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/snarktank/code-editing-agent/analyze-complexitygit clone --depth 1 https://github.com/snarktank/code-editing-agentWhat 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 | $0.00633 |
| Opus 5 | $0.00000 | $0.00316 |
| Sonnet 5 | $0.00000 | $0.00127 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
analyze-complexity 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
100% identical to analyze-complexity — 0 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze task complexity and generate expansion recommendations.
Arguments: $ARGUMENTS
Perform deep analysis of task complexity across the project.
Complexity Analysis
Uses AI to analyze tasks and recommend which ones need breakdown.
Execution Options
task-master analyze-complexity [--research] [--threshold=5]
Analysis Parameters
--research→ Use research AI for deeper analysis--threshold=5→ Only flag tasks above complexity 5- Default: Analyze all pending tasks
Analysis Process
1. Task Evaluation
For each task, AI evaluates:
- Technical complexity
- Time requirements
- Dependency complexity
- Risk factors
- Knowledge requirements
2. Complexity Scoring
Assigns score 1-10 based on:
- Implementation difficulty
- Integration challenges
- Testing requirements
- Unknown factors
- Technical debt risk
3. Recommendations
For complex tasks:
- Suggest expansion approach
- Recommend subtask breakdown
- Identify risk areas
- Propose mitigation strategies
Smart Analysis Features
-
Pattern Recognition
- Similar task comparisons
- Historical complexity accuracy
- Team velocity consideration
- Technology stack factors
-
Contextual Factors
- Team expertise
- Available resources
- Timeline constraints
- Business criticality
-
Risk Assessment
- Technical risks
- Timeline risks
- Dependency risks
- Knowledge gaps
Output Format
Task Complexity Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
High Complexity Tasks (>7):
📍 #5 "Implement real-time sync" - Score: 9/10
Factors: WebSocket complexity, state management, conflict resolution
Recommendation: Expand into 5-7 subtasks
Risks: Performance, data consistency
📍 #12 "Migrate database schema" - Score: 8/10
Factors: Data migration, zero downtime, rollback strategy
Recommendation: Expand into 4-5 subtasks
Risks: Data loss, downtime
Medium Complexity Tasks (5-7):
📍 #23 "Add export functionality" - Score: 6/10
Consider expansion if timeline tight
Low Complexity Tasks (<5):
✅ 15 tasks - No expansion needed
Summary:
- Expand immediately: 2 tasks
- Consider expanding: 5 tasks
- Keep as-is: 15 tasks
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 · 121 lines · 0 tokens per session scan A 7eb8edfcfe21
analyze-complexity is a command published in the GitHub repository snarktank/code-editing-agent (24 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 633 tokens. A static security scan graded it A with 0 findings. It is 100% identical to analyze-complexity, differing in 0 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.