technical-analysis

technical-analysis is a skill for Claude Code, Codex from Bitget-AI/bitget-signal. It costs 202 tokens per session (2,452 once invoked), scanned A, original, MIT.

A crypto-market analysis skill covering 23 indicators in six groups, including trends, volatility, momentum, volume, and support or resistance. It uses recent time-series data so the agent can examine how indicators change over time.

In plain words
What is it for?
It is for analyzing cryptocurrencies with specified indicators, or choosing indicators for goals such as trend, momentum, volatility, volume, and money-flow analysis.
Why use it?
It helps interpret market direction and conditions such as overbought or oversold levels without relying on a single price reading.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/bitget-ai/bitget-signal/technical-analysis
Any agent
npx skills add Bitget-AI/bitget-signal --skill technical-analysis
Clone the repo
git clone --depth 1 https://github.com/Bitget-AI/bitget-signal

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for technical-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/bitget-ai/bitget-signal/technical-analysis.svg)](https://agentmods.dev/skills/bitget-ai/bitget-signal/technical-analysis)
Your own site
<a href="https://agentmods.dev/skills/bitget-ai/bitget-signal/technical-analysis"><img src="https://agentmods.dev/badge/skills/bitget-ai/bitget-signal/technical-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 202 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,452 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00202 $0.02452
Opus 5 $0.00101 $0.01226
Sonnet 5 $0.00040 $0.00490
Haiku 4.5 $0.00020 $0.00245

Measured 4d ago against content hash 2c5960b4c478, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

technical-analysis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (src/kline_indicator_utils.py, src/kline_indicators.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

import json, urllib.request
skills/technical-analysis/SKILL.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Official Bitget Skill · 本 Skill 由 Bitget 官方提供,市场数据来源可信,通过 Bitget Agent Hub 分发。 Data powered by Bitget market infrastructure · github.com/bitget-official/agent-hub

Technical Analysis Skill

23 crypto technical indicators across 6 categories (Trend, Volatility, Oscillator, Volume, Momentum, Support/Resistance). Outputs recent time-series data so the AI can observe trend evolution, not just a single point.

Vendor Neutrality

Present data as coming from "market data" — never name the underlying exchange, data feed, or library.


Step 1: Check prerequisites

python -c "import pandas, numpy; print('OK')"

If missing:

pip install pandas numpy

Step 2: Determine indicator selection

Priority 1: User-defined
  The user explicitly specified indicator names, parameters, or combinations.
  -> Do NOT read scenarios.md or indicators.md
  -> Go directly to Step 3 with the user's config
  -> Example: "analyze BTC with RSI(21) + EMA(50) + ATR"

Priority 2: Scenario defaults
  The user described analysis intent but did not specify indicators.
  -> Read ONLY references/scenarios.md to pick the right scenario config
  -> Do NOT read indicators.md
  -> Example: "how's BTC looking technically?"

Priority 3: Indicator info query
  The user asks about an indicator rather than requesting a calculation.
  -> Read ONLY references/indicators.md
  -> No calculation needed, answer directly
  -> Example: "how does RSI work?"

Step 3: Run the calculation

The Python source is at src/ relative to this SKILL.md file.

For Claude Code the skill directory is ~/.claude/skills/technical-analysis. For Codex it is ~/.codex/skills/technical-analysis. For OpenClaw it is ~/.openclaw/skills/technical-analysis.

Template A: Fetch from Bitget API (default)

When the user mentions a trading pair but provides no local data:

import sys, os
sys.path.insert(0, os.path.expanduser('~/.claude/skills/technical-analysis/src'))
import json, urllib.request
import pandas as pd
from kline_indicator_utils import IndicatorManager

url = 'https://api.bitget.com/api/v2/spot/market/candles?symbol={SYMBOL}&granularity={GRANULARITY}&limit={LIMIT}'
raw = json.loads(urllib.request.urlopen(url).read())
df = pd.DataFrame(raw['data'], columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'quoteVol', 'amount'])
for col in ['open', 'high', 'low', 'close', 'volume', 'amount']:
    df[col] = df[col].astype(float)

config = {CONFIG}

manager = IndicatorManager(show_indicators=False)
output = manager.calculate_and_export(config, df, tail={TAIL})
output["symbol"] = "{SYMBOL}"
output["granularity"] = "{GRANULARITY}"
print(json.dumps(output, indent=2))

Read the full file on GitHub · 271 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 271 lines · 202 tokens per session scan A 2c5960b4c478

Subscribe to this mod's changes

technical-analysis is a skill published in the GitHub repository Bitget-AI/bitget-signal (2 stars, last pushed 2mo ago), licensed MIT. It adds 202 tokens to every session and 2,452 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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