Retrieve knowledge base memories created in a date range. Use for: memory report, show memories, new memories, memories for March, what memories were created, knowledge base additions, KB entries.
npx skills add https://github.com/Azure/azure-sdk-tools --skill report-memory
Unless the user says otherwise, always apply these defaults:
production--language unless user specifies one)--format)The user will typically specify a calendar month by name (e.g. "March", "January 2025"). Resolve to the full month date range:
| User says | start_date | end_date |
|-----------|-----------|----------|
| "March" (current year) | YYYY-03-01 | YYYY-03-31 |
| "January 2025" | 2025-01-01 | 2025-01-31 |
| "March 1 to March 15" | YYYY-03-01 | YYYY-03-15 |
When only a month name is given without a year, use the current year. Be careful with month lengths (28/29/30/31 days).
Show the resolved command and run it immediately in a foreground terminal with a 60-second timeout (timeout: 60000). Redirect to a file since output can be large.
Full terminal command (cleanup + run):
New-Item -ItemType Directory -Path output -Force | Out-Null; if (Test-Path output/memory_output.json) { Remove-Item output/memory_output.json }; python cli.py report memory -s <start_date> -e <end_date> | Out-File -Encoding UTF8 output/memory_output.json
After the command completes, read the output file with read_file to get the JSON results. Summarize the findings for the user (total count, breakdown by language, etc.).
For follow-up questions about the same data (filtering, counting, searching), read the output file with read_file instead of re-running the command. The file is at output/memory_output.json.
# All memories for March 2025
python cli.py report memory -s 2025-03-01 -e 2025-03-31
# Python memories only
python cli.py report memory -s 2025-03-01 -e 2025-03-31 -l python
# YAML output
python cli.py report memory -s 2025-03-01 -e 2025-03-31 --format yaml
# Staging environment
python cli.py report memory -s 2025-03-01 -e 2025-03-31 --environment staging
| Flag | Type | Default | Description |
|------|------|---------|-------------|
| --start-date / -s | string | required | Start date (YYYY-MM-DD) |
| --end-date / -e | string | required | End date (YYYY-MM-DD) |
| --language / -l | string | all | Language to filter by (e.g., python, csharp, C#) |
| --environment | string | production | production or staging |
| --format / -f | string | json | Output format: json or yaml |
Each memory object includes:
id — Cosmos DB document IDlanguage — Language the memory applies tocreated_at — Human-readable timestamp (converted from Cosmos _ts)_ts: Filters by when the document was created/modified in Cosmos DB, not by any explicit date field in the memory content.read_file rather than relying on terminal output.python cli.py not .\avc: The avc.bat script may resolve to system Python.2>&1: Merges stderr into stdout, corrupting JSON. Only redirect stdout.>: Produces UTF-16 in PowerShell 5.1. Use | Out-File -Encoding UTF8.Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
Create institutional-quality equity research initiation reports through a 5-task workflow. Tasks must be executed individually with verified prerequisites - (1) company research, (2) financial modeling, (3) valuation analysis, (4) chart generation, (5) final report assembly. Each task produces specific deliverables (markdown docs, Excel models, charts, or DOCX reports). Tasks 3-5 have dependencies on earlier tasks.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.
Take azure/report-memory from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.