arbazkhan971
Registry indexed
chart
Data visualization. chart, graph, dashboard, visualize data, plot, analytics, D3.js, Chart.js, Recharts, Plotly.
Registry indexed
Data visualization. chart, graph, dashboard, visualize data, plot, analytics, D3.js, Chart.js, Recharts, Plotly.
Data visualization. chart, graph, dashboard, visualize data, plot, analytics, D3.js, Chart.js, Recharts, Plotly.
Source documentation, not instructions for this website. Review permissions before running any commands.
/godmode:chart/godmode:plan identifies data visualization tasks/godmode:review flags visualization accessibility or usability issuesUnderstand the data and what the visualization needs to communicate:
VISUALIZATION DISCOVERY:
Project: <name and purpose>
Data source: <API endpoint | database query | static JSON | CSV | real-time stream>
Data shape: <rows x columns, field names, types>
Audience: <executives | engineers | end-users | public>
Goal: <compare | trend | distribute | correlate | compose | flow | geospatial>
Interactivity: <static | hover tooltips | click-to-filter | drill-down | real-time>
Environment: <React | Vue | Angular | vanilla JS | server-side PDF | Jupyter>
Existing library: <D3.js | Chart.js | Recharts | Plotly | Nivo | Victory | none>
Constraints: <bundle size limit | IE support | print-friendly | offline | color-blind safe>
If the user hasn't specified, ask: "What story should this visualization tell? Who is the audience?"
Select the optimal chart type based on the data and communication goal:
CHART TYPE SELECTION:
| Goal | Recommended Chart Types |
|--|--|
| Compare values | Bar (vertical/horizontal), Grouped bar, Lollipop |
| Show trends | Line, Area, Sparkline, Step |
| Show distribution | Histogram, Box plot, Violin, Density |
| Show correlation | Scatter, Bubble, Heatmap (correlation matrix) |
| Show composition | Stacked bar, Treemap, Sunburst, Waffle |
| Show flow/process | Sankey, Alluvial, Chord diagram |
| Show hierarchy | Treemap, Sunburst, Dendrogram, Circle packing |
| Show geographic | Choropleth, Bubble map, Hex bin map |
| Show part-to-whole | Donut, Stacked area, Marimekko |
...
Rules:
Choose the right visualization library for the project:
LIBRARY SELECTION:
| Library | Best For | Bundle Size | Learning Curve |
|--|--|--|--|
| D3.js | Custom, complex, | ~90KB | Steep — full control |
| | unique visualizations | | over every pixel |
| Chart.js | Standard charts, | ~60KB | Low — declarative |
| | quick setup, canvas | | config-based API |
| Recharts | React dashboards, | ~120KB | Low — React-native |
| | composable charts | | component API |
| Plotly | Scientific/data | ~1MB | Medium — rich |
| | analysis, 3D plots | | interactive charts |
Prepare data for the selected chart type:
DATA TRANSFORMATION:
Source format: <raw data shape — e.g., array of objects, CSV rows, nested JSON>
Target format: <what the chart library expects>
Transformations needed:
1. <transformation — e.g., group by category, aggregate sum>
2. <transformation — e.g., pivot rows to columns>
3. <transformation — e.g., normalize to percentages>
4. <transformation — e.g., sort descending by value>
5. <transformation — e.g., compute rolling average>
Missing data strategy: <omit | zero-fill | interpolate | show gap>
...
Generate the transformation code:
// Data transformation pipeline
function transformData(raw: RawData[]): ChartData {
return raw
.filter(/* remove invalid entries */)
.map(/* reshape to chart format */)
.sort(/* order for readability */)
Build the chart with full configuration:
CHART CONFIGURATION:
| Property | Value |
|--|--|
| Type | <bar | line | scatter | heatmap | ...> |
| Width | <responsive | fixed px> |
| Height | <responsive | fixed px> |
| Aspect ratio | <16:9 | 4:3 | 1:1 | custom> |
| Margins | top=<N> right=<N> bottom=<N> left=<N> |
| Colors | <palette name or hex values> |
| Font family | <system | project font> |
| Animation | <none | enter | update | transition> |
| Legend | <position: top | right | bottom | none> |
...
Use the selected library's standard patterns:
ResponsiveContainer wrapper, declarative component compositionMobile (<480px): stack legend below, reduce ticks, enlarge touch targets. Tablet (480-1024px): side legend, full interactivity. Desktop (>1024px): full layout, annotations, brush/zoom.
Design accessible visualizations that work for everyone:
ACCESSIBILITY CHECKLIST:
| Check | Status |
|--|--|
| Color contrast ratio >= 3:1 against background | PASS | FAIL |
| Colorblind-safe palette (no red/green only) | PASS | FAIL |
| Patterns/textures as secondary differentiator | PASS | FAIL |
| aria-label on chart container (SVG role="img") | PASS | FAIL |
| Data table alternative available | PASS | FAIL |
| Keyboard navigable (focus on data points) | PASS | FAIL |
| Screen reader descriptions for trends | PASS | FAIL |
| Tooltip accessible via keyboard (not hover-only) | PASS | FAIL |
| Text labels minimum 12px font size | PASS | FAIL |
...
When building multi-chart dashboards, apply layout principles:
DASHBOARD DESIGN:
Layout: <grid columns — e.g., 12-column grid>
Sections:
1. <KPI row — number cards with sparklines>
2. <Primary chart — largest, most important visualization>
3. <Supporting charts — 2-3 smaller charts providing context>
4. <Detail table — filterable data table for drill-down>
DASHBOARD PRINCIPLES:
1. Most important metric is top-left (F-pattern reading)
2. KPI cards first — give the executive summary before details
3. Max 7 ± 2 charts per dashboard (cognitive load limit)
...
Optimize chart rendering for large datasets:
PERFORMANCE STRATEGIES:
| < 1,000 points | Render all — no optimization needed |
|--|--|
| 1K - 10K points | Canvas rendering (not SVG), debounce tooltips |
| 10K - 100K points | Data aggregation, LTTB downsampling, WebGL |
| > 100K points | Server-side aggregation, WebGL (deck.gl) |
Key techniques: Canvas over SVG for > 1K points, LTTB downsampling for time series,
IntersectionObserver for lazy-loading, useMemo for data transforms, Web Workers for heavy processing.
Validate the visualization and produce deliverables:
VISUALIZATION VALIDATION:
| Check | Status |
|--|--|
| Chart type matches data and communication goal | PASS | FAIL |
| Data transformations produce correct output | PASS | FAIL |
| Responsive at mobile, tablet, desktop breakpoints | PASS | FAIL |
| Accessibility checklist complete (all items pass) | PASS | FAIL |
| Color palette is colorblind-safe | PASS | FAIL |
| Performance acceptable at expected data volume | PASS | FAIL |
| Tooltips show correct formatted values | PASS | FAIL |
| Axis labels and titles are clear and formatted | PASS | FAIL |
| Legend is present and correctly maps to data series | PASS | FAIL |
...
Produce deliverables:
VISUALIZATION COMPLETE:
Artifacts:
- Chart component: src/components/charts/<ChartName>.tsx
- Data transformer: src/utils/chart-data/<transformer>.ts
- Dashboard layout: src/pages/<dashboard>.tsx (if dashboard)
- Storybook story: src/components/charts/<ChartName>.stories.tsx
- Tests: src/components/charts/__tests__/<ChartName>.test.tsx
Validation: <PASS | NEEDS REVISION>
Chart type: <type>
Library: <library>
...
Commit: "chart: <component> — <chart type>, <library>, <N> data series, responsive + accessible"
# Test chart rendering and accessibility
npm run test:charts
npx storybook build --ci
npx chromatic --exit-zero-on-changes
| Flag | Description |
|---|---|
| (none) | Full chart design and implementation workflow |
--type <chart> | Force chart type: bar, line, scatter, heatmap, treemap, sankey, pie, area |
--lib <library> | Force library: d3, chartjs, recharts, plotly, nivo, victory |
Never ask to continue. Loop autonomously until all charts render within targets and pass accessibility checks.
timestamp chart_type library data_points responsive a11y_score status
On activation, automatically detect project context without asking:
AUTO-DETECT:
1. Framework:
ls package.json 2>/dev/null && grep -o '"react"\|"vue"\|"angular"\|"svelte"' package.json
# Determines component style and library compatibility
2. Existing chart libraries:
grep -r "recharts\|chart.js\|d3\|plotly\|nivo\|victory" package.json 2>/dev/null
# Prefer existing library over introducing a new one
3. Design system:
ls src/theme* src/styles/tokens* tailwind.config* 2>/dev/null
# Extract color palette, font family, spacing tokens
...
After each chart skill invocation, emit a structured report:
CHART BUILD REPORT:
| Charts created | <N> |
|--|--|
| Charts updated | <N> |
| Library used | <library name> |
| Data points | <N> total across all charts |
| Responsive | YES / NO |
| A11y (data table) | YES / NO |
| Colorblind-safe | YES / NO |
| Bundle impact | +<N> KB (gzipped) |
| Render time | <N> ms (largest chart) |
| Verdict | PASS | NEEDS REVISION |
KEEP if: improvement verified. DISCARD if: regression or no change. Revert discards immediately.
Stop when: target reached, budget exhausted, or >5 consecutive discards.
name: chart description: Data visualization. chart, graph, dashboard, visualize data, plot, analytics, D3.js, Chart.js, Recharts, Plotly.
---
name: chart
description: Data visualization. chart, graph, dashboard, visualize data, plot, analytics, D3.js, Chart.js, Recharts, Plotly.
---
# Chart — Data Visualization
## Activate When
- User invokes `/godmode:chart`
- User says "create a chart", "visualize this data", "make a graph"
- User says "build a dashboard", "display metrics", "plot this"
- When building reporting pages or analytics dashboards
- When `/godmode:plan` identifies data visualization tasks
- When `/godmode:review` flags visualization accessibility or usability issues
## Workflow
### Step 1: Data & Intent Discovery
Understand the data and what the visualization needs to communicate:
```
VISUALIZATION DISCOVERY:
Project: <name and purpose>
Data source: <API endpoint | database query | static JSON | CSV | real-time stream>
Data shape: <rows x columns, field names, types>
Audience: <executives | engineers | end-users | public>
Goal: <compare | trend | distribute | correlate | compose | flow | geospatial>
Interactivity: <static | hover tooltips | click-to-filter | drill-down | real-time>
Environment: <React | Vue | Angular | vanilla JS | server-side PDF | Jupyter>
Existing library: <D3.js | Chart.js | Recharts | Plotly | Nivo | Victory | none>
Constraints: <bundle size limit | IE support | print-friendly | offline | color-blind safe>
```
If the user hasn't specified, ask: "What story should this visualization tell? Who is the audience?"
### Step 2: Chart Type Selection
Select the optimal chart type based on the data and communication goal:
```
CHART TYPE SELECTION:
| Goal | Recommended Chart Types |
|--|--|
| Compare values | Bar (vertical/horizontal), Grouped bar, Lollipop |
| Show trends | Line, Area, Sparkline, Step |
| Show distribution | Histogram, Box plot, Violin, Density |
| Show correlation | Scatter, Bubble, Heatmap (correlation matrix) |
| Show composition | Stacked bar, Treemap, Sunburst, Waffle |
| Show flow/process | Sankey, Alluvial, Chord diagram |
| Show hierarchy | Treemap, Sunburst, Dendrogram, Circle packing |
| Show geographic | Choropleth, Bubble map, Hex bin map |
| Show part-to-whole | Donut, Stacked area, Marimekko |
...
```
Rules:
- Never use pie charts for more than 5 categories — use bar charts instead
- Never use 3D charts — they distort perception and reduce accuracy
- Use line charts only for continuous data (time series) — not categorical
- Prefer horizontal bar charts when labels are long
- Use small multiples over complex multi-series charts when series exceed 5
### Step 3: Library Selection & Setup
Choose the right visualization library for the project:
```
LIBRARY SELECTION:
| Library | Best For | Bundle Size | Learning Curve |
|--|--|--|--|
| D3.js | Custom, complex, | ~90KB | Steep — full control |
| | unique visualizations | | over every pixel |
| Chart.js | Standard charts, | ~60KB | Low — declarative |
| | quick setup, canvas | | config-based API |
| Recharts | React dashboards, | ~120KB | Low — React-native |
| | composable charts | | component API |
| Plotly | Scientific/data | ~1MB | Medium — rich |
| | analysis, 3D plots | | interactive charts |
```
### Step 4: Data Transformation
Prepare data for the selected chart type:
```
DATA TRANSFORMATION:
Source format: <raw data shape — e.g., array of objects, CSV rows, nested JSON>
Target format: <what the chart library expects>
Transformations needed:
1. <transformation — e.g., group by category, aggregate sum>
2. <transformation — e.g., pivot rows to columns>
3. <transformation — e.g., normalize to percentages>
4. <transformation — e.g., sort descending by value>
5. <transformation — e.g., compute rolling average>
Missing data strategy: <omit | zero-fill | interpolate | show gap>
...
```
Generate the transformation code:
```typescript
// Data transformation pipeline
function transformData(raw: RawData[]): ChartData {
return raw
.filter(/* remove invalid entries */)
.map(/* reshape to chart format */)
.sort(/* order for readability */)
```
### Step 5: Chart Implementation
Build the chart with full configuration:
```
CHART CONFIGURATION:
| Property | Value |
|--|--|
| Type | <bar | line | scatter | heatmap | ...> |
| Width | <responsive | fixed px> |
| Height | <responsive | fixed px> |
| Aspect ratio | <16:9 | 4:3 | 1:1 | custom> |
| Margins | top=<N> right=<N> bottom=<N> left=<N> |
| Colors | <palette name or hex values> |
| Font family | <system | project font> |
| Animation | <none | enter | update | transition> |
| Legend | <position: top | right | bottom | none> |
...
```
Use the selected library's standard patterns:
- **D3.js**: SVG with margin convention, scales, axes, data joins
- **Recharts**: `ResponsiveContainer` wrapper, declarative component composition
- **Chart.js**: Canvas-based config object with datasets array
- **Plotly**: Trace objects with layout configuration
### Step 6: Responsive Design
Mobile (<480px): stack legend below, reduce ticks, enlarge touch targets. Tablet (480-1024px): side legend,
full interactivity. Desktop (>1024px): full layout, annotations, brush/zoom.
### Step 7: Color & Accessibility
Design accessible visualizations that work for everyone:
```
ACCESSIBILITY CHECKLIST:
| Check | Status |
|--|--|
| Color contrast ratio >= 3:1 against background | PASS | FAIL |
| Colorblind-safe palette (no red/green only) | PASS | FAIL |
| Patterns/textures as secondary differentiator | PASS | FAIL |
| aria-label on chart container (SVG role="img") | PASS | FAIL |
| Data table alternative available | PASS | FAIL |
| Keyboard navigable (focus on data points) | PASS | FAIL |
| Screen reader descriptions for trends | PASS | FAIL |
| Tooltip accessible via keyboard (not hover-only) | PASS | FAIL |
| Text labels minimum 12px font size | PASS | FAIL |
...
```
### Step 8: Dashboard Composition
When building multi-chart dashboards, apply layout principles:
```
DASHBOARD DESIGN:
Layout: <grid columns — e.g., 12-column grid>
Sections:
1. <KPI row — number cards with sparklines>
2. <Primary chart — largest, most important visualization>
3. <Supporting charts — 2-3 smaller charts providing context>
4. <Detail table — filterable data table for drill-down>
DASHBOARD PRINCIPLES:
1. Most important metric is top-left (F-pattern reading)
2. KPI cards first — give the executive summary before details
3. Max 7 ± 2 charts per dashboard (cognitive load limit)
...
```
### Step 9: Performance Optimization
Optimize chart rendering for large datasets:
```
PERFORMANCE STRATEGIES:
| < 1,000 points | Render all — no optimization needed |
|--|--|
| 1K - 10K points | Canvas rendering (not SVG), debounce tooltips |
| 10K - 100K points | Data aggregation, LTTB downsampling, WebGL |
| > 100K points | Server-side aggregation, WebGL (deck.gl) |
Key techniques: Canvas over SVG for > 1K points, LTTB downsampling for time series,
IntersectionObserver for lazy-loading, useMemo for data transforms, Web Workers for heavy processing.
```
### Step 10: Validation & Delivery
Validate the visualization and produce deliverables:
```
VISUALIZATION VALIDATION:
| Check | Status |
|--|--|
| Chart type matches data and communication goal | PASS | FAIL |
| Data transformations produce correct output | PASS | FAIL |
| Responsive at mobile, tablet, desktop breakpoints | PASS | FAIL |
| Accessibility checklist complete (all items pass) | PASS | FAIL |
| Color palette is colorblind-safe | PASS | FAIL |
| Performance acceptable at expected data volume | PASS | FAIL |
| Tooltips show correct formatted values | PASS | FAIL |
| Axis labels and titles are clear and formatted | PASS | FAIL |
| Legend is present and correctly maps to data series | PASS | FAIL |
...
```
Produce deliverables:
```
VISUALIZATION COMPLETE:
Artifacts:
- Chart component: src/components/charts/<ChartName>.tsx
- Data transformer: src/utils/chart-data/<transformer>.ts
- Dashboard layout: src/pages/<dashboard>.tsx (if dashboard)
- Storybook story: src/components/charts/<ChartName>.stories.tsx
- Tests: src/components/charts/__tests__/<ChartName>.test.tsx
Validation: <PASS | NEEDS REVISION>
Chart type: <type>
Library: <library>
...
```
Commit: `"chart: <component> — <chart type>, <library>, <N> data series, responsive + accessible"`
## Key Behaviors
```bash
# Test chart rendering and accessibility
npm run test:charts
npx storybook build --ci
npx chromatic --exit-zero-on-changes
```
1. **Data story first, chart second.** Communication goal first.
2. **Accessibility not optional.** Data table + colorblind-safe + screen reader.
3. **Responsive by default.** Works at 320px, 768px, 1440px.
4. **Performance scales with data.** Canvas for > 1K points.
5. **Consistent dashboards.** Same colors, typography, interactions.
6. **No misleading visualizations.** Bar charts start at 0.
7. **Color is not the only channel.** Patterns, labels, position too.
On failure: revert with git reset --hard HEAD~1.
## Flags & Options
| Flag | Description |
|--|--|
| (none) | Full chart design and implementation workflow |
| `--type <chart>` | Force chart type: `bar`, `line`, `scatter`, `heatmap`, `treemap`, `sankey`, `pie`, `area` |
| `--lib <library>` | Force library: `d3`, `chartjs`, `recharts`, `plotly`, `nivo`, `victory` |
## HARD RULES
Never ask to continue. Loop autonomously until all charts render within targets and pass accessibility checks.
1. **NEVER use pie charts for more than 5 categories.** No exceptions. Use bar charts instead.
2. **NEVER use 3D charts.** They distort data and add no information.
3. **NEVER ship without a data table alternative** for screen readers.
4. **NEVER start bar chart y-axis above zero** unless explicitly documented with justification.
5. **ALWAYS test at 320px, 768px, and 1440px** before marking responsive as done.
6. **ALWAYS verify colorblind safety** with Chrome DevTools vision deficiency emulation.
7. **git commit BEFORE verify** — commit the chart component, then run visual/a11y tests.
8. **TSV logging** — log every chart creation:
```
timestamp chart_type library data_points responsive a11y_score status
```
## Auto-Detection
On activation, automatically detect project context without asking:
```
AUTO-DETECT:
1. Framework:
ls package.json 2>/dev/null && grep -o '"react"\|"vue"\|"angular"\|"svelte"' package.json
# Determines component style and library compatibility
2. Existing chart libraries:
grep -r "recharts\|chart.js\|d3\|plotly\|nivo\|victory" package.json 2>/dev/null
# Prefer existing library over introducing a new one
3. Design system:
ls src/theme* src/styles/tokens* tailwind.config* 2>/dev/null
# Extract color palette, font family, spacing tokens
...
```
## Output Format
After each chart skill invocation, emit a structured report:
```
CHART BUILD REPORT:
| Charts created | <N> |
|--|--|
| Charts updated | <N> |
| Library used | <library name> |
| Data points | <N> total across all charts |
| Responsive | YES / NO |
| A11y (data table) | YES / NO |
| Colorblind-safe | YES / NO |
| Bundle impact | +<N> KB (gzipped) |
| Render time | <N> ms (largest chart) |
| Verdict | PASS | NEEDS REVISION |
```
## Keep/Discard
KEEP if: improvement verified. DISCARD if: regression or no change. Revert discards immediately.
## Stop Conditions
Stop when: target reached, budget exhausted, or >5 consecutive discards.
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
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
56/100
Promising
Trust
57/100
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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"name": "chart",
"description": "Data visualization. chart, graph, dashboard, visualize data, plot, analytics, D3.js, Chart.js, Recharts, Plotly.",
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"version": "trust-score-v4",
"install_policy": "block",
"evidence": {
"stars": "26 GitHub stars",
"repoActivity": "26 stars, 7 forks",
"lastPushed": "19d since push",
"license": "MIT",
"repository": "https://github.com/arbazkhan971/godmode/tree/master/skills/chart",
"install": "npx skills add arbazkhan971/godmode --skill chart",
"installSafety": "standard package or runtime install path",
"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Usable metadata, review docs",
"agentOutcomes": "No agent outcome data yet"
},
"outcome_evidence": {
"total": 0,
"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": [
"design-creative",
"agent-skill"
],
"known_risks": [
"AI review approval is missing",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Low GitHub adoption signal",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 26 GitHub stars",
"Stars/forks activity: 26 stars, 7 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment 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": 70,
"risk_level": "needs_review",
"risk_label": "Needs review",
"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",
"Low GitHub adoption signal",
"AI review approval is missing",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
]
},
"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": 56,
"label": "Promising"
},
"supply": {
"track": "Data, BI, and analytics",
"scenario": "Data analysis",
"maintenance": "19d since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"Low GitHub adoption signal",
"No OpenAgentSkill engagement data yet",
"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 chart 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: 70/100 Needs review",
"Safety: 26/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "arbazkhan971-chart (chart)",
"install_command": "npx skills add arbazkhan971/godmode --skill chart",
"risk_summary": "Needs review; 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": "arbazkhan971-chart",
"task": "Use chart 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/arbazkhan971-chart",
"api": "https://www.openagentskill.com/api/agent/skills/arbazkhan971-chart",
"audit": "https://www.openagentskill.com/skills/arbazkhan971-chart/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=arbazkhan971-chart&task=Use%20chart%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/arbazkhan971-chart/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/arbazkhan971-chart"
}
}Listing source
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Do not auto-install
Audit
70/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.