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 skills add sharpdeveye/maestro --skill tempergit clone --depth 1 https://github.com/sharpdeveye/maestroWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sharpdeveye/maestro/temper)<a href="https://agentmods.dev/skills/sharpdeveye/maestro/temper"><img src="https://agentmods.dev/badge/skills/sharpdeveye/maestro/temper/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sharpdeveye/maestro/temper"><img src="https://agentmods.dev/badge/skills/sharpdeveye/maestro/temper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00026 | $0.00574 |
| Opus 5.5 | $0.00010 | $0.00230 |
| Sonnet 5.5 | $0.00005 | $0.00115 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
temper 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 1mo 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the agent-architecture reference in the agent-workflow skill for topology patterns and when multi-agent is justified.
Pull back from over-engineering. The most common mistake isn't building too little — it's building too much.
Over-Engineering Detection
Signs you've over-engineered:
- Multi-agent for a single-agent problem
- Premature optimization before you have performance data
- Abstraction layers with one implementation
- Configuration for things that never change
- Evaluation loops on non-critical outputs
- Framework before features
The Complexity Test
For each component:
- Is this solving a problem we actually have? (not "might have")
- Is this the simplest solution that works?
- Would removing this break anything? (if not, remove it)
- Can someone new understand this in 5 minutes? (if not, simplify)
Tempering Strategies
Collapse Unnecessary Agents
OVER-ENGINEERED: User → Classifier → Router → Specialist → Formatter → Checker (6 components)
TEMPERED: User → Single Agent with good prompt (1 component, same quality)
Remove Premature Abstraction
OVER-ENGINEERED: class AgentOrchestrator with 5 strategy interfaces
TEMPERED: async function runWorkflow(input) — direct, readable
Simplify Configuration
OVER-ENGINEERED: config.yaml (200 lines, 47 params, 3 inheritance levels)
TEMPERED: config.yaml (20 lines, essential params only, sensible defaults)
What NOT to Temper
- Error handling — essential, not overhead
- Logging — saves you when things go wrong
- Input validation — prevents cascading failures
- Core guardrails — safety is non-negotiable
- The golden test set — how you know it still works
Recommended Next Step
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.
- 1mo ago First seen · 81 lines · 26 tokens per session scan A 1dce3625b7fd
temper is a skill published in the GitHub repository sharpdeveye/maestro (591 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 574 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
aider-delegate
Delegate coding tasks to Aider (aider) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
agy-delegate
Delegate coding tasks to the Google Antigravity CLI (agy) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
agent-self-scheduling
Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
deep-interview
Socratic deep interview with mathematical ambiguity gating before execution.
plan
Strategic planning with optional interview workflow.
web-clone
URL-driven website cloning with visual + functional verification.