agiprolabs
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feature-engineering
Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features
Registry indexed
Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features
Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features
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Feature engineering is the single highest-leverage activity in building ML trading models. Model selection (XGBoost vs. neural net vs. logistic regression) matters far less than the quality and diversity of input features. A simple model on great features will outperform a complex model on raw prices every time.
This skill covers constructing, validating, and selecting features from market data for use in classification (signal-classification) and regression models targeting crypto/Solana token trading.
Raw OHLCV data is non-stationary, noisy, and high-dimensional. Models trained directly on price series will overfit. Feature engineering transforms raw data into stationary, informative signals that capture distinct aspects of market behavior:
Derived purely from OHLCV price columns. These capture trend, momentum, and volatility from the price series itself.
| Feature | Formula | Lookback |
|---|---|---|
log_return | ln(close_t / close_{t-1}) | 1 bar |
abs_return | abs(log_return) | 1 bar |
return_volatility | std(log_return, N) | 20 bars |
momentum_N | close_t / close_{t-N} - 1 | 5, 10, 20 |
acceleration | momentum_5 - momentum_5[5] | 10 bars |
high_low_range | (high - low) / close | 1 bar |
close_position | (close - low) / (high - low) | 1 bar |
gap | open_t / close_{t-1} - 1 | 1 bar |
rolling_skew | skew(log_return, N) | 20 bars |
rolling_kurtosis | kurtosis(log_return, N) | 20 bars |
Volume confirms or contradicts price movements. Divergences between price and volume are among the most reliable signals in short-term trading.
| Feature | Formula | Lookback |
|---|---|---|
volume_ratio | volume_t / mean(volume, N) | 20 bars |
volume_ma_ratio | sma(volume, 5) / sma(volume, 20) | 20 bars |
obv_slope | slope(OBV, N) | 10 bars |
vwap_deviation | (close - VWAP) / VWAP | intraday |
volume_acceleration | volume_ratio_t - volume_ratio_{t-1} | 21 bars |
buy_volume_ratio | buy_volume / total_volume | 1 bar |
dollar_volume | close * volume | 1 bar |
volume_cv | std(volume, N) / mean(volume, N) | 20 bars |
Standard technical indicators computed via pandas-ta. Use the pandas-ta skill
for full parameter documentation.
| Feature | Source | Lookback |
|---|---|---|
rsi | RSI(14) | 14 bars |
macd_histogram | MACD(12,26,9) histogram | 33 bars |
bb_position | (close - BB_lower) / (BB_upper - BB_lower) | 20 bars |
bb_width | (BB_upper - BB_lower) / BB_mid | 20 bars |
atr_ratio | ATR(14) / close | 14 bars |
adx | ADX(14) | 14 bars |
stoch_k | Stochastic %K(14,3) | 14 bars |
cci | CCI(20) | 20 bars |
mfi | MFI(14) | 14 bars |
supertrend_direction | Supertrend direction (+1/-1) | 10 bars |
Derived from trade-level data (individual swaps/transactions). Require on-chain or DEX API data.
| Feature | Description |
|---|---|
trade_count_ratio | Trades this bar / avg trades per bar |
avg_trade_size | Mean trade size in USD |
large_trade_pct | % of volume from trades > $10k |
unique_traders | Count of distinct wallet addresses |
buy_count_ratio | Buy trades / total trades |
trade_size_entropy | Shannon entropy of trade size distribution |
Derived from blockchain state changes. Require Helius or Solana RPC data.
| Feature | Description |
|---|---|
holder_count_change | Change in unique holders over N periods |
whale_net_flow | Net tokens moved by top-10 holders |
token_velocity | Transfer volume / circulating supply |
liquidity_change | Change in DEX liquidity pool TVL |
Capture relationships between the target token and broader market.
| Feature | Description |
|---|---|
sol_correlation | Rolling correlation with SOL price |
btc_beta | Rolling beta to BTC returns |
sector_momentum | Average return of tokens in same sector |
Cyclical encoding of calendar time. Use sin/cos encoding to preserve cyclical continuity (hour 23 is close to hour 0).
import numpy as np
hour_sin = np.sin(2 * np.pi * hour / 24)
hour_cos = np.cos(2 * np.pi * hour / 24)
day_of_week = np.sin(2 * np.pi * day / 7)
Non-stationary features will cause your model to fail on new data. A feature is stationary if its statistical properties (mean, variance) don't change over time.
Use the Augmented Dickey-Fuller (ADF) test:
from scipy.stats import adfuller
result = adfuller(feature_series.dropna())
p_value = result[1]
is_stationary = p_value < 0.05
| Non-Stationary | Stationary Transform |
|---|---|
| Price | Log return |
| Volume | Volume ratio (vol / avg vol) |
| OBV | OBV slope (regression coefficient) |
| Holder count | Holder count change |
| RSI | Already stationary (bounded 0-100) |
| Dollar volume | Dollar volume / rolling mean |
Rule: If a feature trends upward or downward over time, it is non-stationary. Transform it into a ratio, difference, or rate of change.
After computing features, normalize them so that all features have comparable scales. This is critical for distance-based models (KNN, SVM) and helpful for tree models.
| Method | Formula | When to Use |
|---|---|---|
| Z-score | (x - mean) / std | Gaussian-like distributions |
| Min-max | (x - min) / (max - min) | Bounded features (RSI, BB position) |
| Rank | rank(x) / len(x) | Heavy-tailed distributions |
Critical: Use rolling statistics for normalization. Never use full-sample mean/std — that introduces lookahead bias.
# CORRECT: rolling z-score
z = (feature - feature.rolling(60).mean()) / feature.rolling(60).std()
# WRONG: full-sample z-score (lookahead bias!)
z = (feature - feature.mean()) / feature.std()
The most dangerous bug in trading ML is lookahead bias — using future information to compute features or targets. Follow these rules absolutely:
.mean() or .std() on the full
series. Always use .rolling(N).mean().close.shift(-N) / close - 1 (future return), not close / close.shift(N) - 1
(past return used as target).t is paired with target row t (where target already
contains the forward shift).train = data[:split_idx], test = data[split_idx:].After computing many features, select the most predictive and least redundant:
from sklearn.feature_selection import VarianceThreshold
selector = VarianceThreshold(threshold=0.01)
X_filtered = selector.fit_transform(X)
Remove features with > 0.9 correlation to another feature (keep the one with higher target correlation):
corr_matrix = X.corr().abs()
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
to_drop = [col for col in upper.columns if any(upper[col] > 0.9)]
Train a random forest and rank by importance:
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
importances = pd.Series(rf.feature_importances_, index=X.columns).sort_values(ascending=False)
Non-linear alternative to correlation:
from sklearn.feature_selection import mutual_info_classif
mi = mutual_info_classif(X_train, y_train, random_state=42)
mi_scores = pd.Series(mi, index=X.columns).sort_values(ascending=False)
Labels (targets) define what the model learns to predict.
forward_return = close.shift(-N) / close - 1
label = (forward_return > threshold).astype(int) # 1 = up, 0 = not up
Typical thresholds: 1% for 1h bars, 3% for 4h bars, 5% for daily bars.
label = pd.cut(forward_return,
bins=[-np.inf, -threshold, threshold, np.inf],
labels=[0, 1, 2]) # 0=down, 1=flat, 2=up
target = forward_return # Predict exact return magnitude
Binary classification is recommended for initial models — it's simpler and more robust to noise.
pandas-ta: Compute technical indicators that become featuresbirdeye-api: Fetch OHLCV and trade data for feature computationhelius-api: Fetch on-chain data for holder/whale featuressignal-classification: Use engineered features as model inputsregime-detection: Regime labels as features or for regime-conditional modelsohlcv-processing: Clean and resample raw data before feature computationreferences/feature_catalog.md — Complete catalog of ~40 features with formulas,
lookbacks, stationarity status, and interpretation notesreferences/pitfalls.md — Common mistakes in trading feature engineering:
lookahead bias, overfitting, survivorship bias, data snooping, non-stationarityscripts/build_features.py — Compute 25+ features from OHLCV data with
stationarity testing and quality reporting. Supports demo mode with synthetic data
or live data via Birdeye API.scripts/feature_importance.py — Rank features by predictive power using
tree-based importance and permutation importance. Identifies redundant features
via correlation analysis.name: feature-engineering description: Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features
---
name: feature-engineering
description: Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features
---
# Feature Engineering for Trading ML
Feature engineering is the single highest-leverage activity in building ML trading
models. Model selection (XGBoost vs. neural net vs. logistic regression) matters far
less than the quality and diversity of input features. A simple model on great
features will outperform a complex model on raw prices every time.
This skill covers constructing, validating, and selecting features from market data
for use in classification (signal-classification) and regression models targeting
crypto/Solana token trading.
## Why Features Beat Models
Raw OHLCV data is non-stationary, noisy, and high-dimensional. Models trained
directly on price series will overfit. Feature engineering transforms raw data into
stationary, informative signals that capture distinct aspects of market behavior:
- **Compression**: Reduce thousands of price bars to dozens of descriptive statistics
- **Stationarity**: Convert non-stationary prices into stationary returns and ratios
- **Domain knowledge**: Encode trader intuition (support/resistance, volume climax)
as computable quantities
- **Regime awareness**: Features that behave differently in trending vs. ranging
markets help models adapt
## Feature Categories
### 1. Price Features
Derived purely from OHLCV price columns. These capture trend, momentum, and
volatility from the price series itself.
| Feature | Formula | Lookback |
|---------|---------|----------|
| `log_return` | `ln(close_t / close_{t-1})` | 1 bar |
| `abs_return` | `abs(log_return)` | 1 bar |
| `return_volatility` | `std(log_return, N)` | 20 bars |
| `momentum_N` | `close_t / close_{t-N} - 1` | 5, 10, 20 |
| `acceleration` | `momentum_5 - momentum_5[5]` | 10 bars |
| `high_low_range` | `(high - low) / close` | 1 bar |
| `close_position` | `(close - low) / (high - low)` | 1 bar |
| `gap` | `open_t / close_{t-1} - 1` | 1 bar |
| `rolling_skew` | `skew(log_return, N)` | 20 bars |
| `rolling_kurtosis` | `kurtosis(log_return, N)` | 20 bars |
### 2. Volume Features
Volume confirms or contradicts price movements. Divergences between price and
volume are among the most reliable signals in short-term trading.
| Feature | Formula | Lookback |
|---------|---------|----------|
| `volume_ratio` | `volume_t / mean(volume, N)` | 20 bars |
| `volume_ma_ratio` | `sma(volume, 5) / sma(volume, 20)` | 20 bars |
| `obv_slope` | `slope(OBV, N)` | 10 bars |
| `vwap_deviation` | `(close - VWAP) / VWAP` | intraday |
| `volume_acceleration` | `volume_ratio_t - volume_ratio_{t-1}` | 21 bars |
| `buy_volume_ratio` | `buy_volume / total_volume` | 1 bar |
| `dollar_volume` | `close * volume` | 1 bar |
| `volume_cv` | `std(volume, N) / mean(volume, N)` | 20 bars |
### 3. Technical Features
Standard technical indicators computed via `pandas-ta`. Use the `pandas-ta` skill
for full parameter documentation.
| Feature | Source | Lookback |
|---------|--------|----------|
| `rsi` | RSI(14) | 14 bars |
| `macd_histogram` | MACD(12,26,9) histogram | 33 bars |
| `bb_position` | `(close - BB_lower) / (BB_upper - BB_lower)` | 20 bars |
| `bb_width` | `(BB_upper - BB_lower) / BB_mid` | 20 bars |
| `atr_ratio` | `ATR(14) / close` | 14 bars |
| `adx` | ADX(14) | 14 bars |
| `stoch_k` | Stochastic %K(14,3) | 14 bars |
| `cci` | CCI(20) | 20 bars |
| `mfi` | MFI(14) | 14 bars |
| `supertrend_direction` | Supertrend direction (+1/-1) | 10 bars |
### 4. Microstructure Features
Derived from trade-level data (individual swaps/transactions). Require on-chain
or DEX API data.
| Feature | Description |
|---------|-------------|
| `trade_count_ratio` | Trades this bar / avg trades per bar |
| `avg_trade_size` | Mean trade size in USD |
| `large_trade_pct` | % of volume from trades > $10k |
| `unique_traders` | Count of distinct wallet addresses |
| `buy_count_ratio` | Buy trades / total trades |
| `trade_size_entropy` | Shannon entropy of trade size distribution |
### 5. On-Chain Features
Derived from blockchain state changes. Require Helius or Solana RPC data.
| Feature | Description |
|---------|-------------|
| `holder_count_change` | Change in unique holders over N periods |
| `whale_net_flow` | Net tokens moved by top-10 holders |
| `token_velocity` | Transfer volume / circulating supply |
| `liquidity_change` | Change in DEX liquidity pool TVL |
### 6. Cross-Asset Features
Capture relationships between the target token and broader market.
| Feature | Description |
|---------|-------------|
| `sol_correlation` | Rolling correlation with SOL price |
| `btc_beta` | Rolling beta to BTC returns |
| `sector_momentum` | Average return of tokens in same sector |
### 7. Time Features
Cyclical encoding of calendar time. Use sin/cos encoding to preserve cyclical
continuity (hour 23 is close to hour 0).
```python
import numpy as np
hour_sin = np.sin(2 * np.pi * hour / 24)
hour_cos = np.cos(2 * np.pi * hour / 24)
day_of_week = np.sin(2 * np.pi * day / 7)
```
## Stationarity
**Non-stationary features will cause your model to fail on new data.** A feature
is stationary if its statistical properties (mean, variance) don't change over time.
### Testing for Stationarity
Use the Augmented Dickey-Fuller (ADF) test:
```python
from scipy.stats import adfuller
result = adfuller(feature_series.dropna())
p_value = result[1]
is_stationary = p_value < 0.05
```
### Making Features Stationary
| Non-Stationary | Stationary Transform |
|----------------|---------------------|
| Price | Log return |
| Volume | Volume ratio (vol / avg vol) |
| OBV | OBV slope (regression coefficient) |
| Holder count | Holder count change |
| RSI | Already stationary (bounded 0-100) |
| Dollar volume | Dollar volume / rolling mean |
**Rule**: If a feature trends upward or downward over time, it is non-stationary.
Transform it into a ratio, difference, or rate of change.
## Normalization
After computing features, normalize them so that all features have comparable
scales. This is critical for distance-based models (KNN, SVM) and helpful for
tree models.
| Method | Formula | When to Use |
|--------|---------|-------------|
| Z-score | `(x - mean) / std` | Gaussian-like distributions |
| Min-max | `(x - min) / (max - min)` | Bounded features (RSI, BB position) |
| Rank | `rank(x) / len(x)` | Heavy-tailed distributions |
**Critical**: Use **rolling** statistics for normalization. Never use full-sample
mean/std — that introduces lookahead bias.
```python
# CORRECT: rolling z-score
z = (feature - feature.rolling(60).mean()) / feature.rolling(60).std()
# WRONG: full-sample z-score (lookahead bias!)
z = (feature - feature.mean()) / feature.std()
```
## No-Lookahead Guarantee
The most dangerous bug in trading ML is lookahead bias — using future information
to compute features or targets. Follow these rules absolutely:
1. **Rolling calculations only**: Never use `.mean()` or `.std()` on the full
series. Always use `.rolling(N).mean()`.
2. **Shift targets forward, not features backward**: The target is
`close.shift(-N) / close - 1` (future return), not `close / close.shift(N) - 1`
(past return used as target).
3. **No future index alignment**: When joining feature and target DataFrames,
verify that feature row `t` is paired with target row `t` (where target already
contains the forward shift).
4. **Train/test split by time**: Never random split. Always
`train = data[:split_idx]`, `test = data[split_idx:]`.
## Feature Selection
After computing many features, select the most predictive and least redundant:
### Step 1: Remove Low-Variance Features
```python
from sklearn.feature_selection import VarianceThreshold
selector = VarianceThreshold(threshold=0.01)
X_filtered = selector.fit_transform(X)
```
### Step 2: Correlation Filter
Remove features with > 0.9 correlation to another feature (keep the one with
higher target correlation):
```python
corr_matrix = X.corr().abs()
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
to_drop = [col for col in upper.columns if any(upper[col] > 0.9)]
```
### Step 3: Feature Importance
Train a random forest and rank by importance:
```python
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)
importances = pd.Series(rf.feature_importances_, index=X.columns).sort_values(ascending=False)
```
### Step 4: Mutual Information
Non-linear alternative to correlation:
```python
from sklearn.feature_selection import mutual_info_classif
mi = mutual_info_classif(X_train, y_train, random_state=42)
mi_scores = pd.Series(mi, index=X.columns).sort_values(ascending=False)
```
## Label Creation
Labels (targets) define what the model learns to predict.
### Binary Classification
```python
forward_return = close.shift(-N) / close - 1
label = (forward_return > threshold).astype(int) # 1 = up, 0 = not up
```
Typical thresholds: 1% for 1h bars, 3% for 4h bars, 5% for daily bars.
### Multi-Class Classification
```python
label = pd.cut(forward_return,
bins=[-np.inf, -threshold, threshold, np.inf],
labels=[0, 1, 2]) # 0=down, 1=flat, 2=up
```
### Regression
```python
target = forward_return # Predict exact return magnitude
```
Binary classification is recommended for initial models — it's simpler and
more robust to noise.
## Integration with Other Skills
- **`pandas-ta`**: Compute technical indicators that become features
- **`birdeye-api`**: Fetch OHLCV and trade data for feature computation
- **`helius-api`**: Fetch on-chain data for holder/whale features
- **`signal-classification`**: Use engineered features as model inputs
- **`regime-detection`**: Regime labels as features or for regime-conditional models
- **`ohlcv-processing`**: Clean and resample raw data before feature computation
## Files
### References
- `references/feature_catalog.md` — Complete catalog of ~40 features with formulas,
lookbacks, stationarity status, and interpretation notes
- `references/pitfalls.md` — Common mistakes in trading feature engineering:
lookahead bias, overfitting, survivorship bias, data snooping, non-stationarity
### Scripts
- `scripts/build_features.py` — Compute 25+ features from OHLCV data with
stationarity testing and quality reporting. Supports demo mode with synthetic data
or live data via Birdeye API.
- `scripts/feature_importance.py` — Rank features by predictive power using
tree-based importance and permutation importance. Identifies redundant features
via correlation analysis.
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Review before install: Avoid automatic install
License: MIT
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"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": [
"data-analysis",
"agent-skill"
],
"known_risks": [
"The script feature_importance.py imports adfuller from scipy.stats, but adfuller is actually in statsmodels.tsa.stattools. This will cause an ImportError.",
"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, network or browser access",
"Permission surface: secrets or environment access, network or browser access"
]
},
"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": 77,
"risk_level": "risky",
"risk_label": "Risky",
"warnings": [
"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 script feature_importance.py imports adfuller from scipy.stats, but adfuller is actually in statsmodels.tsa.stattools. This will cause an ImportError.",
"The skill references a 'pandas-ta' skill but does not include it or specify installation instructions, which may lead to missing dependencies.",
"The SKILL.md excerpt is truncated, but the provided content is thorough; however, the full file may contain additional details not reviewed.",
"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": "Data, BI, and analytics",
"scenario": "Research agents",
"maintenance": "10d since push",
"risk": "Risky"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"The script feature_importance.py imports adfuller from scipy.stats, but adfuller is actually in statsmodels.tsa.stattools. This will cause an ImportError.",
"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Secrets or environment access",
"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"
],
"agent_contract": {
"task_input": "Use feature-engineering 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: 67/100 Manual review",
"Audit: 77/100 Risky",
"Safety: 49/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "agiprolabs-feature-engineering (feature-engineering)",
"install_command": "npx skills add agiprolabs/claude-trading-skills --skill feature-engineering",
"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-feature-engineering",
"task": "Use feature-engineering 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-feature-engineering",
"api": "https://www.openagentskill.com/api/agent/skills/agiprolabs-feature-engineering",
"audit": "https://www.openagentskill.com/skills/agiprolabs-feature-engineering/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=agiprolabs-feature-engineering&task=Use%20feature-engineering%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20feature-engineering%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20feature-engineering%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/agiprolabs-feature-engineering/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/agiprolabs-feature-engineering"
}
}Listing source
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Audit
77/100
Risky
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.