gangtise-copilot（gangtise GitHub Copilot 搜索）

Gangtise Copilot

One-command installer, credential configurator, and diagnostic layer for the full Gangtise (岗底斯投研) OpenAPI skill suite.

🚀 One-shot installation (complete flow)

This is the only section you need to read to go from zero to fully working Gangtise. Follow steps in order.

Step 1 — Download this skill to your agent's skills directory

Primary method (git clone):

git clone --depth 1 https://github.com/daymade/claude-code-skills.git /tmp/gangtise-repo
cp -r /tmp/gangtise-repo/gangtise-copilot <your-agent-skills-dir>/

Fallback method (when git clone times out or is unavailable — use GitHub API directly):

# Python one-liner to download and write any file from GitHub:
import urllib.request, json, base64, os
def fetch_github_file(repo_path, local_path):
    url = f"https://api.github.com/repos/daymade/claude-code-skills/contents/{repo_path}"
    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
    d = json.loads(urllib.request.urlopen(req, timeout=20).read())
    content = base64.b64decode(d["content"]).decode("utf-8")
    os.makedirs(os.path.dirname(local_path), exist_ok=True)
    open(local_path, "w").write(content)
    return len(content)

# Download all files:
for path, local in [
    ("gangtise-copilot/SKILL.md",                         "<skills-dir>/gangtise-copilot/SKILL.md"),
    ("gangtise-copilot/scripts/install_gangtise.sh",      "<skills-dir>/gangtise-copilot/scripts/install_gangtise.sh"),
    ("gangtise-copilot/scripts/configure_auth.sh",        "<skills-dir>/gangtise-copilot/scripts/configure_auth.sh"),
    ("gangtise-copilot/scripts/diagnose.sh",              "<skills-dir>/gangtise-copilot/scripts/diagnose.sh"),
    ("gangtise-copilot/references/installation_flow.md",   "<skills-dir>/gangtise-copilot/references/installation_flow.md"),
    ("gangtise-copilot/references/credentials_setup.md",    "<skills-dir>/gangtise-copilot/references/credentials_setup.md"),
    ("gangtise-copilot/references/skill_registry.md",       "<skills-dir>/gangtise-copilot/references/skill_registry.md"),
    ("gangtise-copilot/references/known_issues.md",         "<skills-dir>/gangtise-copilot/references/known_issues.md"),
    ("gangtise-copilot/references/best_practices.md",       "<skills-dir>/gangtise-copilot/references/best_practices.md"),
    ("gangtise-copilot/config-template/authorization.json.example", "<skills-dir>/gangtise-copilot/config-template/authorization.json.example"),
]:
    size = fetch_github_file(path, local)
    print(f"OK {path} → {local} ({size} bytes)")

Important: Network to GitHub may be unstable. If any file fails, retry the specific file up to 3 times with a 2-second delay between attempts. Do NOT abort the whole process for a single file failure.

Step 2 — Register this skill with your agent

For OpenClaw: create a symlink in the agent's skills directory and enable it in gateway config:

# Find the OpenClaw skills directory
ls ~/workspace/agent/skills/     # or ls ~/.openclaw/skills/

# Symlink this skill
ln -sf <gangtise-copilot-source-dir> <openclaw-skills-dir>/gangtise-copilot

# Enable in OpenClaw gateway config (if the agent supports skill entries in config)
# Run: openclaw config set 'skills.entries.gangtise-copilot' '{}'
# Then restart the gateway: sh scripts/restart.sh

Step 3 — Install all 19 Gangtise official skills

bash <gangtise-copilot-dir>/scripts/install_gangtise.sh --preset full

What it does: Downloads 4 ZIP bundles from the official Huawei Cloud OBS bucket, extracts 19 skill directories, and symlinks them into detected agent skills directories (~/.openclaw/skills/, ~/.claude/skills/, etc.).

Known agent skills directory paths (if auto-detection misses yours):

If install fails, check references/installation_flow.md.

Step 4 — Configure credentials

bash <gangtise-copilot-dir>/scripts/configure_auth.sh \
  --access-key <your-accessKey> \
  --secret-key <your-secretAccessKey>

What it does:

1. Writes ~/.config/gangtise/authorization.json (mode 600)
2. Performs live auth call to verify credentials work
3. Writes ~/.GTS_AUTHORIZATION runtime token
4. Creates symlinks from every installed skill's scripts/.authorization to the shared credential file

⚠️ Critical: After Step 3, diagnose.sh may report "19 skill(s) missing.authorization" even if credentials exist. Run Step 4 even when ~/.config/gangtise/authorization.json already exists — configure_auth.sh creates the missing symlinks.

Step 5 — Verify installation

bash <gangtise-copilot-dir>/scripts/diagnose.sh

Expected output: 9 pass ✅, 0 fail ❌ — all 19 skills present, credentials valid, RAG reachable.

If any ❌ or ⚠️ remains, cross-reference with references/known_issues.md.

Step 6 — Test with a real query

# Example: query latest research report for 宁德时代
# Use gangtise-file-client with its report runner:
cd <gangtise-copilot-dir>/references/
# See skill_registry.md for the exact command per skill

Overview

Gangtise is a Chinese professional investment-research data platform. It publishes an OpenAPI that covers research reports, company announcements, meeting summaries, chief analyst opinions, financial statements, valuation metrics, OHLC market data, shareholder data, industry indicators, and a catalog of pre-built research workflow skills. The underlying API is well-designed, but the skill ecosystem is not discoverable: there is no public manifest listing the 19 skills, the skills are distributed as independent ZIP files on a Huawei Cloud OBS bucket with listing permission disabled, and the skills live in two parallel naming conventions (gangtise-<name> for the minimal line, gangtise-<name>-client for the full-capability line) that carry different feature sets. A first-time user has to reverse-engineer the complete skill inventory before they can install it.

Gangtise Copilot solves this in one command:

1. Installs all 19 official Gangtise skills to Claude Code, OpenClaw, and Codex via a single bundled-download + distribute pipeline.
2. Walks the user through accessKey + secretAccessKey setup with a live authentication call against open.gangtise.com/application/auth/oauth/open/loginV2.
3. Provides a read-only diagnostic script that reports which skills are installed, which credentials are valid, and which capability tiers are reachable.
4. Exposes preset install modes so a workshop learner gets a 7-skill minimal install while a power user can get the full 19-skill catalog.

Runtime note from April 2026 usage: after installing skills, run configure_auth.sh even if ~/.config/gangtise/authorization.json already exists. Upstream CLI scripts also read ~/.GTS_AUTHORIZATION, a bare runtime token file. The configurator refreshes both files.

Architectural principles (do not violate)

This skill is a wrapper layer around the Gangtise OpenAPI skill suite. The wrapper contract is non-negotiable:

• Never vendor upstream files. This skill directory contains no copy, fork, or excerpt of any Gangtise skill content. When Gangtise ships a new release, users get the new release without any interference from this wrapper — the installer re-downloads from the canonical OBS URL every run.
• Repairs (if any arise) happen at runtime, not at ship time. This wrapper was distilled from a session that encountered no actual upstream bugs — the friction was discoverability and install orchestration, not broken files. If future upstream bugs arise, they will be added to references/known_issues.md with runtime repair instructions, not patched at ship time.
• Always ask before touching upstream files. Modifying any installed gangtise-* skill directory requires explicit user consent via AskUserQuestion.
• Teach rather than hide. Every installation step shows the user exactly which skills were downloaded, from where, and where the credential file was saved. This is how users learn to maintain their own installs.

What this skill does

Routing

When this skill is triggered, classify the user's intent and jump to the corresponding capability:

When in doubt, start with Capability 3 (diagnose.sh) — it is the only read-only entry point and it surfaces exactly which installs and credentials are currently blocked. Running it never has a destructive side effect.

Capability 1: Install Gangtise skills

Gangtise publishes 19 independent skills on a Huawei Cloud OBS bucket. They are organized into 3 bundle ZIPs plus 1 standalone ZIP. The installer downloads the 4 archives, extracts the 19 skill directories, and symlinks each one into the detected agents' skills directories.

Distribution source

All skills come from the official Gangtise OBS bucket:

https://gts-download.obs.myhuaweicloud.com/skills/

No mirrors. The installer uses this URL directly.

Bundle map

Total: 4 HTTP requests → 19 skill directories.

Two skills (gangtise-file-client-no-download and gangtise-stockpool-client) only exist inside the gangtise-skills-client bundle — they do not have standalone ZIPs. A naive "list the standalone ZIP for each skill" approach would miss them entirely. See references/known_issues.md ISSUE-002 for the full explanation.

One-command install

bash scripts/install_gangtise.sh

Flags:

bash scripts/install_gangtise.sh --preset workshop   # 7 skills for investor Workshop (Demo 1+2)
bash scripts/install_gangtise.sh --preset minimal    # 3 skills (legacy kb/file/data only)
bash scripts/install_gangtise.sh --preset full       # all 19 skills (default)
bash scripts/install_gangtise.sh --only data-client,kb-client,file-client  # custom subset
bash scripts/install_gangtise.sh --no-openclaw       # skip OpenClaw even if detected
bash scripts/install_gangtise.sh --target claude-code  # force single target

Preset contents

Capability 2: Configure credentials

Every Gangtise skill needs an .authorization credential file colocated with its Python runtime, in one of two shapes:

Shape A — accessKey + secretAccessKey (most common, auto-refreshes tokens):

{
  "accessKey": "<your-accessKey>",
  "secretAccessKey": "<your-secretAccessKey>"
}

Shape B — long-term token (advanced, for pre-generated long-lived tokens):

{
  "long-term-token": "Bearer <token>"
}

Because 19 skills each need the same .authorization file, the wrapper stores one shared file at ~/.config/gangtise/authorization.json (XDG standard, mode 600) and symlinks every skill's local credential file to it. Rotating credentials means editing one file, not 19.

Run the configurator:

bash scripts/configure_auth.sh

It will:

1. Prompt for accessKey and secretAccessKey (or read from the GANGTISE_ACCESS_KEY / GANGTISE_SECRET_KEY environment variables if set).
2. Write to ~/.config/gangtise/authorization.json with mode 600.
3. Perform a live authentication call to https://open.gangtise.com/application/auth/oauth/open/loginV2 to verify the credentials actually work.
4. Write ~/.GTS_AUTHORIZATION with the bare runtime token required by upstream CLI scripts.
5. Create symlinks from every installed skill's local credential file to the shared XDG file.
6. Report success with the uid + userName returned by the Gangtise auth server.

Credential rotation

# Edit one file:
$EDITOR ~/.config/gangtise/authorization.json

# Re-verify against the live server:
bash scripts/configure_auth.sh --verify-only

No other files need to change — the symlinks still point at the updated file.

Capability 3: Diagnose install state

bash scripts/diagnose.sh

The diagnostic script is strictly read-only. It checks:

• Which of the 19 skills are present in each detected agent's skills/ directory
• Whether ~/.config/gangtise/authorization.json exists with mode 600
• Whether each skill's local credential file is a valid symlink pointing at the shared XDG file
• Whether the stored credentials pass a live authentication call (short probe that only needs oauth/open/loginV2)
• Whether the canonical RAG endpoint responds to a minimal query (scoped liveness check — proves the credential has rag scope, not just auth scope)

Exit codes:

• 0 — all healthy
• 1 — one or more issues need user action
• 2 — diagnostic itself failed (network error, no internet, etc.)

If diagnose reports issues, cross-reference the output against references/known_issues.md. Each reported issue maps to a specific remediation section.

Capability 4: Skill registry — "which skill answers my data question?"

This is the non-obvious value of the wrapper. Gangtise's 19 skills form a two-dimensional matrix (data tier × operation type) that is not clearly documented. Use this table to route a user question to the right skill:

Data-layer skills (6)

See references/skill_registry.md for the full per-runner parameter reference and cross-skill composition examples.

Workflow-layer skills (10) — higher-order research workflows

These skills orchestrate the data-layer skills into end-to-end research workflows. They produce Markdown + HTML reports following Gangtise's professional investment-research templates and built-in compliance guardrails (no "买入 / 卖出 / 目标价 / 推荐" language).

Utility skills (3)

See references/skill_registry.md for the full per-skill script catalog, versions, and capability matrix.

What this skill refuses to do

• Vendor, fork, or mirror any gangtise-* skill's content into this directory — only the canonical OBS URLs are referenced.
• Pin an upstream skill version in SKILL.md — the installer always downloads the current OBS artifact.
• Silently patch upstream files — every modification path (if any are ever added) would require explicit consent via AskUserQuestion.
• Hardcode personal accessKey / secretAccessKey values.
• Make investment recommendations or trading decisions. Gangtise's own skills already enforce these compliance rules; this wrapper strictly delegates.

File layout

gangtise-copilot/
├── SKILL.md                         # This file
├── scripts/
│   ├── install_gangtise.sh          # Download bundles → stage → distribute
│   ├── configure_auth.sh            # Set up + verify credentials
│   └── diagnose.sh                  # Read-only health report
├── references/
│   ├── installation_flow.md         # How the installer works, flag reference, troubleshooting
│   ├── credentials_setup.md         # accessKey / secretAccessKey, XDG paths, liveness check
│   ├── skill_registry.md            # Complete per-skill capability matrix
│   ├── known_issues.md              # Two parallel product lines, bundle-only skills, and other gotchas
│   └── best_practices.md            # How to combine stock-research + opinion-pk + data-client effectively
└── config-template/
    └── authorization.json.example   # Credential file template (placeholder values only)