Agent skill

D

by notque in notque/vexjoy-agent

Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline.

MITAuto-check: notes

Install D

skills CLI
$ npx skills add notque/vexjoy-agent --skill d -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install notque/vexjoy-agent d --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/notque/vexjoy-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/meta/d .claude/skills/d && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
d
GitHub stars
438
Token cost
~3.8k tokens
SKILL.md length
1,540 words
Files
4 (incl. references)
Skills in repo
61
Repo updated
First seen
Licence
MIT

At a glance

Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline.

  • Works in 5 steps: CLASSIFY → ALIGN INTENT (required for every matched… → DECIDE (fallback == false, after aligned… → …
  • SKILL.md covers Error handling and References
  • Calls python3; needs AI_GATEWAY_API_KEY and TYPESAFE_API_KEY

What it does

D is an agent skill from notque/vexjoy-agent. Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `EVAL.md`, `SPEC.md` and `references/jev-classifier-design.md`).

The repository describes itself as: VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop. The licence is MIT.

Requirements

  • Python 3
  • A credential in AI_GATEWAY_API_KEY
  • A credential in TYPESAFE_API_KEY
  • Pre-approved tools (allowed-tools): Read, Bash, Grep, Glob, Skill, Task

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. CLASSIFY
  2. ALIGN INTENT (required for every matched /d route)
  3. DECIDE (fallback == false, after aligned intent)
  4. ENHANCE (attach skills)
  5. EXECUTE

What it can do on your machine

Read from SKILL.md and the folder at commit 5218674. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash
    • Grep
    • Glob
    • Skill
    • Task

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • AI_GATEWAY_API_KEY
    • TYPESAFE_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

D loads about 3.8k tokens when it runs, and up to ~9.7k if it reads all its reference files. Until then it costs about 28 tokens; SKILL.md has 1,540 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash, Grep, Glob, Skill, Task

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from notque/vexjoy-agent at commit 5218674, republished under its MIT licence (© notque). 1,540 words, ~3,831 tokens.

Download SKILL.mdSave it as .claude/skills/d/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
d
description
Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline.
allowed-tools
Read, Bash, Grep, Glob, Skill, Task
version
1.3.0
user-invocable
true
argument-hint
[request]
routing.triggers
jev router, route with jev, use the d router
routing.not_for
General-purpose task execution — /d classifies and dispatches, it does not perform the work itself.
routing.category
meta-tooling

/d — Jev Router

Classifies requests through Jev and dispatches to the matched agent, skill, and pipeline. Before every dispatch, it restates the requested outcome and uses Jev to check that the restatement and route preserve it.

The classification path has four layers: a deterministic pre-route.py force-route guard (offline, runs first, authoritative for git/security only), a configured Jev transport presence check, a two-stage classification (a cheap wide-rank stage 1 over all manifest candidates plus a trivial-bypass gate, then a full-detail shortlist-rerank stage 2 with per-candidate fit checks, stack/fan-out signals, and domain-attachment checks), and a deterministic attachment step that lists the extra skills to load.

Design rationale: ${CLAUDE_SKILL_DIR}/references/jev-classifier-design.md.

Phase Banners

Every phase: /d > Phase N: PHASE_NAME — description... After intent alignment resolves: === routing banner. Both required.


Phase 1: CLASSIFY

scripts/jev-route.py owns the entire classification in one subprocess call.

When JEV_RESULT is already in context (the jev-route-injector hook ran the script before your first token), use it and skip the command below.

bash
REQUEST_FILE=$(mktemp); printf '%s' "{user_request}" > "$REQUEST_FILE"
python3 "$SDIR/jev-route.py" --request-file "$REQUEST_FILE" --json-compact
rm -f "$REQUEST_FILE"

Resolve $SDIR: ${HOME}/.claude/scripts, falling back through .hermes/.factory/.codex/.reasonix, or the repo's scripts/ directory.

Hold the result as JEV_RESULT. Shape (stable — see design reference for full schema):

available, jev_called, matched, fallback, fallback_reason, agent, agent_source, skill, pipeline, attach, complexity, confidence, match_type, reasoning, stack, signals, signal_scores, domain_scores, source, latency_ms, usage, agents, gate_score, fits_scores, stage1_shortlist, pre_route_hint, and intent_alignment (the hook-generated baseline alignment receipt).

attach is the list of extra skills that ride with skill (for example programming for Go work, testing when tests are part of the work). The script already validated every name; it is the whole Phase 4 stack.

latency_ms and usage are itemized dicts ({"stage1_ms","stage2_ms","total_ms"} and {"stage1","stage2"}). Read .total_ms for a single latency figure.

Gate: fallback == true → Phase 1F. Every matched result, including source == "jev-trivial-bypass", proceeds to Phase 2: ALIGN INTENT.


Phase 1T: TRIVIAL-BYPASS (source == "jev-trivial-bypass")

Stage 1's gate fired: gate_score below threshold, no agent/skill/pipeline is needed. It remains a direct-handling path, but it must still pass through Phase 2 so the user outcome is restated and Jev validates it. After an aligned Phase 2 result, show Classification: Trivial and Source: jev-trivial-bypass, then answer or do the one-line action directly. Do not run Phases 3–5 or call build-dispatch.py. Stop there.


Phase 1F: UNAVAILABLE (fallback == true)

Jev could not classify this request. JEV_RESULT.source explains why:

  • unavailable — neither configured Jev transport is available. /d accepts Vercel AI Gateway (AI_GATEWAY_API_KEY) or the direct Jev API (TYPESAFE_API_KEY), selected by JEV_TRANSPORT=auto|vercel|direct.
  • invalid-pick — Jev's pick was not a valid manifest name.
  • error — a Jev call timed out or failed.

Show:

===================================================================
 /d: Jev unavailable — [JEV_RESULT.fallback_reason]
 Use /do for manifest-based routing.
===================================================================

Fail open to /do's full routing flow and continue the request. Do not reject the request merely because Vercel AI Gateway is unavailable.


Phase 2: ALIGN INTENT (required for every matched /d route)

MANDATORY STOP: For every matched /d invocation, write PROPOSED_INTENT and run the validator on that exact text before any routing banner, dispatch, answer, edit, or other action. JEV_RESULT.intent_alignment is only the hook baseline and does not satisfy Phase 2. This requirement has no exception for force routes, trivial routes, or an apparently aligned baseline.

Before selecting the work method, write PROPOSED_INTENT: a concise one- or two-sentence restatement of what the user wants accomplished. State the outcome and each deliverable the request names or directly requires. Copy every explicit constraint in the user's words: limits ("only", "at most"), exclusions ("don't touch", "do not deploy"), required methods, and authorization boundaries. Do not add deliverables the user did not ask for, such as extra tests, docs, cleanup, refactors, or verification steps. Do not add notes about missing inputs or preconditions; the validator decides whether clarification is needed. Do not describe the selected agent, skill, or implementation mechanics as the outcome. Preserve the user's words where precision matters.

Run the Jev validator even when the hook already supplied JEV_RESULT.intent_alignment; that receipt validates a conservative baseline, while this call validates the actual restatement that will enter the task spec. Put the request, route JSON, and proposed intent in temporary files rather than shell-splicing user text, then call:

bash
python3 "$SDIR/jev-intent-align.py" \
  --request-file "$REQUEST_FILE" \
  --route-file "$ROUTE_FILE" \
  --proposed-intent-file "$INTENT_FILE" \
  --json-compact

The validator sends one bounded state and all independent questions together through the selected Jev transport. It checks whether the outcome and constraints are preserved, the route can cover the material scope, the restatement is too narrow, it introduces unrequested work, and essential clarification is needed. It returns aligned, clarification_needed, issues, and raw scores.

Show this before the routing banner:

Intent alignment (/d):
  -> Restated outcome: [PROPOSED_INTENT]
  -> Jev: [aligned|review|unavailable] [issues, if any]

Gate:

  • clarification_needed == true → ask one concise question that names the essential ambiguity; do not dispatch until answered.
  • alignment == aligned and source == jev-trivial-bypass → direct handling in Phase 1T; otherwise → Phase 3.
  • alignment == review because scope is lost, work was added, or the route cannot cover the request → correct PROPOSED_INTENT or the route and run this validator once more. Carry unresolved issues into task_spec.gaps; do not silently proceed as though Jev approved it.
  • alignment == unavailable or error → state that validation was unavailable, preserve the verbatim request and proposed intent in the task spec, then continue under the normal /d routing result. Gateway outage must not become a false request rejection.

This runtime gate applies to every matched route, including force-routes and trivial bypasses. A Phase 1 fallback cannot run this gate because no usable Jev route exists; it fails open to /do as described in Phase 1F.

This is an instruction gate enforced by the /d contract, not a hook-enforced technical boundary. The user remains the final backstop if an agent violates it.


Show full SKILL.md (654 more words)Show less
Phase 3: DECIDE (fallback == false, after aligned intent)

JEV_RESULT.source is either pre-route-force (a git/PR or security force route kept its skill and pipeline, or Jev failed on another force match) or jev (Jev classification, manifest-validated). A non-safety force match appears only as pre_route_hint: Jev saw it on its shortlist and made the pick.

Apply directly:

  • agent / skill / pipeline: use JEV_RESULT's values as-is. Already validated against the live manifest membership sets inside the script. agent_source: skill-default means Jev found no domain agent and the script used the skill's owning agent.
  • agent is null or general-purpose: pick from /do's Agent-greediness table (skills/meta/do/SKILL.md, Phase 2 Step 0b) when a row fits the request's domain. Otherwise keep general-purpose and write a one-line fallback_reason: general-purpose: <why no listed agent covers this>.
  • complexity: use JEV_RESULT.complexity when set. When null (always for pre-route-force), default to medium, except a single one-line trivial fix → simple.
  • Confidence: JEV_RESULT.confidence (high/medium/low).

Routing banner (Phase 2 intent block + routing block, both required, printed together):

===================================================================
 ROUTING (/d): [brief summary]
===================================================================

 Intent (/d):
   -> Restated: [PROPOSED_INTENT]
   -> Alignment: [aligned|review|unavailable] [— issues, if any]

 Selected:
   -> Agent: [JEV_RESULT.agent] - [JEV_RESULT.reasoning]
   -> Skill: [JEV_RESULT.skill] - [JEV_RESULT.reasoning]
   -> Attached: [JEV_RESULT.attach, comma-separated, or "none"]
   -> Pipeline: [JEV_RESULT.pipeline, if set]
   -> Source: [JEV_RESULT.source] (confidence: [JEV_RESULT.confidence])

 Invoking...
===================================================================

The Intent block must be populated from the Phase 2 validator run. Printing the banner with a placeholder or omitting the Intent block is a Phase 2 skip and is not allowed.

Gate: Agent+skill set, banner shown. Phase 4.


Phase 4: ENHANCE (attach skills)

stack = JEV_RESULT.attach, in order, plus anti-rationalization-core. Copy the names exactly. Do not add, rename, or drop skills: the script built attach from these rules, and build-dispatch.py rejects any name absent from skills/INDEX.json.

SourceAttaches
JEV_RESULT.stack (pre-route, e.g. a .go file with PR or security work)its entries, first
Agent domain floorprogramming for Go, Kotlin, PHP, and Swift agents; kubernetes for kubernetes-helm-engineer; frontend for ui-design-engineer
domain_scores at 0.6 or higherprogramming, frontend, kubernetes, testing, building-with-jev, research
tests_requested / comprehensive_review / objective_loop_worthytesting / review / workflow
local_onlylocal-only shared pattern

At most three skills are attached beyond skill. Two adjustments stay with you:

  • comprehensive_review attached review and a real multi-file diff exists: right-size-review.py outranks it, so drop review from stack.
  • signals.research_needed is true: add research-coordinator-engineer to the fan-out agents.

Fan-out agents: union JEV_RESULT.agents (script-computed fan-out picks, each passed its per-candidate fit check) into the research_needed agent list, deduped. Dispatch fan-out agents as separate parallel Agent tool calls alongside the primary build-dispatch.py dispatch.

Gate: Stack applied. Phase 5.


Phase 5: EXECUTE

Build the task spec with request_verbatim unchanged and intent exactly PROPOSED_INTENT; include any unresolved alignment issue in gaps, then invoke build-dispatch.py:

bash
python3 "$SDIR/build-dispatch.py" --json '{
  "agent": "<JEV_RESULT.agent>", "skill": "<JEV_RESULT.skill; omit when agent-only>",
  "pipeline": "<JEV_RESULT.pipeline; omit when null>",
  "complexity": "<from Phase 2>",
  "model": "inherit",
  "context_mode": "summary",
  "provider": "<anthropic|openai|other>",
  "manual_model_override": false,
  "health": "-",
  "fallback_reason": "<REQUIRED when agent=general-purpose; omit otherwise>",
  "stack": ["<JEV_RESULT.attach, in order>", "anti-rationalization-core"],
  "task_spec": {"request_verbatim": "<user message, unchanged>", "intent": "...",
                "constraints": "<applicable rules, limits, and authorization>",
                "decisions": "...",
                "gaps": "...",
                "acceptance": "<command> -> <expected>",
                "files": "<owned paths; optional line ranges>", "ownership": "<worker scope>",
                "operator_context": "..."},
  "flags": {"worktree": false, "local_only": false, "thinking_override": null},
  "token_remaining": 480000
}'

The builder validates each name against its index, then emits the dispatch action. For Complex or creation requests, apply creation detection, plan-file gating, quality-loop, workflow dispatch, fan-out, and auto-pipeline fallback.

Gate: Agent invoked, results delivered.


Post-execute: GRILL-JEV (plan/spec/design output)

After any execution that produces a plan, spec, or design artifact, run grill-jev automatically before declaring the work complete. This applies whenever the agent's output contains phases, steps, checklists, or a structured implementation plan.

Detection: the agent wrote task_plan.md, a spec file, a design document, or the response itself is a structured plan with numbered steps or phases.

bash
# File artifact
python3 scripts/grill-jev.py --file task_plan.md --mode plan

# Inline plan (write to temp file first, then grill)
python3 scripts/grill-jev.py --file /tmp/plan_output.md --mode plan

Print the findings report. If high-signal findings exist (exit code 1):

  • Show findings to the user
  • Ask whether to address findings before proceeding or accept and move on

If no high-signal findings (exit code 0): proceed, note "grill-jev: clean".

Skip grill-jev when:

  • The output is code only (no plan structure) — use --mode code instead
  • The output is a pure research response with no actionable steps
  • grill-jev is itself the requested action (avoid recursion)

Error handling

Errors inside jev-route.py resolve to fallback: true, source: "error" — Phase 1F reports the error and fails open to /do.

When changing how the router or validator builds or sends Jev requests, apply skills/shared-patterns/jev-production-lessons.md: stage requests at or under the reliable size, send each stage's requests together, and retry by status code.

References

  • ${CLAUDE_SKILL_DIR}/references/jev-classifier-design.md — request/response contract, fallback conditions, phase-by-phase design decisions
  • ${CLAUDE_SKILL_DIR}/SPEC.md, ${CLAUDE_SKILL_DIR}/EVAL.md — maintenance contract and regression cases (load only when creating, evaluating, or redesigning this skill)
  • scripts/jev-route.py, scripts/jev-intent-align.py, scripts/jev_transport.py, scripts/jev_vercel.py, scripts/jev_gateway/jev_vercel_gateway.mjs, scripts/pre-route.py, scripts/routing-manifest.py, scripts/build-dispatch.py
  • Jev hook: hooks/jev-route-injector-userprompt.py (UserPromptSubmit) precomputes JEV_RESULT

© notque, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/meta/d of notque/vexjoy-agent.

  • SKILL.md
  • EVAL.md
  • SPEC.md
  • references/jev-classifier-design.md

Open the folder on GitHubat commit 5218674

Compare with similar skills

D next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

D compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
D this skillnotque/vexjoy-agent438—~3.8kAutomated safety check: NotesMIT
Request Body Validatorjeremylongshore/tons-of-skills-marketplace2.8k—~567Automated safety check: PassMIT
React Router Pull Request Creatorremix-run/react-router57k—~2.5kAutomated safety check: PassMIT
Form Validationthedaviddias/Front-End-Checklist74k—~633Automated safety check: PassMIT
Acp Routeropenclaw/openclaw392k—~2.4kAutomated safety check: PassMIT
Express Request ValidationPrairieLearn/PrairieLearn515—~851Automated safety check: PassCustom licence

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Questions about D

What does D do?

Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline. D is an agent skill from notque/vexjoy-agent. Jev request router: validates the requested outcome, then dispatches to the matched agent, skill, and pipeline.

How do I install D in Claude Code?

Run `npx skills add notque/vexjoy-agent --skill d -a claude-code`. Or copy the skill folder (skills/meta/d in notque/vexjoy-agent) into .claude/skills/d in your project. Claude Code loads it when a task matches its description.

How do I install D in Codex?

Run `npx skills add notque/vexjoy-agent --skill d -a codex`. Or copy the skill folder (skills/meta/d in notque/vexjoy-agent) into .agents/skills/d in your project. Codex loads it when a task matches its description.

Can I use D in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add notque/vexjoy-agent --skill d -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/d, .gemini/skills/d, .github/skills/d and .opencode/skills/d in your project.

What does D need to run?

Going by SKILL.md and its folder, D needs the command-line tools its instructions call (python3) and credentials named AI_GATEWAY_API_KEY and TYPESAFE_API_KEY. Our summary lists: Python 3; A credential in AI_GATEWAY_API_KEY; A credential in TYPESAFE_API_KEY. Its frontmatter pre-approves these tools: Read, Bash, Grep, Glob, Skill, Task.

Does D access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is D safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does D use?

D is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does D use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.8k tokens, read only when the agent opens those files.

What are the alternatives to D?

Skills that share tags, products or a category with D: Request Body Validator (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), React Router Pull Request Creator (remix-run/react-router, 57k stars), Form Validation (thedaviddias/Front-End-Checklist, 74k stars) and Acp Router (openclaw/openclaw, 392k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains D?

notque (a GitHub user) maintains it in notque/vexjoy-agent, which has 438 GitHub stars. The repository holds 61 skills in this directory. The repository was last updated on October 3, 2026.

Source: notque/vexjoy-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.