Borrowing it
Nothing to install: this file belongs to seasonedcc/discord-manager. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/seasonedcc/discord-manager/main/.claude/skills/subagents/SKILL.mdgit clone --depth 1 https://github.com/seasonedcc/discord-managerWrote 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/seasonedcc/discord-manager/subagents)<a href="https://agentmods.dev/skills/seasonedcc/discord-manager/subagents"><img src="https://agentmods.dev/badge/skills/seasonedcc/discord-manager/subagents/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/seasonedcc/discord-manager/subagents"><img src="https://agentmods.dev/badge/skills/seasonedcc/discord-manager/subagents.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 | $0.00057 | $0.04172 |
| Opus 5.5 | $0.00023 | $0.01669 |
| Sonnet 5.5 | $0.00011 | $0.00834 |
| Haiku 4.5 | $0.00006 | $0.00417 |
Grade A, and why
subagents 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 2mo 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subagents and Dynamic Workflows
Everything here documents the repository as it is on main. If main disagrees with this file, main wins: follow it and flag the drift.
The orchestrator delegates execution and keeps its own context lean. The two decisions that make delegation work are how big to make each subagent's task and which model runs it. Getting the size right matters more than anything else: it is the difference between coherent, trustworthy work and hallucinated, low-quality work.
This skill is self-improving. When a better sizing heuristic or a new calibration point emerges, update it.
The context budget: aim for ~33% of the 1M window
- Fable, Opus, and Sonnet all have 1,000,000-token context windows. So ~33% ≈ 330k tokens.
- These models begin degrading around 25–33% window fill. Past ~33%, distrust the output — that is where hallucinations, silently-dropped requirements, and quality regressions appear. Do not trust results from a context much fuller than a third.
- Design each task to LAND near 33% by the time it finishes — not to blow past it. The target is the end state of a healthy task, not a ceiling to race toward.
- Soft rule, not hard: if an agent is genuinely near the end of its task when it crosses 33%, let it finish — interrupting or compacting mid-task is worse than mild degradation at the finish line. What must be avoided is an agent still doing substantive work at 50%+.
Sizing: the tradeoff to balance
- Too small → over-parallelization. Many agents each holding a partial view produce fragmented, less coherent work with integration seams, duplicated context loading, and coordination overhead.
- Too big → the context overflows past ~33%, performance degrades, and either the quality drops (hallucinations, missed requirements) or the agent dies mid-task with its work wasted.
- Right-sized = one coherent deliverable an agent can hold entirely in a healthy (<~33%) context, including all the reading and gate-iteration the task requires.
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.
- 2mo ago First seen · 150 lines · 57 tokens per session scan A b1a838d98f09
subagents is a skill published in the GitHub repository seasonedcc/discord-manager (3 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 4,172 once invoked, about $0.0002 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 2026-08-31.
subagents compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| subagentsseasonedcc/discord-manager | Skill | 3 | 2d ago | MIT | A | 57 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |