are included since v0.1.16 but are not installed unless you opt in. They are absent from v0.1.15.

Add your own installed skill

The built-in catalog cannot know when a private skill fits your work. Register a short, generic description explicitly, after reading its SKILL.md and checking the fields you are willing to share:

jevcompass skills add my-review-skill \
  --capability 'Review local code changes' \
  --use-when 'The task calls for source review' \
  --avoid-when 'The task only needs implementation' \
  --category review --domain software

The first run previews the fields and makes no change. If those fields are safe to send to OpenRouter when optional remote ranking is enabled, repeat with --approve-remote-metadata. Registration writes only your descriptions to CODEX_HOME/jevcompass/catalog.json (normally ~/.codex/jevcompass/catalog.json); it does not copy the skill body or path. Only an actually installed skill with matching name becomes a candidate. Keep descriptions generic: the skill ID, capability, use and avoid conditions may be sent to OpenRouter for ranking. Without an OpenRouter key, matching advice stays local. Remove an entry by editing that local JSON file; a changed catalog invalidates cached rankings. A suggestion never substitutes for reading the actual SKILL.md.

Enable optional Jev ranking

JevCompass works without an OpenRouter key: it can offer a local, unranked shortlist or stay silent. To let Jev rank eligible candidate choices, provide an OpenRouter API key. On a system with a supported keyring, install the optional dependency and enter the key through the hidden terminal prompt:

pipx inject jevcompass 'keyring>=25'
jevcompass auth status
jevcompass auth set
jevcompass doctor

For a new installation, pipx install 'jevcompass[secure-store]' includes the keyring dependency. If no supported keyring is available, supply OPENROUTER_API_KEY to the Codex process through your existing secret manager. Never paste a literal key into a hook command, repository file, or shell history. The environment variable takes precedence over the keyring; jevcompass auth status reports the source without printing the secret. jevcompass auth delete removes only the keyring entry after interactive confirmation.

jevcompass doctor checks model metadata without a paid decision request. Run jevcompass doctor --test-jev only when you explicitly want one synthetic, billed API test. A configured key permits remote ranking for eligible tasks; it does not guarantee a recommendation or prove that a hook delivered one to Codex.

Verify advice in a fresh session

After jevcompass install, inspect the two registrations in Codex /hooks, trust them if prompted, and start a new Desktop or CLI session. For a focused check, give Codex a substantive task such as planning a small Python package and ask it to report any JevCompass advice ID and suggested IDs from its initial context before its first tool call. An absent ID means that session did not receive advice; doctor and local hook metrics alone cannot prove delivery. The advisor may intentionally stay silent when only a generic shell command is available.

If your Codex host does not load either hook, run jevcompass doctor --json to distinguish registration from recent invocation, and use the explicit jevcompass recommend --category project-setup --domain python command while investigating. Never treat a manual result as proof that a hook delivered context to an agent.

60-second demo

A clean Codex profile may have only a generic shell tool. JevCompass stays silent in that case because repeating “use the shell” would add no value. To see a concrete suggestion, explicitly preview and install the two optional first-party coding skills:

jevcompass skills install --dry-run
jevcompass skills install
jevcompass recommend --category coding --domain python

On a fresh isolated Linux profile using the published 0.1.16 package with no OpenRouter key, the final command returned this excerpt:

Local unranked fallback; Jev did not select these candidates.
- tool `exec_command`: Codex built-in exec_command for bounded local shell commands
- skill `jevcompass-focused-tests`: Choose focused tests after a change and complete required repository validation

The skill is available locally after installation; read its SKILL.md and use it only when its condition fits. The recommendation does not prove the agent read or followed it, and installing a skill does not guarantee faster or better work. If you want no added skills, leave the profile unchanged; doctor and recommend will explain or demonstrate when JevCompass has enough of your existing candidates to advise. Hook installation is separate: jevcompass install --dry-run, then jevcompass install, review /hooks, and start a fresh session. Optional OpenRouter ranking uses coarse metadata only and may incur charges.

How it works

• Prompt hook: UserPromptSubmit classifies an eligible prompt locally, then checks reviewed candidates discovered on the machine. Explicit Codex Desktop/CLI hook or skill setup can point to the installed openai-docs skill; routine prompts and unavailable skills can remain silent.
• Subagent hook: SubagentStart can suggest role-level candidates for Codex's built-in explorer and worker roles. That event does not include the subagent's task text.
• Optional spawn advice: jevcompass install --spawn-advice adds a narrowly matched PreToolUse hook for agent creation only (Agent, spawn_agent, or collaborationspawn_agent). It locally classifies readable child task text or a descriptive task_name, then sends only coarse metadata and reviewed candidate descriptions to Jev. It never gates a spawn, shell, Git, Helm, or network command. If neither field provides a clear category or the only candidates are generic shell and Git, it stays silent. A normal reinstall preserves the opt-in; jevcompass install --disable-spawn-advice removes it. Review and trust the updated hook in /hooks, then start a fresh session. This is experimental and disabled by default; verify the child sees an advice ID before its first tool before relying on it.
• Manual mode: jevcompass recommend --category CATEGORY --domain DOMAIN [--role ROLE] requests advice using explicit metadata. Use jevcompass recommend --help for accepted values.
• Optional skill pack: from v0.1.16, jevcompass skills install is an explicit, experimental opt-in that makes two reviewed coding workflows available locally. Matching-skill guidance is conditional on the task; installation is separate from hooks and never automatic.
• Python test runner: for testing/python, a clear unittest command in local GitHub Actions workflows suppresses a globally installed pytest candidate and offers unittest instead. If CI uses pytest or both runners, JevCompass does not infer a unittest requirement. Always follow the repository's exact test command.
• Uncertainty: Local fallback advice is labeled unranked. If there is no useful candidate, JevCompass can stay silent. A configured MCP server is not assumed to be callable in the active session.
• Control: Advice does not run tools or skills, change permissions, block commands, or replace project instructions and required checks.

For setup diagnostics, run jevcompass doctor; use jevcompass --version to identify the installed command and jevcompass doctor --json for structured output. The optional --test-jev flag sends one synthetic, billed request.

Privacy

Prompt classification and candidate discovery happen locally. When optional Jev ranking is used, JevCompass sends an allowlisted category, domain, role, criteria, and generic descriptions of reviewed candidates to OpenRouter's Decisions API. It does not send the raw prompt, source code, diffs, repository paths, memory contents, or unapproved SKILL.md descriptions. A skill added with --approve-remote-metadata is an exception you control: its ID and your generic capability, use and avoid descriptions can be sent to OpenRouter when remote ranking is enabled. Preview these fields first; omit names or details you consider private. OpenRouter receives the API key in the HTTPS authorization header.

Local metrics contain event/category/outcome/timing and a short advice ID; they do not record the prompt, model response, paths, or memory content. Local cache entries contain selection metadata. As with any external service, review OpenRouter's terms and data handling before enabling remote ranking.

Remote ranking is optional. Without a key or a reliable Jev response, JevCompass can use a small local shortlist when useful; otherwise it can stay silent. Network errors and timeouts do not block the Codex task.

Compatibility

• Python 3.11+
• Linux: local runtime and CLI checks have been performed.
• macOS: targeted, but runtime behavior has not been verified.
• Windows: not a verified target.
• Codex Desktop and CLI: the default installation registers UserPromptSubmit and SubagentStart; optional agent-spawn advice adds only a narrowly matched PreToolUse hook. Host delivery is version-, trust-, and session-dependent. Fresh Desktop prompt delivery and broad subagent coverage remain unverified.

JevCompass does not infer Codex Plan UI mode. Check /hooks and start a fresh session after installation or a Codex upgrade.

What has been verified

Evidence is deliberately limited to the environments tested:

• A fresh isolated Codex CLI 0.155.1 smoke using the published v0.1.12 wheel received openai-docs advice before its first tool, with a matching local hook ID. Additional synthetic CLI pairs confirmed remote Jev advice IDs before the first tool. The published v0.1.13 wheel adds bounded skill use/skip conditions; these checks do not prove broad coverage or effectiveness.
• The initial four blinded synthetic CLI pairs tied on task quality. Later focused pairs had mixed results, including one baseline win and one treatment win; no repeatable speed or quality improvement has been demonstrated.
• A native Desktop explorer child reported a correlated advice ID in an earlier smoke. With published 0.1.18 and two optional bundled skills explicitly installed, a fresh worker reported advice ID 96b34a1d and exec_command/jevcompass-focused-tests before its first shell read; the matching local SubagentStart metric took 56.79 ms. A fresh explorer with only generic candidates correctly stayed silent. These are isolated role-level delivery checks; fresh Desktop prompt delivery and outcome benefit remain unverified.
• PyPI v0.1.19 and the GitHub release include the local Python test runner relevance fix. Hosted CI and Trusted Publishing passed; a clean registry pipx install on Python 3.11 registered two hooks, passed doctor and suggested unittest without pytest in this repository. The first uv install attempt briefly missed the just-published version; retry without its cache succeeded. PyPI v0.1.18 and its GitHub release contain the corrected approved-metadata disclosure. Trusted publish run 36142929270 passed; a clean PyPI pipx install on Python 3.11 registered the two default hooks and doctor passed. PyPI v0.1.17 and its GitHub release include the opt-in custom-skill catalog. Its PyPI README had an imprecise privacy sentence; v0.1.18 corrects it. The 0.1.17 wheel passed local content checks, 271 Python 3.11 tests, green hosted CI and isolated pipx installation. Earlier PyPI v0.1.16 and its GitHub release contain byte-identical Python modules, catalog and optional skill files. The exact-tag archive passed 254 Python 3.11 tests and an isolated pipx wheel install with two default hooks, no PreToolUse gate and doctor PASS. This validates packaging and local setup, not task outcomes or Desktop/macOS runtime.
• The v0.1.14 source includes the changes from PR #40 (respect installed skill MCP prerequisites), PR #41 (C05 equal-environment comparison tied on blind scores), and PR #42 (opt-in agent-spawn advice). The spawn check was an isolated CLI source-checkout run: the child saw an advice ID before its first tool using the descriptive task title because the host message was encoded. The v0.1.14 wheel also passed an isolated pipx install and clean synthetic CLI delivery check: the child's context contained a spawn advice ID before its first tool, though it did not repeat the ID before that tool. At the v0.1.14 source check, Desktop and macOS were unverified; later named-role Desktop smokes are described above. Fresh Desktop prompt delivery, macOS runtime and outcome benefit remain unverified. The default install remains two advisory hooks; routine commands are not gated.
• A fresh isolated Codex CLI 0.155.1 session with installed 0.1.19 reported exec_command and unittest with correlated advice ID 215744b8 before its first tool (UserPromptSubmit/testing/local, 33.66 ms). The fictional fixture's required unittest suite failed an existing assertion; this is local advice delivery, not remote ranking or measured benefit.
• A 0.1.19 installed-package P01 coding pair with equal opt-in skills delivered local advice before first tool, but neither arm recorded a required unittest exit. A quality correction after unblinding cannot support a blind outcome claim. See PILOT.md.
• One repeated 0.1.19 P01 pair froze an anonymous quality assessment before mapping: both helpers passed. Treatment had correlated local advice and a recognized successful unittest; baseline test execution was unobserved. This limited result does not prove that advice caused the difference or improve overall outcomes.
• A separate installed 0.1.19 P05 documentation pair tied on blind README quality. Treatment suggested only exec_command; a required fixture unittest failure was observed there, while baseline validation remained unobserved. Productive-action timing and advice benefit were not established.
• macOS runtime behavior and broad usefulness remain unverified.

These checks do not establish that recommendations improve outcomes. If you test JevCompass, please share a reproducible example of advice that helped—or a case where silence was the right result.

Soon available

More host validation, narrower coding recommendations, and a measured 20-case comparison are tracked in the roadmap. These are planned work, not shipped capabilities or proven productivity gains.

Contribute

Issues and pull requests are welcome. Helpful contributions include reproducible compatibility reports, careful documentation fixes, and synthetic tests that preserve the privacy boundary. Please do not post credentials, raw prompts, private code, or unredacted logs.

License

JevCompass is licensed under the Apache License 2.0. Copyright 2026 Toni Nowak.

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