Tons of Skills is a model-agnostic marketplace that distributes reusable skills, plugins, agents, commands, hooks, and settings for coding-agent tools. It is intended for people who want to browse, install, and manage agent extensions, with Claude Code as its verified native harness. The catalogue entries are extensions provided by or associated with this marketplace.
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 jeremylongshore/tons-of-skills-marketplace --skill dispatchgit clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplaceWrote 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/jeremylongshore/tons-of-skills-marketplace/dispatch)<a href="https://agentmods.dev/skills/jeremylongshore/tons-of-skills-marketplace/dispatch"><img src="https://agentmods.dev/badge/skills/jeremylongshore/tons-of-skills-marketplace/dispatch/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/jeremylongshore/tons-of-skills-marketplace/dispatch"><img src="https://agentmods.dev/badge/skills/jeremylongshore/tons-of-skills-marketplace/dispatch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 369 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 4 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 329 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 383 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00081 | $0.12636 |
| Opus 5.5 | $0.00032 | $0.05054 |
| Sonnet 5.5 | $0.00016 | $0.02527 |
| Haiku 4.5 | $0.00008 | $0.01264 |
Grade A, and why
dispatch 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 — 473 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dispatch
Workhorse phase. Picks up a task file from /hyperflow:plan and runs it through the orchestrator pattern with parallel worker dispatch and multi-level reviews.
This skill exercises Layer 3 (Orchestrator), Layer 5 (Quality Gates), Layer 6 (Project Memory), Layer 8 (Git Workflow), and Layer 9 (Security) from the doctrine. Multi-level review (L1–L5) is applied per the triage's flow profile.
Per-Step Agent Map (DOCTRINE rule 12 — §12.1 inline-allowed for trivial steps · §12.2 sub-phase decomposition)
Every substantive step dispatches at least one Agent. Trivial steps (≤ 2 tool calls, no content generation, no decision-making, mechanically verifiable) MAY be performed inline by the orchestrator per §12.1. Non-trivial steps decompose into ≥ 2 named sub-phases per §12.2.
| Step | Sub-phase | Workers | Reviewer | Notes |
|---|---|---|---|---|
| 0 — Mode confirm | — (exempt) | — | — | AskUserQuestion only |
| 0.5 — Operational choices | — (exempt) | — | — | AskUserQuestion only |
| 1 — Load task | — (atomic · §12.2.8) | — | — | Read + schema check = single mechanical decision; no parallel angles |
| 2a — Pre-dispatch | Composer × N parallel — one per sub-task; stitches persona + injects learnings | Reviewer — reviews prompt set for completeness | Parallel worker prompts built before any fan-out fires | |
| 2b — Worker fan-out | Implementer / Searcher / Writer × N parallel | Domain specialist Reviewer — the Specialist:-matched agent, batched over full batch (P2) or per-sub-task fallback |
One Reviewer call per batch; security/correctness specialists run with --thorough |
|
| 2c — Gate run | Worker — runs lint/typecheck/tests on affected files | Reviewer — judges gate output | Small focused diff | |
| 2d — Learnings + commit | Writer — synthesizes per-batch learnings | — (mechanical commit · §12.1) | Per-sub-task PASS commits land here; learnings appended to context | |
| 3 — Final integration review | — (atomic · §12.2.8) | Reviewer — broadest matching specialist(s), L1–L over full diff | Single Reviewer dispatch; skipped under D7 incl. single-specialist coverage (rule 17) | |
| 4 — Wrap up | Writer — optional; only if memory prose is non-trivial | — | §12.1 trivial-inline; no Reviewer (D5) | |
| 5 — End of chain | — (exempt) | — | ONE AskUserQuestion with audit + deploy questions |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 473 lines · 81 tokens per session scan A 5aab2c0b03b9
dispatch is a skill published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,881 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 12,636 once invoked, about $0.0003 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
orchestrate
This skill should be used when the user asks to 'orchestrate a task', 'break down work into parallel agents', 'coordinate subtasks', 'run agents in parallel', or mentions 'multi-agent'. Decomposes complex tasks into tracked subtasks, dispatches parallel subagents, and coordinates until completion.
fabric-swarm
Creates a self-organizing team of persistent Pi Fabric actors with durable topics, mailboxes, and compare-and-swap tasks. Use for messenger-like collaboration and long-lived delegated work.
squads-cli
Squads CLI — operate your squads through the loop; intent in, reviewed work out. TRIGGER when running squads, dispatching agents, triaging the inbox, reading/writing memory, recording feedback, or checking status/usage.
gh
You have access to gh (GitHub CLI) for repository and project management.
dagr-producer
Emit and maintain a dagr run file — a live, contract-valid JSON description of recursive projects, tasks, attempts, gates, evidence, policies, events, and operator-message resolutions that dagr view renders as a DAG. Use when orchestrating agents or tracking multi-step work that a dagr pane should display.
review-team
A multi-reviewer code review process that checks a change from several specialist viewpoints and combines the results into one report. It can cover bugs, security, tests, dependencies, frontend behavior, and continuous-integration workflows.