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 commands/sakrut/ai-code-graph/complexity-reportgit clone --depth 1 https://github.com/sakrut/ai-code-graphWhat 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 | $0.00000 | $0.00612 |
| Opus 5 | $0.00000 | $0.00306 |
| Sonnet 5 | $0.00000 | $0.00122 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
complexity-report 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 yesterday.
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.
This is a copy
89% identical to complexity-report — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
Complexity Report
Arguments: $ARGUMENTS Display the task complexity analysis report.
Arguments: $ARGUMENTS
View the detailed complexity analysis generated by analyze-complexity command.
Viewing Complexity Report
Shows comprehensive task complexity analysis with actionable insights.
Execution
task-master complexity-report [--file=<path>]
Report Location
Default: .taskmaster/reports/complexity-analysis.md
Custom: Specify with --file parameter
Report Contents
1. Executive Summary
Complexity Analysis Summary
━━━━━━━━━━━━━━━━━━━━━━━━
Analysis Date: 2024-01-15
Tasks Analyzed: 32
High Complexity: 5 (16%)
Medium Complexity: 12 (37%)
Low Complexity: 15 (47%)
Critical Findings:
- 5 tasks need immediate expansion
- 3 tasks have high technical risk
- 2 tasks block critical path
2. Detailed Task Analysis
For each complex task:
- Complexity score breakdown
- Contributing factors
- Specific risks identified
- Expansion recommendations
- Similar completed tasks
3. Risk Matrix
Visual representation:
Risk vs Complexity Matrix
━━━━━━━━━━━━━━━━━━━━━━━
High Risk | #5(9) #12(8) | #23(6)
Med Risk | #34(7) | #45(5) #67(5)
Low Risk | #78(8) | [15 tasks]
| High Complex | Med Complex
4. Recommendations
Immediate Actions:
- Expand task #5 - Critical path + high complexity
- Expand task #12 - High risk + dependencies
- Review task #34 - Consider splitting
Sprint Planning:
- Don't schedule multiple high-complexity tasks together
- Ensure expertise available for complex tasks
- Build in buffer time for unknowns
Interactive Features
When viewing report:
-
Quick Actions
- Press 'e' to expand a task
- Press 'd' for task details
- Press 'r' to refresh analysis
-
Filtering
- View by complexity level
- Filter by risk factors
- Show only actionable items
-
Export Options
- Markdown format
- CSV for spreadsheets
- JSON for tools
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.
- yesterday First seen · 120 lines · 0 tokens per session scan A 6a0ad50ac6d9
complexity-report is a command published in the GitHub repository sakrut/ai-code-graph (3 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 612 tokens. A static security scan graded it A with 0 findings. It is 89% identical to complexity-report, differing in 9 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.