Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/anthropics/knowledge-work-plugins/analyzenpx skills add anthropics/knowledge-work-plugins --skill analyzegit clone --depth 1 https://github.com/anthropics/knowledge-work-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00045 | $0.00955 |
| Opus 5 | $0.00023 | $0.00477 |
| Sonnet 5 | $0.00009 | $0.00191 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
analyze scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- analyze — 100% identical, 0 lines differ
- analyze-th — 94% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze - Answer Data Questions
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Answer a data question, from a quick lookup to a full analysis to a formal report.
Usage
/analyze <natural language question>
Workflow
1. Understand the Question
Parse the user's question and determine:
- Complexity level:
- Quick answer: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
- Full analysis: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
- Formal report: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
- Data requirements: Which tables, metrics, dimensions, and time ranges are needed
- Output format: Number, table, chart, narrative, or combination
2. Gather Data
If a data warehouse MCP server is connected:
- Explore the schema to find relevant tables and columns
- Write SQL query(ies) to extract the needed data
- Execute the query and retrieve results
- If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
- If results look unexpected, run sanity checks before proceeding
If no data warehouse is connected:
- Ask the user to provide data in one of these ways:
- Paste query results directly
- Upload a CSV or Excel file
- Describe the schema so you can write queries for them to run
- If writing queries for manual execution, use the
sql-queriesskill for dialect-specific best practices - Once data is provided, proceed with analysis
3. Analyze
- Calculate relevant metrics, aggregations, and comparisons
- Identify patterns, trends, outliers, and anomalies
- Compare across dimensions (time periods, segments, categories)
- For complex analyses, break the problem into sub-questions and address each
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 120 lines · 45 tokens per session scan A 4ee816363d94
analyze is a skill published in the GitHub repository anthropics/knowledge-work-plugins (23,791 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 955 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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