Agent skill

Consult Zai

by centminmod in centminmod/my-claude-code-setup

Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher — a lightweight two-model second opinion.

MITAuto-check passedAgent Workflows

Install Consult Zai

skills CLI
$ npx skills add centminmod/my-claude-code-setup --skill consult-zai -a claude-code

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

GitHub CLI
$ gh skill install centminmod/my-claude-code-setup consult-zai --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/centminmod/my-claude-code-setup.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/consult-zai .claude/skills/consult-zai && 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
consult-zai
GitHub stars
2.7k
Token cost
~4.1k tokens
SKILL.md length
1,411 words
Files
1
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher — a lightweight two-model second opinion.

  • Works in 5 steps: Build Enhanced Prompt → Invoke Both Analyses in Parallel → Cleanup Temp Files → …
  • A quick z.ai-backed check on a code question
  • SKILL.md covers When to Use This Skill, Workflow, z.ai (GLM 5.2) Response and Code-Searcher (Claude) Response, plus 6 more sections
  • Calls jq, brew and git; needs ANTHROPIC_AUTH_TOKEN

What it does

Consult Zai is an agent skill from centminmod/my-claude-code-setup. Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher — a lightweight two-model second opinion. Use for a quick z.ai-backed check on a code question.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. It works with Zhipu GLM. The repository describes itself as: Shared starter template configuration and CLAUDE.md memory bank system for Claude Code. The licence is MIT.

When your agent uses it

  • A quick z.ai-backed check on a code question

Example prompts

  • “/consult-zai”

Requirements

  • A credential in ANTHROPIC_AUTH_TOKEN

Workflow steps

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

  1. Build Enhanced Prompt
  2. Invoke Both Analyses in Parallel
  3. Cleanup Temp Files
  4. Handle Errors
  5. Create Comparison Analysis

What it can do on your machine

Read from SKILL.md and the folder at commit f93acbc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq
    • brew
    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, 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 these keys or tokens, usually read from environment variables:

    • ANTHROPIC_AUTH_TOKEN

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

Context cost

Consult Zai loads about 4.1k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 1,411 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~44
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

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 passed

The automated check found no risky patterns in SKILL.md.

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 centminmod/my-claude-code-setup at commit f93acbc, republished under its MIT licence (© centminmod). 1,411 words, ~4,099 tokens.

Download SKILL.mdSave it as .claude/skills/consult-zai/SKILL.md (or your agent's skills folder).
name
consult-zai
description
Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher — a lightweight two-model second opinion. Use for a quick z.ai-backed check on a code question.

Dual-AI Consultation: z.ai GLM 5.2 vs Code-Searcher

You orchestrate consultation between z.ai's GLM 5.2 model and Claude's code-searcher to provide comprehensive analysis with comparison.

When to Use This Skill

High value queries:

  • Complex code analysis requiring multiple perspectives
  • Debugging difficult issues
  • Architecture/design questions
  • Code review requests
  • Finding specific implementations across a codebase

Lower value (single AI may suffice):

  • Simple syntax questions
  • Basic file lookups
  • Straightforward documentation queries

Workflow

When the user asks a code question:

1. Build Enhanced Prompt

Problem-restate pre-flight (non-blocking). Before building the prompt, emit ONE line restating the code question you are about to dispatch (and, only if genuinely ambiguous, the alternative reading), then proceed:

Reading this as: «one-line restatement» (alt: «other reading», if any) — proceeding to consult; interrupt now to correct the framing.

Emit-and-proceed — do not ask-and-wait (the orchestrator can't reliably detect its own misframing). One line, and it guards the whole dispatch against a wrong-framing run.

Wrap the user's question with structured output requirements:

[USER_QUESTION]

=== Analysis Guidelines ===

**Structure your response with:**
1. **Summary:** 2-3 sentence overview
2. **Key Findings:** bullet points of discoveries
3. **Evidence:** file paths with line numbers (format: `file:line` or `file:start-end`)
4. **Confidence:** High/Medium/Low with reasoning
5. **Limitations:** what couldn't be determined

**Line Number Requirements:**
- ALWAYS include specific line numbers when referencing code
- Use format: `path/to/file.ext:42` or `path/to/file.ext:42-58`
- For multiple references: list each on a SEPARATE line with its own file path
  (avoid comma-separated multi-citation like `file.ts:45, 67, 98`)
- Include brief code snippets for key findings

**Examples of good citations:**
- "The authentication check at `src/auth/validate.ts:127-134`"
- "Configuration loaded from `config/settings.json:15`"
- "Error handling in `lib/errors.ts:45`, `lib/errors.ts:67-72`, and `lib/errors.ts:98`"

**Citations Index (required):** end your response with a fenced block, one line per
Key Finding (repeat each block entry's `file:line` inline in the finding as usual):
```citations
<finding #> — path/to/file.ext:LINE[-END]
```

Severity / no-manufacture block — ORCHESTRATOR-GATED. Append the block below to both agents' prompts identically ONLY when the query is a defect hunt / code review (bug, security audit, "what's wrong with…", "review this"). OMIT it for explanatory / "how does X work" questions, where "found nothing" is not meaningful. The orchestrator — which knows the query type — makes this include/omit decision once, BEFORE writing the prompt files; do not leave it to each agent to self-classify. When included, append exactly these two bullets (the text only — no leading marker):

- Tag each finding with a **Severity** — Critical (wrong/broken on expected inputs) · Warning (fails on unusual but valid inputs) · Info (noteworthy, not actionable). Severity is *impact*, orthogonal to the Confidence field (*certainty*).
- **Finding nothing is a valid, valuable result.** If the code is correct, say so plainly with one verifying note — do NOT manufacture issues to look thorough.
2. Invoke Both Analyses in Parallel

Setup (run first). $CLAUDE_PROJECT_DIR is not always exported into the Bash tool shell, so resolve it with a $PWD fallback and ensure the tmp dir exists. Substitute the resolved literal path for $PROJECT_DIR, and a freshly generated RUN_ID (seconds-resolution + 4-char nonce, e.g. run-2026-07-04-143052-a7f3), into every command below. The RUN_ID in temp filenames prevents collisions between two concurrent invocations sharing $PROJECT_DIR/tmp.

bash
PROJECT_DIR="${CLAUDE_PROJECT_DIR:-$PWD}"
# Validate BEFORE creating tmp — `mkdir -p` would otherwise make the check pass even
# for a bad path (it creates the dir, then `[ -d ]` always succeeds).
[ -d "$PROJECT_DIR" ] || { echo "ERROR: PROJECT_DIR '$PROJECT_DIR' is not a directory" >&2; exit 1; }
mkdir -p "$PROJECT_DIR/tmp"

# Pre-flight (fail fast, not after a 20-min hang). jq is a HARD dependency — the §2a
# parse recipe needs it — so abort now rather than warn-and-continue into opaque failures.
command -v jq >/dev/null 2>&1 || { echo "ERROR: 'jq' not found — required for output parsing; aborting" >&2; exit 1; }
# zai is a soft dependency (a shell function wrapping the claude CLI against z.ai's
# endpoint, loaded from ~/.zshrc or ~/.bashrc — hence the interactive-shell probes).
# Capture WHICH interactive shell resolves it; the dispatch below substitutes
# $INTERACTIVE_SHELL so a .bashrc-only setup on macOS still works. If neither shell
# resolves zai, skip its dispatch and label the run degraded (see §2 dispatch + §4).
ZAI_AVAIL=1; INTERACTIVE_SHELL=zsh
if   zsh  -i -c 'type zai' >/dev/null 2>&1; then ZAI_AVAIL=0; INTERACTIVE_SHELL=zsh
elif bash -i -c 'type zai' >/dev/null 2>&1; then ZAI_AVAIL=0; INTERACTIVE_SHELL=bash
else echo "WARNING: 'zai' not found in zsh or bash interactive shells — z.ai will be skipped"
fi
echo "ZAI_AVAIL=$ZAI_AVAIL"                   # MUST echo: shell vars don't persist across Bash tool calls
echo "INTERACTIVE_SHELL=$INTERACTIVE_SHELL"   # substitute into the Step-2 dispatch below

# Sweep stale orphans (>60 min) from crashed prior runs (best-effort, age-based —
# can theoretically delete a live run's files if it paused >60 min; acceptable).
find "$PROJECT_DIR/tmp" -maxdepth 1 -name 'zai-prompt-*.txt'  -mmin +60 -delete 2>/dev/null
find "$PROJECT_DIR/tmp" -maxdepth 1 -name 'zai-output-*.json' -mmin +60 -delete 2>/dev/null
find "$PROJECT_DIR/tmp" -maxdepth 1 -name 'zai-stderr-*.log'  -mmin +60 -delete 2>/dev/null

# Resolve the timeout binary used to wrap the Step-2 z.ai dispatch so a hung CLI is
# bounded rather than running unbounded — the harness may auto-background the dispatch,
# letting it escape the Bash tool's own timeout. Homebrew coreutils installs GNU
# timeout as `gtimeout`; plain `timeout` exists only when the gnubin PATH is on. If
# neither exists, TIMEOUT_CMD stays empty → dispatch UNWRAPPED (best-effort;
# `brew install coreutils` restores the hard guard).
TIMEOUT_CMD=""
if   command -v timeout  >/dev/null 2>&1; then TIMEOUT_CMD="timeout"
elif command -v gtimeout >/dev/null 2>&1; then TIMEOUT_CMD="gtimeout"
fi
echo "TIMEOUT_CMD=$TIMEOUT_CMD"   # substitute into the Step-2 dispatch (when empty: omit the wrap)

Two-phase dispatch (required). Tool calls in one message run concurrently, so emitting the z.ai prompt-file Write and the z.ai dispatch together races the dispatch ahead of the file existing (the cat pipes an empty/missing file). Use two messages: message 1 writes the z.ai prompt file (Step 1 below); message 2 issues the z.ai dispatch (Step 2) and the Code-Searcher Agent call in parallel.

Gen-dispatch timeout watchdog (GEN_TIMEOUT=1200). The z.ai dispatch is wrapped in $TIMEOUT_CMD -k 10 1200 (resolved in Setup) — SIGTERM at 1200s (20 min), SIGKILL 10s later (-k 10, reaps orphaned Node/MCP children). This bounds a hung z.ai CLI that could otherwise run unbounded (the harness may auto-background the dispatch, so the Bash tool's own timeout is not a reliable cap). When TIMEOUT_CMD is empty: omit the $TIMEOUT_CMD -k 10 1200 prefix and dispatch unwrapped — set the Bash tool's own timeout parameter to 1300000 ms as a best-effort cap, and brew install coreutils to restore the hard guard. On a timed-out dispatch (exit 124 = SIGTERM, 137 = SIGKILL): the output file is empty/truncated, so the §2a [ -z … ] parse guard drops the agent — treat z.ai as failed per §4 (present Code-Searcher's response and note the timeout; do NOT retry). Code-Searcher (Agent tool) is not wrapped — it bounds itself.

  • For z.ai GLM 5.2:

    Step 1: Write the enhanced prompt to a temp file using the Write tool:

    Write to $PROJECT_DIR/tmp/zai-prompt-RUN_ID.txt with the ENHANCED_PROMPT content

    Step 2: Execute z.ai (skip if Setup echoed ZAI_AVAIL=1 — no working zai; present only the Code-Searcher response and label the report a degraded single-AI run: no cross-comparison, and note a direct Read or lighter path would have been cheaper). Pipe the prompt via stdin and capture output/stderr to files ($INTERACTIVE_SHELL = the zsh|bash literal resolved in Setup):

    bash
    cat "$PROJECT_DIR/tmp/zai-prompt-RUN_ID.txt" | \
      $TIMEOUT_CMD -k 10 1200 $INTERACTIVE_SHELL -i -c "zai --bare --print --output-format json --model 'glm-5.2[1m]' --allowedTools 'Read,Grep,Glob' --disallowedTools 'Bash,Edit,Write,NotebookEdit,WebFetch,WebSearch,Task,KillShell,BashOutput' --add-dir '$PROJECT_DIR'" \
      > "$PROJECT_DIR/tmp/zai-output-RUN_ID.json" \
      2> "$PROJECT_DIR/tmp/zai-stderr-RUN_ID.log"

    Why this exact form (each piece prevents a failure seen in practice):

    • --bare is required — zai exports ANTHROPIC_AUTH_TOKEN; without --bare the parent session's OAuth token shadows it → 401 against the z.ai endpoint.
    • --model 'glm-5.2[1m]' is required — guarantees GLM 5.2 regardless of which tier default resolution would pick (guards against the glm-5-turbo subagent default).
    • Read-only enforcement = --allowedTools 'Read,Grep,Glob' plus the load-bearing --disallowedTools 'Bash,Edit,Write,NotebookEdit,WebFetch,WebSearch,Task,KillShell,BashOutput' — --allowedTools only auto-approves and does NOT deny unlisted tools, so a global ~/.claude/settings.json allow-list would otherwise re-permit write-capable Bash on the --bare path (verified 2026-07-18); consultation is analysis, never modification. Freeze the reviewed tree while agents run: you, the orchestrator, must not edit, git checkout/stash/reset, or otherwise mutate the reviewed source (your own $PROJECT_DIR/tmp scratch files are exempt) from the first dispatch until the final report — a slow agent still reading would see the tree shift mid-analysis and can rat-hole producing no report.
    • stdin pipe (cat … | …) instead of -p "$(cat …)" avoids shell-quoting breakage and ARG_MAX limits on large prompts.
    • --add-dir '$PROJECT_DIR' — outer-shell single-quote expansion of the absolute path gives z.ai project context; never pass the dir via an inner-shell positional (skill argument substitution rewrites positional tokens).
    • stderr captured separately; on a 401/auth failure it carries the diagnostic.
  • For Code-Searcher: Use Agent tool with subagent_type: "code-searcher" with the same enhanced prompt (plus the orchestrator-gated Severity block above on defect-hunt runs)

    • No sub-agent fan-out — append this VERBATIM to the code-searcher prompt: "Do this analysis YOURSELF — do NOT spawn sub-agents. Do not use the Agent/Task tool to fan out to code-searcher, Explore, general-purpose, or any other subagent; use Read/Grep/Glob/Bash directly, however many calls that takes." Code-searcher runs with all tools and fans out unprompted on Sonnet 5 (reported live 2026-08-01); a sub-agent inherits none of this run's constraints, and the Agent-tool call carries no dispatch watchdog — a stalled fan-out underneath it stalls the whole consult. (consult-panel §1d carries the full form of this guard.)

This parallel execution significantly improves response time.

Show full SKILL.md (443 more words)Show less
2a. Parse z.ai JSON Output (jq Recipe)

zai --print --output-format json emits a JSON array (possibly prefixed with ANSI/OSC terminal escapes — iTerm2 shell-integration codes); rarely the CLI returns a bare object instead of an array. Canonical slice, then a bare-object fallback:

bash
ZAI_FILE="$PROJECT_DIR/tmp/zai-output-RUN_ID.json"
response=$(jq -Rrs '
  (try (match("\\[\\s*\\{[\\s\\S]*\\]").string) catch empty)
  | fromjson?
  | .[]
  | select(.type=="result")
  | .result // empty
' "$ZAI_FILE")
# Fallback (rare — primary slice empty on a NON-empty file): salvage ONLY a genuine
# result/message/assistant shape.
if [ -z "$response" ] && [ -s "$ZAI_FILE" ]; then
  response=$(jq -Rrs '
    (try (match("\\{[\\s\\S]*\\}").string) catch empty)
    | fromjson?
    | if   .type=="result"    then (.result // empty)
      elif .type=="message"   then ((.content[]? | select(.type=="text") | .text) // empty)
      elif .type=="assistant" then ((.message.content[]? | select(.type=="text") | .text) // empty)
      else empty end
  ' "$ZAI_FILE")
fi
[ -z "$response" ] && echo "ERROR: z.ai produced no result event — check the stderr log (and the --bare / --model 'glm-5.2[1m]' flags)" >&2
printf '%s\n' "$response"
3. Cleanup Temp Files

After processing the z.ai response, clean up the temp files — on a FAILURE, do this only AFTER §4 has quoted the stderr tail (cleanup deletes the diagnostic):

bash
rm -f "$PROJECT_DIR/tmp/zai-prompt-RUN_ID.txt" \
      "$PROJECT_DIR/tmp/zai-output-RUN_ID.json" \
      "$PROJECT_DIR/tmp/zai-stderr-RUN_ID.log"

This prevents stale prompts from accumulating and avoids potential confusion in future runs.

4. Handle Errors
  • If one agent fails or times out, still present the successful agent's response
  • Note the failure in the comparison: "Agent X failed to respond: [error message]"
  • On a z.ai failure, quote the tail of zai-stderr-RUN_ID.log (auth/endpoint diagnostics live there) before cleanup
  • Provide analysis based on the available response; with only one agent, label the report a degraded single-AI run (no cross-comparison)
5. Create Comparison Analysis

Use this exact format:


z.ai (GLM 5.2) Response

[Raw output from zai-cli agent]


Code-Searcher (Claude) Response

[Raw output from code-searcher agent]


Comparison Table

(MANDATORY — always render this table on a multi-agent run; it is the at-a-glance visual diff readers rely on, so never skip it. Omit only in a degraded single-AI run, where there is nothing to compare.)

Aspectz.ai (GLM 5.2)Code-Searcher (Claude)
File paths[Specific/Generic/None][Specific/Generic/None]
Line numbers[Provided/Missing][Provided/Missing]
Code snippets[Yes/No + details][Yes/No + details]
Unique findings[List any][List any]
Accuracy[Note discrepancies][Note discrepancies]
Strengths[Summary][Summary]

Agreement Level

  • High Agreement: Both AIs reached similar conclusions - Higher confidence in findings
  • Partial Agreement: Some overlap with unique findings - Investigate differences
  • Disagreement: Contradicting findings - Manual verification recommended

[State which level applies and explain]

Findings by Corroboration

Bucket each distinct finding by how many agents independently reached it:

  • Corroborated — both agents report it. Highest trust as a consensus signal — this dual has no citation-verification stage, so it is agreement, not verified correctness.
  • Solo — reported by one agent only. Plausible but unconfirmed.
  • Disputed — the agents contradict on the point. Flag for manual verification.

(Cluster findings across agents by their claim + file:line — the Citations Index blocks make this pairing mechanical. On a defect-hunt run, tag each listed finding with its agent-assigned Severity — Critical/Warning/Info.)

Key Differences

  • z.ai GLM 5.2: [unique findings, strengths, approach]
  • Code-Searcher: [unique findings, strengths, approach]

Synthesized Summary

[Combine the best insights from both sources into unified analysis. Prioritize findings that are:

  1. Corroborated by both agents
  2. Supported by specific file:line citations
  3. Include verifiable code snippets]

Recommendation

[Which source was more helpful for this specific query and why. Consider:

  • Accuracy of file paths and line numbers
  • Quality of code snippets provided
  • Completeness of analysis
  • Unique insights offered]

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

Files

Just SKILL.md in .claude/skills/consult-zai of centminmod/my-claude-code-setup.

Open the folder on GitHubat commit f93acbc

Compare with similar skills

Consult Zai 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.

Consult Zai compared with similar skills
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Auto Review Loop LLMAI4Scientist/nano-scientist1284 repos~1.8kAutomated safety check: WarnNone
Glm Delegationathola/claude-night-market342—~1.1kAutomated safety check: PassMIT
Moai Ref Cross Model Auditmodu-ai/moai-adk1.2k—~4.8kAutomated safety check: PassApache-2.0
Higress Openclaw Integrationhigress-group/higress9.5k—~2.5kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Consult Zai

What does Consult Zai do?

Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher — a lightweight two-model second opinion. Consult Zai is an agent skill from centminmod/my-claude-code-setup.2 with Claude code-searcher — a lightweight two-model second opinion.

When should I use Consult Zai?

Consult Zai fits situations like: A quick z.ai-backed check on a code question.

How do I install Consult Zai in Claude Code?

Run `npx skills add centminmod/my-claude-code-setup --skill consult-zai -a claude-code`. Or copy the skill folder (.claude/skills/consult-zai in centminmod/my-claude-code-setup) into .claude/skills/consult-zai in your project. Claude Code loads it when a task matches its description.

How do I install Consult Zai in Codex?

Run `npx skills add centminmod/my-claude-code-setup --skill consult-zai -a codex`. Or copy the skill folder (.claude/skills/consult-zai in centminmod/my-claude-code-setup) into .agents/skills/consult-zai in your project. Codex loads it when a task matches its description.

Can I use Consult Zai 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 centminmod/my-claude-code-setup --skill consult-zai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/consult-zai, .gemini/skills/consult-zai, .github/skills/consult-zai and .opencode/skills/consult-zai in your project.

What does Consult Zai need to run?

Going by SKILL.md and its folder, Consult Zai needs the command-line tools its instructions call (jq, brew and git) and credentials named ANTHROPIC_AUTH_TOKEN. Our summary lists: A credential in ANTHROPIC_AUTH_TOKEN.

Does Consult Zai access the network?

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

Is Consult Zai safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Consult Zai use?

Consult Zai 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 Consult Zai use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Consult Zai?

Skills that share tags, products or a category with Consult Zai: Gh Issues (trpc-group/trpc-agent-go, 1.8k stars), Auto Review Loop LLM (AI4Scientist/nano-scientist, 128 stars), Glm Delegation (athola/claude-night-market, 342 stars) and Moai Ref Cross Model Audit (modu-ai/moai-adk, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Consult Zai?

centminmod (a GitHub user) maintains it in centminmod/my-claude-code-setup, which has 2,655 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on September 28, 2026.

Source: centminmod/my-claude-code-setup on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.