on-chain-analysis

Name: on-chain-analysis
Author: SKE-Labs

15stars2forks

How to install on-chain-analysis?

ske-labs/agent-trading-skills/on-chain-analysis

$npx -y skills add SKE-Labs/agent-trading-skills --skill on-chain-analysis

Installs into the current project.

Summary

Analyze on-chain metrics with provider, entity-adjustment, revision, and normalization controls. Use when evaluating holder behavior, exchange flows, valuation ratios, or network activity as contextual trading features.

Files · 1

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SKILL.md

1	# On-Chain Analysis
2
3	Interpret blockchain data to understand market participant behavior and identify macro accumulation/distribution phases.
4
5	> Note: Agent does not have direct on-chain API access. Use this framework to interpret on-chain data found via `get_financial_news`.
6
7	## Exchange Flows
8
9	\| Metric \| Accumulation hypothesis \| Distribution hypothesis \|
10	\| ----------------- \| ------------------- \| -------------- \|
11	\| Exchange net flow \| Persistent outflow \| Persistent inflow \|
12	\| Exchange reserves \| Decreasing \| Increasing \|
13	\| Stablecoin flows \| Dry-powder hypothesis \| Redemption or risk-off hypothesis \|
14
15	## Wallet Activity
16
17	\| Metric \| Growth hypothesis \| Weakness hypothesis \|
18	\| ----------------------- \| ---------- \| ---------- \|
19	\| Whale accumulation \| High \| Low \|
20	\| Long-term holder supply \| Increasing \| Decreasing \|
21	\| Active addresses \| Growing \| Declining \|
22
23	## Normalization
24
25	Do not reuse absolute BTC thresholds across eras or networks. For every metric, record provider, definition, unit, entity-adjustment status, timestamp, revision policy, and a trailing percentile or robust z-score. Multiple metrics derived from the same transaction graph are correlated and do not count as independent votes.
26
27	## MVRV Ratio
28
29	\| MVRV Range \| Interpretation \| Action \|
30	\| ---------- \| ------------------------------------- \| ---------------------- \|
31	\| High relative to its own history \| Large aggregate unrealized profit \| Distribution risk, not an automatic sale \|
32	\| Near 1 \| Market value near realized value \| Cost-basis context, not fair value \|
33	\| Below 1 \| Aggregate unrealized loss \| Stress context, not an automatic buy \|
34
35	For NVT and activity metrics, match the numerator and denominator frequency and account for batching, L2 migration, exchange-internal transfers, spam, and changes in address clustering. “Active address” is not the same as a user.
36
37	## Workflow
38
39	1. Gather on-chain intelligence from news sources:
40	```
41	get_financial_news(topic="BTC on-chain exchange outflow whale accumulation")
42	get_financial_news(topic="BTC MVRV NVT realized cap ratio")
43	```
44
45	2. Check current price and momentum for technical confirmation:
46	```
47	get_candles(symbol="BTC/USD", exchange="binance", interval="1d", count=1)
48	get_indicators(indicator_code="rsi", symbol="BTC/USD", exchange="binance", interval="1d")
49	get_indicators(indicator_code="macd", symbol="BTC/USD", exchange="binance", interval="1d")
50	```
51
52	3. Classify evidence: separate observed metric changes from provider interpretation and from the trading hypothesis. Reconcile contradictory metrics instead of counting votes.
53
54	4. Report macro thesis: on-chain regime, metric alignment count, technical confirmation status, key levels, and confidence level.
55
56	## Evidence and Validation
57
58	- Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
59	- Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
60	- Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
61	- Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
62	- Research basis: [Glassnode's MVRV methodology](https://docs.glassnode.com/guides-and-tutorials/metric-guides/mvrv/mvrv-ratio) defines the metric, while [entity-adjustment notes](https://docs.glassnode.com/guides-and-tutorials/on-chain-concepts/entity-adjusted-metrics) explain clustering and revision risk; neither supports universal cutoffs.
63
64	## Key Rules
65
66	- Match each on-chain metric's timestamp, revision latency, and tested forecast horizon; do not assume one universal horizon.
67	- NEVER convert a count of correlated metrics into “high confidence” without testing incremental information
68	- Large transfers may be internal exchange movements -- context matters
69	- On-chain data is lagging; it confirms trends rather than predicting them
70	- LTH selling = distribution phase warning; LTH accumulating = bullish macro signal
71
72	## Related Skills
73
74	- altcoin-rotation -- on-chain metrics confirm cycle phases driving rotation decisions
75	- dca-strategy -- MVRV and realized cap identify macro accumulation zones for enhanced DCA

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on-chain-analysis

15stars2forks

View on GitHub

How to install on-chain-analysis?

ske-labs/agent-trading-skills/on-chain-analysis

$npx -y skills add SKE-Labs/agent-trading-skills --skill on-chain-analysis

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# On-Chain Analysis
2
3	Interpret blockchain data to understand market participant behavior and identify macro accumulation/distribution phases.
4
5	> Note: Agent does not have direct on-chain API access. Use this framework to interpret on-chain data found via `get_financial_news`.
6
7	## Exchange Flows
8
9	\| Metric \| Accumulation hypothesis \| Distribution hypothesis \|
10	\| ----------------- \| ------------------- \| -------------- \|
11	\| Exchange net flow \| Persistent outflow \| Persistent inflow \|
12	\| Exchange reserves \| Decreasing \| Increasing \|
13	\| Stablecoin flows \| Dry-powder hypothesis \| Redemption or risk-off hypothesis \|
14
15	## Wallet Activity
16
17	\| Metric \| Growth hypothesis \| Weakness hypothesis \|
18	\| ----------------------- \| ---------- \| ---------- \|
19	\| Whale accumulation \| High \| Low \|
20	\| Long-term holder supply \| Increasing \| Decreasing \|
21	\| Active addresses \| Growing \| Declining \|
22
23	## Normalization
24
25	Do not reuse absolute BTC thresholds across eras or networks. For every metric, record provider, definition, unit, entity-adjustment status, timestamp, revision policy, and a trailing percentile or robust z-score. Multiple metrics derived from the same transaction graph are correlated and do not count as independent votes.
26
27	## MVRV Ratio
28
29	\| MVRV Range \| Interpretation \| Action \|
30	\| ---------- \| ------------------------------------- \| ---------------------- \|
31	\| High relative to its own history \| Large aggregate unrealized profit \| Distribution risk, not an automatic sale \|
32	\| Near 1 \| Market value near realized value \| Cost-basis context, not fair value \|
33	\| Below 1 \| Aggregate unrealized loss \| Stress context, not an automatic buy \|
34
35	For NVT and activity metrics, match the numerator and denominator frequency and account for batching, L2 migration, exchange-internal transfers, spam, and changes in address clustering. “Active address” is not the same as a user.
36
37	## Workflow
38
39	1. Gather on-chain intelligence from news sources:
40	```
41	get_financial_news(topic="BTC on-chain exchange outflow whale accumulation")
42	get_financial_news(topic="BTC MVRV NVT realized cap ratio")
43	```
44
45	2. Check current price and momentum for technical confirmation:
46	```
47	get_candles(symbol="BTC/USD", exchange="binance", interval="1d", count=1)
48	get_indicators(indicator_code="rsi", symbol="BTC/USD", exchange="binance", interval="1d")
49	get_indicators(indicator_code="macd", symbol="BTC/USD", exchange="binance", interval="1d")
50	```
51
52	3. Classify evidence: separate observed metric changes from provider interpretation and from the trading hypothesis. Reconcile contradictory metrics instead of counting votes.
53
54	4. Report macro thesis: on-chain regime, metric alignment count, technical confirmation status, key levels, and confidence level.
55
56	## Evidence and Validation
57
58	- Treat the setup as a testable hypothesis, not a prediction. Define thresholds, entry, invalidation, and exit before evaluating outcomes.
59	- Calibrate on the same instrument, venue, session, and timeframe. Use closed candles and a held-out or walk-forward sample; record every variant tried.
60	- Include spread, fees, slippage, borrow or funding, partial fills, and latency. Reject the setup when net expectancy is not positive or depends on one narrow parameter.
61	- Return observed inputs, missing data, cost assumptions, entry, invalidation, exit, and a valid, watch, or no-trade status.
62	- Research basis: [Glassnode's MVRV methodology](https://docs.glassnode.com/guides-and-tutorials/metric-guides/mvrv/mvrv-ratio) defines the metric, while [entity-adjustment notes](https://docs.glassnode.com/guides-and-tutorials/on-chain-concepts/entity-adjusted-metrics) explain clustering and revision risk; neither supports universal cutoffs.
63
64	## Key Rules
65
66	- Match each on-chain metric's timestamp, revision latency, and tested forecast horizon; do not assume one universal horizon.
67	- NEVER convert a count of correlated metrics into “high confidence” without testing incremental information
68	- Large transfers may be internal exchange movements -- context matters
69	- On-chain data is lagging; it confirms trends rather than predicting them
70	- LTH selling = distribution phase warning; LTH accumulating = bullish macro signal
71
72	## Related Skills
73
74	- altcoin-rotation -- on-chain metrics confirm cycle phases driving rotation decisions
75	- dca-strategy -- MVRV and realized cap identify macro accumulation zones for enhanced DCA

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6 picks

Type