agiprolabs
Registry indexed
custom-indicators
Crypto-native indicators including NVT ratio, exchange flow, funding rate signals, holder momentum, and smart money flow
Registry indexed
Crypto-native indicators including NVT ratio, exchange flow, funding rate signals, holder momentum, and smart money flow
Crypto-native indicators including NVT ratio, exchange flow, funding rate signals, holder momentum, and smart money flow
Source documentation, not instructions for this website. Review permissions before running any commands.
Traditional technical analysis was built for equities and forex — markets with fixed supply, regulated exchanges, and institutional-dominated order flow. Crypto markets have unique properties that demand purpose-built indicators:
This skill covers nine crypto-native indicators. Each section includes the formula, interpretation guide, data sources, and a working code snippet.
| File | Description |
|---|---|
references/indicator_formulas.md | Full formulas, parameter tables, signal ranges for all 9 indicators |
references/signal_interpretation.md | Composite scoring, divergence detection, false signal filtering |
scripts/compute_crypto_indicators.py | Computes all 9 indicators from free APIs or demo data |
scripts/holder_momentum.py | Holder count tracking with momentum signals |
Network Value to Transactions — the crypto equivalent of a P/E ratio.
NVT = Market Cap / Daily On-Chain Transaction Volume (USD)
def nvt_ratio(market_cap: float, daily_tx_volume_usd: float) -> float:
"""Compute NVT ratio.
Args:
market_cap: Current market capitalization in USD.
daily_tx_volume_usd: 24h on-chain transaction volume in USD.
Returns:
NVT ratio value.
"""
if daily_tx_volume_usd <= 0:
return float("inf")
return market_cap / daily_tx_volume_usd
Smoothing: Apply a 14-day or 28-day moving average to NVT (called NVT Signal) to reduce noise from daily volume spikes.
Market Value to Realized Value — compares the current market cap to the aggregate cost basis of all holders.
MVRV = Market Cap / Realized Cap
Realized Cap = Sum of (each UTXO * price when it last moved)
def mvrv_ratio(market_cap: float, realized_cap: float) -> float:
"""Compute MVRV ratio.
Args:
market_cap: Current market capitalization in USD.
realized_cap: Realized capitalization (aggregate cost basis).
Returns:
MVRV ratio value.
"""
if realized_cap <= 0:
return float("inf")
return market_cap / realized_cap
For tokens without UTXO-based realized cap, estimate using average purchase price from DEX trade history multiplied by circulating supply.
Net exchange deposits minus withdrawals — signals selling or accumulation intent.
Exchange Netflow = Deposits to Exchanges - Withdrawals from Exchanges
def exchange_netflow(
deposits_usd: float, withdrawals_usd: float
) -> tuple[float, str]:
"""Compute exchange netflow and interpret.
Returns:
Tuple of (netflow_value, signal_label).
"""
netflow = deposits_usd - withdrawals_usd
if netflow > 0:
signal = "bearish"
elif netflow < 0:
signal = "bullish"
else:
signal = "neutral"
return netflow, signal
Normalize by market cap for cross-token comparison:
Netflow Ratio = Netflow / Market Cap.
Perpetual futures contracts use funding rates to anchor price to spot.
Funding Rate = (Perp Mark Price - Spot Price) / Spot Price
(paid every 8 hours on most exchanges)
def funding_rate_signal(
rates: list[float], weights: list[float] | None = None
) -> tuple[float, str]:
"""Volume-weighted average funding rate with signal.
Args:
rates: Funding rates from multiple exchanges.
weights: Optional volume weights per exchange.
"""
import numpy as np
if weights is None:
weights = [1.0 / len(rates)] * len(rates)
vw_rate = float(np.average(rates, weights=weights))
if vw_rate > 0.0005:
signal = "bearish"
elif vw_rate < -0.0005:
signal = "bullish"
else:
signal = "neutral"
return vw_rate, signal
Tracks the rate of change in total open interest across derivatives exchanges.
OI Momentum = (OI_today - OI_n_days_ago) / OI_n_days_ago * 100
def oi_momentum(
oi_series: list[float], lookback: int = 7
) -> float:
"""Compute open interest momentum as percentage change.
Args:
oi_series: Daily open interest values (newest last).
lookback: Number of days for momentum calculation.
"""
if len(oi_series) < lookback + 1:
return 0.0
old = oi_series[-(lookback + 1)]
new = oi_series[-1]
if old <= 0:
return 0.0
return (new - old) / old * 100.0
Tracks the net change in unique token holders over time.
Holder Momentum = (Holders_today - Holders_n_days_ago) / Holders_n_days_ago
Holder Acceleration = Holder Momentum_today - Holder Momentum_yesterday
def holder_momentum(
holder_counts: list[int], lookback: int = 7
) -> tuple[float, float]:
"""Compute holder momentum and acceleration.
Returns:
Tuple of (momentum_pct, acceleration).
"""
if len(holder_counts) < lookback + 2:
return 0.0, 0.0
old = holder_counts[-(lookback + 1)]
new = holder_counts[-1]
prev_old = holder_counts[-(lookback + 2)]
prev_new = holder_counts[-2]
mom = (new - old) / old if old > 0 else 0.0
prev_mom = (prev_new - prev_old) / prev_old if prev_old > 0 else 0.0
accel = mom - prev_mom
return mom, accel
See scripts/holder_momentum.py for a full tracking implementation.
A composite metric combining order book depth, bid-ask spread, and DEX pool depth to estimate how easily a position can be entered/exited.
Liquidity Score = w1 * Depth Score + w2 * Spread Score + w3 * Pool Score
Where:
min(1, total_bids_within_2pct / target_position_size)max(0, 1 - spread_bps / 100)min(1, pool_tvl / (target_position_size * 10))w1=0.4, w2=0.3, w3=0.3def liquidity_score(
depth_usd: float,
spread_bps: float,
pool_tvl: float,
position_size: float,
weights: tuple[float, float, float] = (0.4, 0.3, 0.3),
) -> float:
"""Composite liquidity score from 0 (illiquid) to 1 (highly liquid)."""
depth_s = min(1.0, depth_usd / position_size) if position_size > 0 else 0
spread_s = max(0.0, 1.0 - spread_bps / 100.0)
pool_s = min(1.0, pool_tvl / (position_size * 10)) if position_size > 0 else 0
return weights[0] * depth_s + weights[1] * spread_s + weights[2] * pool_s
Net buying pressure from wallets identified as "smart money" (historically profitable, large balances, early entry patterns).
Smart Money Flow = Sum(smart_wallet_buys_usd) - Sum(smart_wallet_sells_usd)
SMF Ratio = Smart Money Flow / Total Volume
def smart_money_flow(
smart_buys_usd: float,
smart_sells_usd: float,
total_volume_usd: float,
) -> tuple[float, float, str]:
"""Compute smart money flow and ratio.
Returns:
Tuple of (net_flow, smf_ratio, signal).
"""
net = smart_buys_usd - smart_sells_usd
ratio = net / total_volume_usd if total_volume_usd > 0 else 0.0
if ratio > 0.1:
signal = "bullish"
elif ratio < -0.1:
signal = "bearish"
else:
signal = "neutral"
return net, ratio, signal
Measures how frequently a token changes hands relative to its supply.
Token Velocity = Daily Trading Volume (tokens) / Circulating Supply
def token_velocity(
daily_volume_tokens: float, circulating_supply: float
) -> tuple[float, str]:
"""Compute token velocity.
Returns:
Tuple of (velocity, interpretation).
"""
if circulating_supply <= 0:
return 0.0, "unknown"
vel = daily_volume_tokens / circulating_supply
if vel > 0.3:
interp = "high_speculation"
elif vel > 0.1:
interp = "moderate"
elif vel > 0.05:
interp = "low"
else:
interp = "very_low_strong_holders"
return vel, interp
No single indicator is reliable in isolation. See
references/signal_interpretation.md for guidance on:
uv pip install httpx pandas numpy
All indicators and analysis provided by this skill are for informational and educational purposes only. They do not constitute financial advice. Always conduct your own research b
name: custom-indicators description: Crypto-native indicators including NVT ratio, exchange flow, funding rate signals, holder momentum, and smart money flow
---
name: custom-indicators
description: Crypto-native indicators including NVT ratio, exchange flow, funding rate signals, holder momentum, and smart money flow
---
# Custom Crypto Indicators
## Why Standard TA Falls Short for Crypto
Traditional technical analysis was built for equities and forex — markets with
fixed supply, regulated exchanges, and institutional-dominated order flow.
Crypto markets have unique properties that demand purpose-built indicators:
- **On-chain transparency**: Every transaction is public. We can measure real
economic activity, not just price and volume on a single exchange.
- **Supply mechanics**: Fixed or programmatic supply schedules make
supply-side analysis (velocity, holder distribution) meaningful.
- **Derivatives dominance**: Perpetual futures funding rates and open interest
often drive spot price, not the other way around.
- **Whale concentration**: A small number of wallets hold outsized supply.
Tracking their behavior provides alpha that equity-market TA cannot.
- **Exchange flows**: On-chain deposit/withdrawal to centralized exchanges
signals intent to sell or accumulate.
This skill covers nine crypto-native indicators. Each section includes the
formula, interpretation guide, data sources, and a working code snippet.
## Files
| File | Description |
|------|-------------|
| `references/indicator_formulas.md` | Full formulas, parameter tables, signal ranges for all 9 indicators |
| `references/signal_interpretation.md` | Composite scoring, divergence detection, false signal filtering |
| `scripts/compute_crypto_indicators.py` | Computes all 9 indicators from free APIs or demo data |
| `scripts/holder_momentum.py` | Holder count tracking with momentum signals |
---
## Indicator 1: NVT Ratio
**Network Value to Transactions** — the crypto equivalent of a P/E ratio.
```
NVT = Market Cap / Daily On-Chain Transaction Volume (USD)
```
- **High NVT (> 65)**: Network is overvalued relative to its economic
throughput. Bearish signal.
- **Low NVT (< 25)**: Network is undervalued or seeing heavy real usage.
Bullish signal.
- **Data sources**: CoinGecko (market cap), blockchain explorers or
DeFiLlama (transaction volume).
```python
def nvt_ratio(market_cap: float, daily_tx_volume_usd: float) -> float:
"""Compute NVT ratio.
Args:
market_cap: Current market capitalization in USD.
daily_tx_volume_usd: 24h on-chain transaction volume in USD.
Returns:
NVT ratio value.
"""
if daily_tx_volume_usd <= 0:
return float("inf")
return market_cap / daily_tx_volume_usd
```
**Smoothing**: Apply a 14-day or 28-day moving average to NVT (called
NVT Signal) to reduce noise from daily volume spikes.
---
## Indicator 2: MVRV Ratio
**Market Value to Realized Value** — compares the current market cap to the
aggregate cost basis of all holders.
```
MVRV = Market Cap / Realized Cap
Realized Cap = Sum of (each UTXO * price when it last moved)
```
- **MVRV > 3.5**: Most holders are in deep profit. Distribution likely.
- **MVRV < 1.0**: Most holders are underwater. Historically marks bottoms.
- **Data sources**: Glassnode, CryptoQuant (Bitcoin/Ethereum). For Solana
tokens, approximate via average entry price of top holders.
```python
def mvrv_ratio(market_cap: float, realized_cap: float) -> float:
"""Compute MVRV ratio.
Args:
market_cap: Current market capitalization in USD.
realized_cap: Realized capitalization (aggregate cost basis).
Returns:
MVRV ratio value.
"""
if realized_cap <= 0:
return float("inf")
return market_cap / realized_cap
```
For tokens without UTXO-based realized cap, estimate using average purchase
price from DEX trade history multiplied by circulating supply.
---
## Indicator 3: Exchange Flow
**Net exchange deposits minus withdrawals** — signals selling or accumulation
intent.
```
Exchange Netflow = Deposits to Exchanges - Withdrawals from Exchanges
```
- **Positive netflow (large deposits)**: Holders moving tokens to exchanges,
likely to sell. Bearish.
- **Negative netflow (withdrawals)**: Tokens leaving exchanges to cold
storage. Bullish accumulation signal.
- **Data sources**: CryptoQuant, Glassnode. For Solana SPL tokens, track
transfers to known exchange wallets via Helius or Solana RPC.
```python
def exchange_netflow(
deposits_usd: float, withdrawals_usd: float
) -> tuple[float, str]:
"""Compute exchange netflow and interpret.
Returns:
Tuple of (netflow_value, signal_label).
"""
netflow = deposits_usd - withdrawals_usd
if netflow > 0:
signal = "bearish"
elif netflow < 0:
signal = "bullish"
else:
signal = "neutral"
return netflow, signal
```
Normalize by market cap for cross-token comparison:
`Netflow Ratio = Netflow / Market Cap`.
---
## Indicator 4: Funding Rate Signal
Perpetual futures contracts use funding rates to anchor price to spot.
```
Funding Rate = (Perp Mark Price - Spot Price) / Spot Price
(paid every 8 hours on most exchanges)
```
- **Highly positive (> 0.05%)**: Longs pay shorts. Market is overleveraged
long. Contrarian bearish.
- **Highly negative (< -0.05%)**: Shorts pay longs. Overleveraged short.
Contrarian bullish.
- **Data sources**: Binance, Bybit, dYdX APIs. Aggregate across exchanges
for a volume-weighted average.
```python
def funding_rate_signal(
rates: list[float], weights: list[float] | None = None
) -> tuple[float, str]:
"""Volume-weighted average funding rate with signal.
Args:
rates: Funding rates from multiple exchanges.
weights: Optional volume weights per exchange.
"""
import numpy as np
if weights is None:
weights = [1.0 / len(rates)] * len(rates)
vw_rate = float(np.average(rates, weights=weights))
if vw_rate > 0.0005:
signal = "bearish"
elif vw_rate < -0.0005:
signal = "bullish"
else:
signal = "neutral"
return vw_rate, signal
```
---
## Indicator 5: Open Interest Momentum
Tracks the rate of change in total open interest across derivatives exchanges.
```
OI Momentum = (OI_today - OI_n_days_ago) / OI_n_days_ago * 100
```
- **Rising OI + Rising Price**: New money entering longs. Trend
confirmation.
- **Rising OI + Falling Price**: New shorts opening. Bearish pressure.
- **Falling OI + Rising Price**: Short squeeze / closing shorts.
- **Falling OI + Falling Price**: Long liquidation.
- **Data sources**: CoinGlass, Binance, Bybit open interest endpoints.
```python
def oi_momentum(
oi_series: list[float], lookback: int = 7
) -> float:
"""Compute open interest momentum as percentage change.
Args:
oi_series: Daily open interest values (newest last).
lookback: Number of days for momentum calculation.
"""
if len(oi_series) < lookback + 1:
return 0.0
old = oi_series[-(lookback + 1)]
new = oi_series[-1]
if old <= 0:
return 0.0
return (new - old) / old * 100.0
```
---
## Indicator 6: Holder Momentum
Tracks the net change in unique token holders over time.
```
Holder Momentum = (Holders_today - Holders_n_days_ago) / Holders_n_days_ago
Holder Acceleration = Holder Momentum_today - Holder Momentum_yesterday
```
- **Accelerating growth**: Viral adoption phase. Bullish.
- **Decelerating growth**: Adoption slowing. Watch for reversal.
- **Negative momentum**: Holders leaving. Bearish.
- **Data sources**: Helius DAS API (Solana), Etherscan token holder count,
Birdeye holder stats.
```python
def holder_momentum(
holder_counts: list[int], lookback: int = 7
) -> tuple[float, float]:
"""Compute holder momentum and acceleration.
Returns:
Tuple of (momentum_pct, acceleration).
"""
if len(holder_counts) < lookback + 2:
return 0.0, 0.0
old = holder_counts[-(lookback + 1)]
new = holder_counts[-1]
prev_old = holder_counts[-(lookback + 2)]
prev_new = holder_counts[-2]
mom = (new - old) / old if old > 0 else 0.0
prev_mom = (prev_new - prev_old) / prev_old if prev_old > 0 else 0.0
accel = mom - prev_mom
return mom, accel
```
See `scripts/holder_momentum.py` for a full tracking implementation.
---
## Indicator 7: Liquidity Score
A composite metric combining order book depth, bid-ask spread, and DEX pool
depth to estimate how easily a position can be entered/exited.
```
Liquidity Score = w1 * Depth Score + w2 * Spread Score + w3 * Pool Score
```
Where:
- **Depth Score** = `min(1, total_bids_within_2pct / target_position_size)`
- **Spread Score** = `max(0, 1 - spread_bps / 100)`
- **Pool Score** = `min(1, pool_tvl / (target_position_size * 10))`
- Default weights: `w1=0.4, w2=0.3, w3=0.3`
```python
def liquidity_score(
depth_usd: float,
spread_bps: float,
pool_tvl: float,
position_size: float,
weights: tuple[float, float, float] = (0.4, 0.3, 0.3),
) -> float:
"""Composite liquidity score from 0 (illiquid) to 1 (highly liquid)."""
depth_s = min(1.0, depth_usd / position_size) if position_size > 0 else 0
spread_s = max(0.0, 1.0 - spread_bps / 100.0)
pool_s = min(1.0, pool_tvl / (position_size * 10)) if position_size > 0 else 0
return weights[0] * depth_s + weights[1] * spread_s + weights[2] * pool_s
```
---
## Indicator 8: Smart Money Flow
Net buying pressure from wallets identified as "smart money" (historically
profitable, large balances, early entry patterns).
```
Smart Money Flow = Sum(smart_wallet_buys_usd) - Sum(smart_wallet_sells_usd)
SMF Ratio = Smart Money Flow / Total Volume
```
- **SMF Ratio > 0.1**: Smart money is net accumulating. Bullish.
- **SMF Ratio < -0.1**: Smart money is distributing. Bearish.
- **Data sources**: Helius transaction parsing + wallet labeling, Birdeye
wallet analytics, Nansen (Ethereum).
```python
def smart_money_flow(
smart_buys_usd: float,
smart_sells_usd: float,
total_volume_usd: float,
) -> tuple[float, float, str]:
"""Compute smart money flow and ratio.
Returns:
Tuple of (net_flow, smf_ratio, signal).
"""
net = smart_buys_usd - smart_sells_usd
ratio = net / total_volume_usd if total_volume_usd > 0 else 0.0
if ratio > 0.1:
signal = "bullish"
elif ratio < -0.1:
signal = "bearish"
else:
signal = "neutral"
return net, ratio, signal
```
---
## Indicator 9: Token Velocity
Measures how frequently a token changes hands relative to its supply.
```
Token Velocity = Daily Trading Volume (tokens) / Circulating Supply
```
- **High velocity (> 0.3)**: Speculative trading dominates. Token is being
flipped, not held. Can precede dumps.
- **Low velocity (< 0.05)**: Holders are sitting tight. Strong hands.
- **Data sources**: CoinGecko (volume, supply), DEX aggregator volumes.
```python
def token_velocity(
daily_volume_tokens: float, circulating_supply: float
) -> tuple[float, str]:
"""Compute token velocity.
Returns:
Tuple of (velocity, interpretation).
"""
if circulating_supply <= 0:
return 0.0, "unknown"
vel = daily_volume_tokens / circulating_supply
if vel > 0.3:
interp = "high_speculation"
elif vel > 0.1:
interp = "moderate"
elif vel > 0.05:
interp = "low"
else:
interp = "very_low_strong_holders"
return vel, interp
```
---
## Combining Indicators
No single indicator is reliable in isolation. See
`references/signal_interpretation.md` for guidance on:
- Building composite scores from multiple indicators
- Detecting divergences (e.g., price rising but NVT expanding)
- Adjusting interpretation by market regime
- Filtering false signals
## Dependencies
```bash
uv pip install httpx pandas numpy
```
## Disclaimer
All indicators and analysis provided by this skill are for informational and
educational purposes only. They do not constitute financial advice. Always
conduct your own research bSkill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
72/100
Strong
Trust
57/100
Do not auto-install
Audit
75/100
Risky
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"version": "trust-score-v4",
"install_policy": "block",
"evidence": {
"stars": "344 GitHub stars",
"repoActivity": "344 stars, 69 forks",
"lastPushed": "6d since push",
"license": "MIT",
"repository": "https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/custom-indicators",
"install": "npx skills add agiprolabs/claude-trading-skills --skill custom-indicators",
"installSafety": "standard package or runtime install path",
"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Strong README/SKILL.md context",
"agentOutcomes": "No agent outcome data yet"
},
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"successes": 0,
"failures": 0,
"not_relevant": 0,
"success_rate": null,
"recent_success_rate": null,
"recent_failure_rate": null,
"install_attempts": 0,
"install_success_rate": null,
"risk_blocked": 0,
"setup_required": 0,
"avg_output_quality": null,
"production_outcomes": 0,
"last_outcome_at": null,
"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"automation",
"agent-skill"
],
"known_risks": [
"The skill directory does not include its own license file, but the repository is MIT-licensed, which is acceptable.",
"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 75,
"risk_level": "risky",
"risk_label": "Risky",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
"The skill directory does not include its own license file, but the repository is MIT-licensed, which is acceptable.",
"The SKILL.md excerpt is truncated, but the provided content is clear and well-structured.",
"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval."
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 72,
"label": "Strong"
},
"supply": {
"track": "Research and knowledge work",
"scenario": "Research agents",
"maintenance": "6d since push",
"risk": "Risky"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"The skill directory does not include its own license file, but the repository is MIT-licensed, which is acceptable.",
"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision"
],
"agent_contract": {
"task_input": "Use custom-indicators in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 65/100 Manual review",
"Audit: 75/100 Risky",
"Safety: 35/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "agiprolabs-custom-indicators (custom-indicators)",
"install_command": "npx skills add agiprolabs/claude-trading-skills --skill custom-indicators",
"risk_summary": "Risky; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "agiprolabs-custom-indicators",
"task": "Use custom-indicators in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/agiprolabs-custom-indicators",
"api": "https://www.openagentskill.com/api/agent/skills/agiprolabs-custom-indicators",
"audit": "https://www.openagentskill.com/skills/agiprolabs-custom-indicators/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=agiprolabs-custom-indicators&task=Use%20custom-indicators%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20custom-indicators%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20custom-indicators%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/agiprolabs-custom-indicators/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/agiprolabs-custom-indicators"
}
}Listing source
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