Agent skill

Process

by notque in notque/vexjoy-agent

Process: retrospectives, session handoff, pair programming, subagent-driven development, condition-based waiting.

MITAuto-check: notesAgent Workflows

Install Process

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

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

GitHub CLI
$ gh skill install notque/vexjoy-agent process --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/process/process .claude/skills/process && 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
process
GitHub stars
435
Token cost
~2.1k tokens
SKILL.md length
843 words
Files
7 (incl. references)
Skills in repo
61
Repo updated
First seen
Licence
MIT

At a glance

Process: retrospectives, session handoff, pair programming, subagent-driven development, condition-based waiting.

  • Works in 3 steps: SETUP → EXECUTE (per task) → FINALIZE
  • Tasks that involve Subagents
  • SKILL.md covers Mode: Retro, Mode: Handoff, Mode: Pair and Mode: Subagent, plus 2 more sections
  • Calls git, python3 and gh

What it does

Process is an agent skill from notque/vexjoy-agent. Process: retrospectives, session handoff, pair programming, subagent-driven development, condition-based waiting.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `adr-reviewer-prompt.md`, `code-quality-reviewer-prompt.md` and `implementer-prompt.md`).

It sits in Agent Workflows, covering Subagents, Retrospectives and Session handoff. 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.

When your agent uses it

  • Tasks that involve Subagents
  • Tasks that involve Retrospectives
  • Tasks that involve Session handoff

Example prompts

  • “/process”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Grep, Glob, Task, Agent

Workflow steps

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

  1. SETUP
  2. EXECUTE (per task)
  3. FINALIZE

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
    • Write
    • Edit
    • Bash
    • Grep
    • Glob
    • Task
    • Agent

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • python3
    • gh

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

  • Network

    No URLs in SKILL.md. Its commands use git and gh, which can reach the network depending on how they are called.

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Process loads about 2.1k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 30 tokens; SKILL.md has 843 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

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, Write, Edit, Bash, Grep, Glob, Task, Agent

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). 843 words, ~2,099 tokens.

Download SKILL.mdSave it as .claude/skills/process/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
process
description
Process: retrospectives, session handoff, pair programming, subagent-driven development, condition-based waiting.
allowed-tools
Read, Write, Edit, Bash, Grep, Glob, Task, Agent
user-invocable
true
argument-hint
[retro|handoff|pair|subagent|wait]
routing.not_for
multi-phase feature work (use workflow), code review (use review)
routing.triggers
what didn't work, negative results, route health, routing telemetry, review roi, hand off this session, package session state, session pickup, rehydrate…
routing.category
process
routing.pairs_with
workflow, testing, pr-workflow

Process Skill

Five modes. Parse the request to pick one. Default to retro when ambiguous.

SignalMode
Negative results, what didn't work, routing stats, review ROI, retroRetro
Hand off, package state, session pickup, rehydrateHandoff
Pair program, step by step, walk me through, one change at a timePair
Subagent per task, execute plan, fresh contextSubagent
Wait for, retry, backoff, poll, health check, circuit breakerWait

Mode: Retro

Read-only retrospective. Two stores: docs/what-didnt-work.md (negative-results registry) and learning.db (routing/review telemetry via scripts/learning-db.py).

Subcommands
ArgumentAction
(none), what-didnt-workRead docs/what-didnt-work.md. Group by date. Show each Decision verdict up front. If missing, report no negative results recorded.
routing, route healthRun python3 ~/.claude/scripts/learning-db.py route-health, then route-stats --by agent (or the user's dimension). Present outcome basis before rates.
reviews, review ROIRun learning-db.py review-roi and review-fps. Present cost, findings, and false positives per agent in one table.

For comparisons: learning-db.py route-delta --from SHA_OR_DATE --to SHA_OR_DATE.


Mode: Handoff

Two submodes sharing one state contract. HANDOFF packages work for the next session. PICKUP rehydrates from that package.

Handoff (ending/pausing work)

Produce a bullet package with these sections:

  1. Scope/status -- task in one line, finished vs. remaining, blockers.
  2. Working tree -- git status -sb; note unpushed commits and worktree path.
  3. Branch/PR -- branch, PR URL, CI status (gh pr checks).
  4. Live processes -- summarize relevant processes with attach/tail commands. Redact secrets.
  5. Tests/checks -- commands, results, checked revision, log paths.
  6. Next steps -- remaining actions in execution order.
  7. Risks/gotchas -- flaky tests, feature flags, approvals needed.

Gate: every process has a copy-paste command; every pending step is ordered.

Pickup (starting on existing work)
  1. Read the handoff and repository instructions.
  2. Confirm branch, local commits, worktree path (git status -sb).
  3. Check CI/PR (gh pr view --comments).
  4. Check live processes from the handoff. Attach or tail logs.
  5. Rerun only invalidated or missing checks.
  6. Write next 2-3 actions as bullets, then execute.

Gate: branch, PR state, and first action confirmed. Observed state wins over handoff.

Constraints: separate observed from inherited results. Redact secrets. Scale detail to the next action.


Mode: Pair

Announce-Show-Wait-Apply-Verify micro-step protocol. Stay in the main session (forks cannot conduct user gates).

Setup

Read the request and code. Show a numbered plan (one logical change per step). Wait for acknowledgment. Track current step, remaining steps, and speed.

Per-step cycle
  1. Announce the change and reason (1-2 sentences).
  2. Show the proposed diff (default 15 lines, cap 50). Split larger changes into sub-steps.
  3. Wait for a control command.
  4. Apply only after ok / yes / y.
  5. Verify with relevant checks. Report result in one sentence.
CommandAction
ok/yes/yApply current step, propose next
no/nSkip, propose alternative
fasterDouble step size (cap 50)
slowerHalve step size (min 5)
skipSkip to next step
planShow remaining steps
doneEnd pairing, run final verification

"Just do it" = switch to autonomous mode for remaining authorized work.


Mode: Subagent

Execute a plan with fresh implementer subagents. Planning owns the plan format; this mode owns dispatch and integration.

Show full SKILL.md (340 more words)Show less
Phase 1: SETUP
  1. Read the plan once. Extract tasks with text, files, dependencies, and verify commands.
  2. Track tasks as pending/in-progress/complete.
  3. Capture BASE_SHA (git rev-parse HEAD), project conventions, and relevant context.
  4. Check scope overlap: python3 scripts/check-scope-overlap.py --tasks '<json>'. Serialize overlapping writes; parallelize independent groups.

Gate: tasks, BASE_SHA, context, and scope checks ready.

Phase 2: EXECUTE (per task)
  1. Mark in-progress. Dispatch implementer with full task text and context.
  2. Dispatch ADR compliance reviewer. Fix requirement failures before code quality.
  3. Dispatch code quality reviewer. Fix Critical and Important findings; Minor optional.
  4. Mark complete. After three failed reviews at either stage, stop and report.

Gate: requirements and code quality pass per task.

Phase 3: FINALIZE
  1. Review combined BASE_SHA..HEAD diff for cross-task conflicts and integration issues.
  2. Follow the authorized completion path (pr-workflow).

Gate: combined changes work, required checks pass.


Mode: Wait

Implement condition-based polling and retry patterns with bounded timeouts.

Pattern selection
ScenarioPattern
Wait for condition to become trueSimple poll (timeout + min interval)
Retry failing operationExponential backoff (max retries + jitter + delay cap)
API returns 429Rate limit recovery (Retry-After + fallback)
Wait for service(s) to startHealth check (all-pass + per-check status)
Prevent cascade failuresCircuit breaker (failure threshold + recovery timeout)
Shared rules
  • Read CLAUDE.md and search for existing wait/retry patterns first.
  • Every loop needs a mandatory timeout. No infinite waits.
  • Use time.monotonic() for elapsed time, never time.time().
  • Minimum poll interval: 10ms in-process, 100ms external services.
  • Jitter is mandatory on exponential backoff (prevents thundering herd).
  • Classify errors before retrying: 408/429/500/502/503/504 are retryable; 400/401/403/404 are not.
  • Log each attempt with failure reason and attempt number.
  • Test both success and timeout/exhaustion paths.
Simple poll core
python
start = time.monotonic()
while time.monotonic() < start + timeout:
    if condition():
        return result
    time.sleep(interval)
raise TimeoutError(f"Timeout waiting for: {description}")
Exponential backoff core
python
for attempt in range(max_retries + 1):
    try:
        return operation()
    except retryable_exceptions:
        if attempt >= max_retries: raise
        jitter = 1.0 + random.uniform(-0.5, 0.5)
        time.sleep(min(delay * jitter, max_delay))
        delay = min(delay * backoff_factor, max_delay)

For full implementations (rate-limited client, health check waiter, circuit breaker), detection commands, and test patterns, load the deep references.


Deep References

SignalLoadContent
Writing wait/retry implementationsreferences/cbw-implementation-patterns.mdComplete Python/Bash code for all 5 patterns
Reviewing wait/retry code for mistakesreferences/cbw-preferred-patterns.mdDetection commands and fixes for common mistakes
Testing wait/retry codereferences/cbw-testing-patterns.mdpytest patterns for polling, backoff, circuit breaker

© 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 6 other files (references) in skills/process/process of notque/vexjoy-agent.

  • SKILL.md
  • adr-reviewer-prompt.md
  • code-quality-reviewer-prompt.md
  • implementer-prompt.md
  • references/cbw-implementation-patterns.md
  • references/cbw-preferred-patterns.md
  • references/cbw-testing-patterns.md

Open the folder on GitHubat commit 5218674

Compare with similar skills

Process 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.

Process compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Process this skillnotque/vexjoy-agent435—~2.1kAutomated safety check: NotesMIT
Badstephenleo/bmad-autonomous-development107—~7.7kAutomated safety check: PassMIT
Skill Retrokenneth-liao/ai-launchpad-marketplace126—~2.1kAutomated safety check: PassNone
Orchestratorbacknotprop/orchestrator118—~1.7kAutomated safety check: PassCustom licence
RetrospectiveSzotasz/marveen115—~1.5kAutomated safety check: PassMIT
Wf Playercatlog22/Claude-Code-Workflow2.1k—~1.3kAutomated safety check: NotesMIT

Similar skills

  • Bad

    stephenleo/bmad-autonomous-development

    BMad Autonomous Development — orchestrates parallel story implementation pipelines.

    107 GitHub stars~7.7k tokensUpdated 5 mo ago
    Agent WorkflowsAuto-check passed
  • Skill Retro

    kenneth-liao/ai-launchpad-marketplace

    A skill your agent uses when reviewing how skills performed during a session, when the user wants to analyze skill invocations and identify improvements, or when the user says "skill retro", "review…

    126 GitHub stars~2.1k tokensUpdated 6 mo ago
    Product & Project ManagementAuto-check passed
  • Orchestrator

    backnotprop/orchestrator

    Use Orchestrator as a CLI-backed skill to delegate and coordinate work across Claude Code, Codex, Copilot, Grok, Pi, shell, and custom runtimes.

    118 GitHub stars~1.7k tokensUpdated 2 mo ago
    Agent WorkflowsAuto-check passed
  • Retrospective

    Szotasz/marveen

    Analyze the current session for improvement opportunities in skills, memory, and workflow.

    115 GitHub stars~1.5k tokensUpdated yesterday
    Product & Project ManagementAuto-check passed
  • Wf Player

    catlog22/Claude-Code-Workflow

    Workflow template player — load a JSON template produced by wf-composer, bind context variables, execute nodes in DAG order (serial/parallel), persist state at checkpoints, support resume from any…

    2.1k GitHub stars~1.3k tokensUpdated 3 mo ago
    Agent WorkflowsAuto-check: notes
  • Session Retrospective

    jpicklyk/task-orchestrator

    Analyzes the current implementation run — evaluates schema effectiveness, delegation alignment, note quality, and plan-to-execution fit.

    207 GitHub stars~9.5k tokensUpdated yesterday
    Product & Project ManagementAuto-check passed

More from notque/vexjoy-agent

All 61 skills in this repo
  • Game Asset Generator

    notque/vexjoy-agent

    Deterministic palette/matrix pixel art (not AI). An agent skill from notque/vexjoy-agent.

    435 GitHub stars~2.3k tokensUpdated 4 days ago
    Auto-check: notes
  • PR Workflow

    notque/vexjoy-agent

    Pull request lifecycle: commit, codex review, sync, review, fix, status, cleanup, and PR mining.

    435 GitHub stars~2.8k tokensUpdated 4 days ago
    Auto-check: notes
  • Architecture Deepening

    notque/vexjoy-agent

    Improve architecture across modules by deepening interfaces.

    435 GitHub stars~3.3k tokensUpdated 4 days ago
    Auto-check: notes
  • Code Quality

    notque/vexjoy-agent

    Code quality: cleanup, linting, formatting, quality gates. An agent skill from notque/vexjoy-agent.

    435 GitHub stars~1.5k tokensUpdated 4 days ago
    Auto-check: notes
  • Codebase Analyzer

    notque/vexjoy-agent

    Statistical rule discovery from Go codebase patterns. An agent skill from notque/vexjoy-agent.

    435 GitHub stars~2k tokensUpdated 4 days ago
    Auto-check: notes
  • Comment Quality

    notque/vexjoy-agent

    Review and fix temporal references in code comments. An agent skill from notque/vexjoy-agent.

    435 GitHub stars~2k tokensUpdated 4 days ago
    Auto-check: notes

Questions about Process

What does Process do?

Process: retrospectives, session handoff, pair programming, subagent-driven development, condition-based waiting. Process is an agent skill from notque/vexjoy-agent. Process: retrospectives, session handoff, pair programming, subagent-driven development, condition-based waiting.

When should I use Process?

Process fits situations like: tasks that involve Subagents; tasks that involve Retrospectives; tasks that involve Session handoff.

How do I install Process in Claude Code?

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

How do I install Process in Codex?

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

Can I use Process 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 process -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/process, .gemini/skills/process, .github/skills/process and .opencode/skills/process in your project.

What does Process need to run?

Going by SKILL.md and its folder, Process needs the command-line tools its instructions call (git, python3 and gh). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Grep, Glob, Task, Agent.

Does Process access the network?

SKILL.md contains no URLs. Its commands use git and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Process 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 Process use?

Process 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 Process use?

About 2.1k tokens (SKILL.md is roughly 8.4k 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 8.1k tokens, read only when the agent opens those files.

What are the alternatives to Process?

Skills that share tags, products or a category with Process: Bad (stephenleo/bmad-autonomous-development, 107 stars), Skill Retro (kenneth-liao/ai-launchpad-marketplace, 126 stars), Orchestrator (backnotprop/orchestrator, 118 stars) and Retrospective (Szotasz/marveen, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Process?

notque (a GitHub user) maintains it in notque/vexjoy-agent, which has 435 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.