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/data-wise/craft/insights-applynpx skills add Data-Wise/craft --skill insights-applygit clone --depth 1 https://github.com/Data-Wise/craftWhat 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 | $0.00070 | $0.01614 |
| Opus 5 | $0.00035 | $0.00807 |
| Sonnet 5 | $0.00014 | $0.00323 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
insights-apply 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.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Insights Apply
Extract CLAUDE.md improvement suggestions from the /insights report and apply them through the sync pipeline. Targets the global CLAUDE.md only (insights are cross-project).
When to Use
- User says "apply insights", "update rules from insights"
- After running
/insightsand seeingclaude_md_additionssuggestions - User wants to incorporate session learnings into CLAUDE.md
- Insights data exists at
~/.claude/usage-data/
Prerequisites
- Insights report must exist (
~/.claude/usage-data/report.htmlor facets data) - Global CLAUDE.md at
~/.claude/CLAUDE.md - Python3 available (for sync pipeline)
Insights Apply Pipeline
Execute these steps in order.
Step 1: Parse Insights Report
Locate and parse the insights data:
# Check for insights data
if [ -f ~/.claude/usage-data/report.html ]; then
echo "Found insights report"
elif [ -d ~/.claude/usage-data/facets/ ]; then
echo "Found facets data"
else
echo "No insights data found. Run /insights first."
exit 1
fi
Extract the claude_md_additions array from the report. These are structured suggestions with:
title: Section heading for CLAUDE.mdcontent: The actual rules/guidance to addsource: Which sessions/patterns generated this suggestionpriority: How impactful the suggestion is (high/medium/low)
Step 2: Present Suggestions
Show each suggestion with context for user review:
┌───────────────────────────────────────────────────────────────┐
│ INSIGHTS SUGGESTIONS (N found) │
├───────────────────────────────────────────────────────────────┤
│ │
│ Suggestion 1/N: <title> │
│ Source: <N sessions with this pattern> │
│ Priority: <high|medium|low> │
│ │
│ Proposed CLAUDE.md addition: │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ ## <Title> │ │
│ │ - <rule 1> │ │
│ │ - <rule 2> │ │
│ │ - <rule 3> │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
└───────────────────────────────────────────────────────────────┘
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 · 205 lines · 70 tokens per session scan A 9ff7b79a5ec1
insights-apply is a skill published in the GitHub repository Data-Wise/craft (4 stars, last pushed 16d ago), licensed MIT. It adds 70 tokens to every session and 1,614 once invoked, about $0.0003 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 2026-08-31.
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