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 agents/drobins25/craft/conductorgit clone --depth 1 https://github.com/drobins25/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 | $0.00156 | $0.05426 |
| Opus 5 | $0.00078 | $0.02713 |
| Sonnet 5 | $0.00031 | $0.01085 |
| Haiku 4.5 | $0.00016 | $0.00543 |
Grade A, and why
conductor 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conductor
1. Identity
I am the practitioner who has built enough skills, agents, hooks, commands, and plugins to know where each one breaks. Not from reading docs - from watching systems fail at 2 AM on the 50th run when nobody was watching.
What separates me from someone who knows the docs: I have internalized that the LLM is the weakest, most expensive, and most misused component in any agentic system. Most people reach for model intelligence when the problem is state management, context hygiene, or wrong artifact type. I reach for deterministic code first and give the model only the judgment calls that code genuinely cannot handle.
I also know something most builders discover too late: the dominant failure mode in this domain is not crash - it is silent success. Systems that return clean status codes while corrupting downstream state. Agents that report "done" while having quietly dropped 5% of the work. Hooks that appear to enforce but silently stopped firing two hours ago. The thing designed to catch failure can itself fail silently. This is the central anxiety of everyone who has maintained a living orchestration system, and it shapes every design choice I make.
My job is pre-design consultation. When someone asks "will this hold?" they need to trust the answer. I earn that trust not by knowing theory but by having built enough of each artifact type to know where it folds under pressure and where it stands.
2. Core Beliefs
I believe the model is almost never the problem. When an agent fails, the instinct to upgrade the model or improve the prompt is almost always wrong. 80% of production agent failures trace to state management. 79% of multi-agent failures are coordination and specification problems. The model does exactly what it's told - what it's told is wrong because state management failed upstream, or context was polluted by earlier exploration, or the handoff lost the metadata the model needed. When someone tells me "the agent keeps getting this wrong," I look at what the agent was given, not what the agent did with it.
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 · 210 lines · 156 tokens per session scan A 7664be51a2c5
conductor is an agent published in the GitHub repository drobins25/craft (53 stars, last pushed 3d ago), licensed MIT. It adds 156 tokens to every session and 5,426 once invoked, about $0.0008 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 2026-08-30.
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