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/wshobson/agents/improve-agentgit clone --depth 1 https://github.com/wshobson/agentsWhat 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 | $0.01896 |
| Opus 5 | $0.00000 | $0.00948 |
| Sonnet 5 | $0.00000 | $0.00379 |
| Haiku 4.5 | $0.00000 | $0.00190 |
Grade A, and why
improve-agent 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
3 near-identical copies found in the catalogue:
- improve-agent — 100% identical, 28 lines differ
- improve-agent — 100% identical, 28 lines differ
- improve-agent — 100% identical, 28 lines differ
How it starts
The opening of the file, as written. The whole thing — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Performance Optimization Workflow
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
[Extended thinking: Agent optimization requires a data-driven approach combining performance metrics, user feedback analysis, and advanced prompt engineering techniques. Success depends on systematic evaluation, targeted improvements, and rigorous testing with rollback capabilities for production safety.]
Phase 1: Performance Analysis and Baseline Metrics
Comprehensive analysis of agent performance using context-manager for historical data collection.
1.1 Gather Performance Data
Use: context-manager
Command: analyze-agent-performance $ARGUMENTS --days 30
Collect metrics including:
- Task completion rate (successful vs failed tasks)
- Response accuracy and factual correctness
- Tool usage efficiency (correct tools, call frequency)
- Average response time and token consumption
- User satisfaction indicators (corrections, retries)
- Hallucination incidents and error patterns
1.2 User Feedback Pattern Analysis
Identify recurring patterns in user interactions:
- Correction patterns: Where users consistently modify outputs
- Clarification requests: Common areas of ambiguity
- Task abandonment: Points where users give up
- Follow-up questions: Indicators of incomplete responses
- Positive feedback: Successful patterns to preserve
1.3 Failure Mode Classification
Categorize failures by root cause:
- Instruction misunderstanding: Role or task confusion
- Output format errors: Structure or formatting issues
- Context loss: Long conversation degradation
- Tool misuse: Incorrect or inefficient tool selection
- Constraint violations: Safety or business rule breaches
- Edge case handling: Unusual input scenarios
1.4 Baseline Performance Report
Generate quantitative baseline metrics:
Performance Baseline:
- Task Success Rate: [X%]
- Average Corrections per Task: [Y]
- Tool Call Efficiency: [Z%]
- User Satisfaction Score: [1-10]
- Average Response Latency: [Xms]
- Token Efficiency Ratio: [X:Y]
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 · 320 lines · 0 tokens per session scan A 8e283c1bf580
improve-agent is a command published in the GitHub repository wshobson/agents (39,318 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,896 tokens. 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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