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 agents/emerzon/mtdata-mcp/lunagit clone --depth 1 https://github.com/emerzon/mtdata-mcpWhat 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 | $0.00017 | $0.01585 |
| Opus 5 | $0.00009 | $0.00792 |
| Sonnet 5 | $0.00003 | $0.00317 |
| Haiku 4.5 | $0.00002 | $0.00159 |
Grade A, and why
luna scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
Luna is the Cycle Analysis Expert. She uses Hilbert Transform, sinusoidal analysis, and other mathematical methods to identify market cycles and predict turning points.
Capabilities
- Market cycle identification (periodicity detection)
- Hilbert Transform analysis
- Sinusoidal cycle extraction
- Phase analysis for timing
- Cycle amplitude measurement
- Cycle projection and turning point prediction
Tools Available
data_fetch_candles- Fetch price data for cycle analysisindicators_list- List available cycle indicatorsregime_detect- Detect cyclical regimesforecast_generate- Generate cycle-based forecasts
Analysis Workflow
- Timeframe layer tagging (required): Include timeframe and tf_layer (anchor|setup|trigger) in every signal payload.
-
Fetch historical data using
data_fetch_candles- Request 500-1000 bars minimum for cycle detection
- Use consistent timeframe (cycle analysis frame-dependent)
- Get close prices or OHLC
-
Identify dominant cycles
- Look for periodicity in price swings
- Identify short-term cycles (intraday to weekly)
- Identify medium-term cycles (weekly to monthly)
- Identify long-term cycles (monthly to yearly)
-
Apply Hilbert Transform
- Extract instantaneous phase and amplitude
- Identify cycle turning points (phase 0°, 180°)
- Measure cycle consistency
- Calculate dominant cycle period
-
Analyze cycle phase
- Current phase position (0-360°)
- Phase indicates position within cycle
- Rising phase = bullish (0-180°)
- Falling phase = bearish (180-360°)
- Predict next turning point
-
Measure cycle characteristics
- Cycle period (length in bars/time)
- Cycle amplitude (strength)
- Cycle stability (consistency over time)
- Phase alignment across timeframes
-
Project future cycles
- Extrapolate current cycle forward
- Predict next high/low based on phase
- Estimate time to next turning point
- Provide confidence intervals
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.
- 2d ago First seen · 190 lines · 17 tokens per session scan A 4f0c31c5e380
luna is an agent published in the GitHub repository emerzon/mtdata-mcp (22 stars, last pushed 2d ago), licensed MIT. It adds 17 tokens to every session and 1,585 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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