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Gangtise Copilot

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daymade
gangtise-copilot

Gangtise (岗底斯投研) OpenAPI skill suite installer and diagnostic tool. One-click install 19 official skills (data, research, utility), configure accessKey/secretAccessKey, run health diagnostics. Trigger when user mentions Gangtise, 岗底斯, any gangtise-* skill, credential setup, or reports errors like 'token is invalid' / '接口地址错误'.

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill namegangtise-copilot
Stars
1.4K
Forks
219
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by daymade on GitHub. Read the source before you install it.

Installation

Install the Gangtise Copilot AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/daymade-financial/gangtise-copilot .claude/skills/gangtise-copilot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gangtise Copilot in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gangtise Copilot on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gangtise Copilot is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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):

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

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

python
# 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 [
    ("daymade-financial/gangtise-copilot/SKILL.md",                         "<skills-dir>/gangtise-copilot/SKILL.md"),
    ("daymade-financial/gangtise-copilot/scripts/install_gangtise.sh",      "<skills-dir>/gangtise-copilot/scripts/install_gangtise.sh"),
    ("daymade-financial/gangtise-copilot/scripts/configure_auth.sh",        "<skills-dir>/gangtise-copilot/scripts/configure_auth.sh"),
    ("daymade-financial/gangtise-copilot/scripts/diagnose.sh",              "<skills-dir>/gangtise-copilot/scripts/diagnose.sh"),
    ("daymade-financial/gangtise-copilot/scripts/classify_response.py",     "<skills-dir>/gangtise-copilot/scripts/classify_response.py"),
    ("daymade-financial/gangtise-copilot/references/installation_flow.md",   "<skills-dir>/gangtise-copilot/references/installation_flow.md"),
    ("daymade-financial/gangtise-copilot/references/credentials_setup.md",    "<skills-dir>/gangtise-copilot/references/credentials_setup.md"),
    ("daymade-financial/gangtise-copilot/references/skill_registry.md",       "<skills-dir>/gangtise-copilot/references/skill_registry.md"),
    ("daymade-financial/gangtise-copilot/references/known_issues.md",         "<skills-dir>/gangtise-copilot/references/known_issues.md"),
    ("daymade-financial/gangtise-copilot/references/best_practices.md",       "<skills-dir>/gangtise-copilot/references/best_practices.md"),
    ("daymade-financial/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:

bash
# 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 using OpenClaw's own script, not this skill's:
# sh <openclaw-install-dir>/scripts/restart.sh

Step 3 — Install all 19 Gangtise official skills

bash
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):

AgentSkills directory
Claude Code~/.claude/skills/
Codex~/.agents/skills/
OpenClaw~/.openclaw/skills/ or <agent-workspace>/skills/

If install fails, check references/installation_flow.md.

Step 4 — Configure credentials

bash
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 each installed skill's own <gangtise-skill-dir>/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
bash <gangtise-copilot-dir>/scripts/diagnose.sh

Expected output: no lines. The exact pass count depends on which agents and preset are installed. A successful OAuth check only proves that the credentials can mint a token; the RAG line separately reports reachable, empty success, permission denial, authentication rejection, or a quota/entitlement response.

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

Step 6 — Test with a real query

bash
# 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 (minimal / workshop / full) so users can match the install size to what their account license actually permits — see ISSUE-007 in references/known_issues.md for why "biggest install" is not the safe default.

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

CapabilityEntry pointDetail
1. Install Gangtise skills (minimal default, workshop alias, full, or --only custom)scripts/install_gangtise.shSee references/installation_flow.md
2. Configure accessKey + secretAccessKey credentialsscripts/configure_auth.shSee references/credentials_setup.md
3. Diagnose install state, credential validity, and capability tiersscripts/diagnose.shSee references/known_issues.md
4. Look up which Gangtise skill answers a specific data questionSkill registry below + references/skill_registry.md

Routing

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

User says something like…Go to
"装 gangtise"、"install gangtise"、"我想用 gangtise 的数据"、"把 gangtise 的 skill 都装上"One-shot installation (Step 1–5 above)
"配 gangtise 的 key"、"configure gangtise credentials"、"gangtise accessKey"、"secretAccessKey"Capability 2
"gangtise 报错"、"token is invalid"、"接口地址错误"、"gangtise skill 加载失败"、"我的 gangtise 装得不对"Capability 3
"宁德时代的研报"、"过去 30 天的首席观点"、"OHLC 蜡烛图"、"个股研究报告 L2"、"对宁德时代做观点 PK"Capability 4 → skill registry → invoke the matching upstream skill
"帮我从头跑一遍 gangtise"One-shot installation (Step 1–5 in sequence)

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

BundleSizeContains
gangtise-skills-client.zip160 KBdata-client, kb-client, file-client, file-client-no-download, stockpool-client
gangtise-research.zip220 KBstock-research, opinion-pk, thematic-research, stock-selector, event-review, interview-outline, announcement-digest, opinion-summarizer, wechat-summary, data-processor
gangtise-skills.zip118 KBdata (v1.2.0), file, kb — the legacy "minimal" parallel line
gangtise-web-client.zip8 KBweb-client (standalone, not in any bundle)

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
bash scripts/install_gangtise.sh

Flags:

bash
bash scripts/install_gangtise.sh --preset minimal    # default — 3 skills via public open-* endpoints
bash scripts/install_gangtise.sh --preset workshop   # alias for minimal (same 3 skills)
bash scripts/install_gangtise.sh --preset full       # all 19 skills (most -client will fail without skills-backend ACL)
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

PresetSkillsIntended for
minimal (default)gangtise-data, gangtise-file, gangtise-kbConservative install for accounts with access to the public open-* endpoints. It avoids the ISSUE-007 skills-backend/* ACL, while API points and product permissions still apply. Covers OHLC, financials, announcements, foreign reports, and RAG retrieval.
workshop(alias for minimal — same 3 skills)Historical preset bundled 7 -client-heavy skills, but those are blocked by ISSUE-007 on most accounts and produce a broken live demo. The preset now points at the same 3 skills as minimal so it can no longer footgun a workshop.
fullAll 19 skillsBoth lines side-by-side. Useful for exploring the full Gangtise catalog. Most -client skills will fail at runtime if your account lacks skills-backend/* ACL — confirm with the diagnostic in ISSUE-007 first.

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):

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

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

json
{
  "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
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

bash
# 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
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. The result distinguishes a successful response with matches, a successful empty response, authentication rejection, permission denial, a quota/entitlement response, malformed response, and network failure. It retains HTTP status, API code, errorType, and traceId without printing credentials or the complete token response.

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)

Want to…Upstream skillInvoke
Query semantic content across knowledge base (reports + opinions + minutes)gangtise-kb-clientkb runner with -q query + optional --file-types / --securities
List documents by type + date + security (reports, announcements, summaries, opinions, roadshows)gangtise-file-clientdedicated runners per document type (report / opinion / summary / announcement / investment_calendar / foreign_report / internal_report / wechat_message)
Pull OHLC daily candles for an A-share or HK stockgangtise-data-clientquote runner with --securities {name} + -sd / -ed date range
Pull financial statements (income / balance / cash flow indicators)gangtise-data-clientfinancial runner with --securities {name} + --indicators
Pull valuation metrics (PE / PS / PB / PEG + historical percentiles)gangtise-data-clientvaluation runner with --securities {name}
Pull main business composition (by product / industry / region)gangtise-data-clientmain_business runner with --securities {name} + --classify-method
Pull shareholder / top-holder datagangtise-data-clientshareholder runner with --securities {name}
Pull macro / industry indicators (GDP, CPI, vehicle sales, commodity prices)gangtise-data-clientindustry_indicator runner with -k {keyword}
Look up security standard codes by namegangtise-data-clientsecurity runner with -k {name}
List sector constituent stocks by theme or industrygangtise-data-clientblock_component runner with -k {theme}
List index members by categorygangtise-data-clientindex runner with -k {index type}
Search the open web for public information not in Gangtise's internal KBgangtise-web-clientweb runner with -q {query}

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).

Want to…Use
Generate a stock research report at L1-L4 depth (L1 = 1-page framework, L4 = full institutional coverage)gangtise-stock-research
Do adversarial analysis on an investment thesis ("play devil's advocate for this long call")gangtise-opinion-pk
Do thematic / sector research (driver analysis, enumeration phase, stock screening, performance check)gangtise-thematic-research
Screen stocks based on research criteriagangtise-stock-selector
Write an 800-1000 word event review / post-mortem for a market eventgangtise-event-review
Generate a company-meeting outline (3-step workflow: data → topics → questions)gangtise-interview-outline
Track recent announcements for a stock pool and produce a daily digestgangtise-announcement-digest
Summarize a chief analyst's recent opinionsgangtise-opinion-summarizer
Turn a WeChat chat-group discussion log into a structured investment dailygangtise-wechat-summary
Get methodology guidance on how to design a custom data-processing workflowgangtise-data-processor

Utility skills (3)

SkillPurpose
gangtise-stockpool-clientCreate / rename / delete a stock pool; add or remove stocks from it. Only distributed inside gangtise-skills-client.zip.
gangtise-file-client-no-downloadVariant of file-client that disables the download capability — useful in read-only environments or compliance-sensitive contexts.
Legacy gangtise-data / gangtise-file / gangtise-kbThe older minimal parallel line. data is v1.2.0 with strictly-typed security codes (no name resolution). Only install if the user wants the smaller feature footprint.

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)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gangtise Copilot AI skill do?

Gangtise (岗底斯投研) OpenAPI skill suite installer and diagnostic tool. One-click install 19 official skills (data, research, utility), configure accessKey/secretAccessKey, run health diagnostics. Trigger when user mentions Gangtise, 岗底斯, any gangtise-* skill, credential setup, or reports errors like 'token is invalid' / '接口地址错误'.

Why use Gangtise Copilot on TypingMind?

Because you install it once and use it with any model. Gangtise Copilot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gangtise Copilot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/daymade-financial/gangtise-copilot. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gangtise Copilot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gangtise Copilot?

As many as you like. As long as a model supports skills, you can use Gangtise Copilot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gangtise Copilot AI skill free?

Yes. It is published on GitHub by daymade under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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