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.
npx agentmods add skills/bitget-ai/bitget-signal/technical-analysisnpx skills add Bitget-AI/bitget-signal --skill technical-analysisgit clone --depth 1 https://github.com/Bitget-AI/bitget-signalWrote 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.
[](https://agentmods.dev/skills/bitget-ai/bitget-signal/technical-analysis)<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>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.
| Model | Per session | Once 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 |
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.
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 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))
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.
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.
- 4d ago First seen · 271 lines · 202 tokens per session scan A 2c5960b4c478
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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