Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.
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Plotly interactive visualization. Express and Graph Objects: scatter, line, bar, heatmap, 3D, geographic charts; subplots; styling; export. Use when interactivity (hover/zoom) is needed. For static figures use plotnine; for GIS use geopandas. R equivalent: plotly-r (use when execution language is R).
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Plotly interactive visualization library for Python. Covers Plotly Express and Graph Objects for scatter, line, bar, histogram, box, heatmap, 3D, and geographic charts; subplots and faceting; styling; and HTML/image export. Use when creating interactive visualizations with hover/zoom/pan, building web-based charts, or producing geographic or 3D plots. Prefer over plotnine when interactivity is required; for spatial analysis, projections, or GIS-style mapping, use geopandas.
Quick reference for creating interactive data visualizations with Plotly, featuring both the high-level Plotly Express API and low-level Graph Objects.
Plotly is an interactive visualization library for Python:
| File | Purpose | When to Read |
|---|---|---|
quickstart.md | Installation, imports, px vs go | Starting out |
charts.md | Scatter, line, bar, histogram, box | Creating visualizations |
subplots-facets.md | Multi-panel layouts, faceting | Multiple charts together |
styling.md | Templates, colors, layout | Customizing appearance |
export.md | HTML, images, JSON | Saving and sharing |
gotchas.md | Common errors, best practices | Debugging |
The reference-file routing in this skill applies to advisory and brainstorming turns as much as implementation. Recommending an approach, reviewing a plan, or answering a question that touches a routed topic calls for reading the routed reference file just as much as writing code does — the reference files carry curated caveats and environment-specific constraints that this overview and general knowledge lack.
What kind of chart?
├─ Scatter plot → ./references/charts.md
├─ Line chart → ./references/charts.md
├─ Bar chart → ./references/charts.md
├─ Histogram → ./references/charts.md
├─ Box/Violin plot → ./references/charts.md
├─ Heatmap → ./references/charts.md
├─ 3D/Maps/Financial → ./references/charts.md (Other Chart Types)
└─ Not sure → ./references/quickstart.md
Multiple panels?
├─ Same chart, split by category → ./references/subplots-facets.md (faceting)
├─ Different charts in grid → ./references/subplots-facets.md (make_subplots)
├─ Shared axes → ./references/subplots-facets.md
└─ Secondary y-axis → ./references/subplots-facets.md
What to customize?
├─ Overall theme → ./references/styling.md (templates)
├─ Colors → ./references/styling.md
├─ Titles/labels → ./references/styling.md
├─ Axes → ./references/styling.md
├─ Legend → ./references/styling.md
└─ Hover info → ./references/styling.md
Export format?
├─ Interactive HTML → ./references/export.md
├─ Static image (PNG/SVG/PDF) → ./references/export.md
├─ JSON for API → ./references/export.md
└─ Embed in webpage → ./references/export.md
Common issues?
├─ Figure not showing → ./references/gotchas.md
├─ Image export fails → ./references/gotchas.md
├─ Performance issues → ./references/gotchas.md
├─ px vs go confusion → ./references/gotchas.md
└─ Column/data errors → ./references/gotchas.md
Important: In data research pipelines (see CLAUDE.md), all visualizations are generated through script files in scripts/stage8_analysis/, not interactively. This ensures auditability and reproducibility.
The pattern:
scripts/stage8_analysis/{step}_{plot-name}.pyClosely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules.
See:
agent_reference/WORKFLOW_PHASE4_ANALYSIS.md — Stage 8 (Analysis & Visualization)The examples below show Plotly syntax. In research workflows, wrap them in scripts following the file-first pattern.
import plotly.express as px # High-level API
import plotly.graph_objects as go # Low-level API
from plotly.subplots import make_subplots # For subplots
import plotly.io as pio # For export/config
import plotly.express as px
fig = px.scatter(df, x="col_x", y="col_y", color="category")
fig.show()
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_data, y=y_data, mode="markers"))
fig.update_layout(title="My Plot")
fig.show()
| Function | Chart Type |
|---|---|
px.scatter() | Scatter plot |
px.line() | Line chart |
px.bar() | Bar chart |
px.histogram() | Histogram |
px.box() | Box plot |
px.violin() | Violin plot |
px.imshow() | Heatmap/Image |
px.pie() | Pie chart |
| Trace | Use Case |
|---|---|
go.Scatter | Points, lines, or both |
go.Bar | Bar charts |
go.Histogram | Histograms |
go.Box | Box plots |
go.Heatmap | Heatmaps |
go.Pie | Pie charts |
# Interactive HTML
fig.write_html("plot.html")
# Static image export (PNG/SVG/PDF) is NOT available in DAAF — kaleido is not
# installed due to its heavy Chromium dependency. Use plotnine for static figures.
# For interactive output, use HTML:
# fig.write_image("plot.png") # Would require: pip install kaleido + Chromium
| Topic | Reference File |
|---|---|
| Installation | ./references/quickstart.md |
| px vs go | ./references/quickstart.md |
| Built-in datasets | ./references/quickstart.md |
| Scatter plots | ./references/charts.md |
| Line charts | ./references/charts.md |
| Bar charts | ./references/charts.md |
| Histograms | ./references/charts.md |
| Box plots | ./references/charts.md |
| Other chart types | ./references/charts.md |
| Faceting | ./references/subplots-facets.md |
| make_subplots | ./references/subplots-facets.md |
| Templates/Themes | ./references/styling.md |
| Colors | ./references/styling.md |
| Layout customization | ./references/styling.md |
| Hover customization | ./references/styling.md |
| HTML export | ./references/export.md |
| Image export | ./references/export.md |
| JSON export | ./references/export.md |
| Common errors | ./references/gotchas.md |
| Performance | ./references/gotchas.md |
| Best practices | ./references/gotchas.md |
When this library is used as a primary analytical tool, include in the report's Software & Tools references:
Plotly Technologies Inc. Plotly: Interactive graphing library [Computer software]. https://plotly.com/
Cite when: Plotly is the primary visualization library producing interactive figures included in the report or notebook. Do not cite when: Only used for quick exploratory plots not included in deliverables.
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Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.
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Researcher-facing analytic triangulation ("creative verification") for stress-testing provisional findings and designing pre-specified challenges before an analysis runs. Turns a researcher's concern or focal claim into competing explanations, bounded discriminating tests, audited diagnostic artifacts, and evidence-calibrated interpretations in a hypothesis/evidence ledger — behavioral, output-level verification replacing line-by-line code review. Covers 12 challenge families organized by Shadish-Cook-Campbell validity type, named sensitivity statistics, pre-committed interpretation rules, anti-fishing guardrails, and interactive diagnostic-artifact QA. Use in Ad Hoc Collaboration to verify, challenge, or stress-test a result, or to design discriminating tests before an analysis runs. Not code-change verification (verify skill / code review) and not final adjudication or re-execution (data-verifier, Reproducibility Verification) — prospective, hypothesis-driven challenge design of analytic claims.
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In-situ smoke testing that verifies a LIVE DAAF deployment functions end-to-end in whatever provider route it is configured for. Auto-detects the active install route (anthropic-subscription, openrouter, chatgpt-subscription shim, openai-api shim) and runs tiered probes: Tier 0 free preflight (route/env coherence, hooks, statuslines, shim health); Tier 1 one live round-trip (~cents); Tier 2 a six-probe functional battery (dispatch, coding, web, skill loading, isolation-strip, nested-dispatch deny); Tier D a zero-cost deterministic battery (bats, Pester, lint, safety-hook tests). Use after install, after a rebuild or update, after any provider/config change (new API key, model remap, shim change), for pre-release validation, or when asked "is my install working" or to "smoke test the deployment/config." Requires the DAAF_DEV=1 dev image; a contributor tool, not end-user-facing. NOT DAAFBench (model behavioral-adherence evaluation under benchmarks/) and NOT scripts/smoke_tests/ (R/Python library-package smoke tests).
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Operational framework for the DAAF orchestrator. Defines engagement modes, confirmation protocol, subagent dispatch, context budget, and reference-loading. Loaded exclusively by the orchestrator — not for subagents or user questions.
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Data science methodology and method-selection routing for quantitative research. Covers EDA, data validation, descriptive analysis, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial analysis, network analysis, and visualization design. Contains the canonical method-to-library routing tree, routed by execution language — Python: statsmodels (OLS/GLM/time series), pyfixest (FE/DiD), linearmodels (RE/GMM/SUR), svy (complex surveys), scikit-learn (clustering/prediction ML), geopandas (spatial), igraph (network analysis); R: r-stats (OLS/GLM/time series), fixest (FE/DiD), plm (panel/RE/IV), survey-r (complex surveys), tidymodels (ML), sf-terra (spatial), igraph-r (network analysis). Load the routed tool-specific skill before giving tool-specific advice or writing code — library skills encode environment constraints and curated caveats absent from general knowledge.
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Interpretation guidance for Urban Institute Education Data Portal datasets. The Portal is a curation layer over federal data: lowercase variable names, integer-encoded categoricals, standardized missing codes (-1 missing, -2 not applicable, -3 suppressed). Covers year definitions, grade encoding (grade=-1 is Pre-K, not missing), suppression rates, ODC-By licensing, and cross-source join identifiers. Load before analyzing any Portal data. Routes to source-specific deep-dive skills for individual datasets.
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