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 plangit 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/plan)<a href="https://agentmods.dev/skills/jeremylongshore/tons-of-skills-marketplace/plan"><img src="https://agentmods.dev/badge/skills/jeremylongshore/tons-of-skills-marketplace/plan/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/plan"><img src="https://agentmods.dev/badge/skills/jeremylongshore/tons-of-skills-marketplace/plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 104 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 | $0.00144 | $0.09462 |
| Opus 5 | $0.00072 | $0.04731 |
| Sonnet 5 | $0.00029 | $0.01892 |
| Haiku 4.5 | $0.00014 | $0.00946 |
Grade A, and why
plan 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 13d 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan
One chain-starter that folds three phases — amplify (sharpen the prompt), design (brainstorm and spec the approach), decompose (write the batched task file) — into a single flow, then stops at a build-location gate (Step 12). Each phase skips itself when the request doesn't need it: a clear task bounces straight to decomposition; an already-structured prompt skips amplify.
Plan never implements. It is thinking, not building — no source code is written here, and it does not silently chain into /hyperflow:dispatch. The only writes are to .hyperflow/specs/, .hyperflow/tasks/, .hyperflow/features/, .hyperflow/memory/, and (another-session mode) the committed .hyperflow-handoff/ package. When the task file is ready, plan always asks where to build it (this session / another session / stop) — that gate fires on every run, and the user's choice is the only thing that ever starts a build. It drives Layer 0.5 (Triage), Layer 4 (Brainstorming/Spec), Layer 0 (Project Analysis), Layer 6 (Memory), and Layer 7 (Task Templates).
Plan runs at maximum thinking depth. Engage extended / ultra reasoning across triage, analysis, design, and decomposition — plan is the chain's one think-heavy front door and pays the reasoning cost once so the build runs faithfully. Every substantive step uses tools (Agents to do the work, Write to persist artefacts); a plan that exists only in chat is a violation.
Every agent runs on the current session model — there is no model-tier routing and no model configuration. Roles (Classifier, Searcher, Writer, Analyst, Planner, Reviewer) differ by responsibility, not by model.
Iron Rules
- Plan never implements. It always stops at the build-location gate (Step 12) and asks where to build — this session / another session / stop. It does NOT silently chain into
dispatch. The gate fires every run, even when asession=/commit=/branch=arg was somehow propagated — never skip it. - Max thinking, always. Run at maximum reasoning depth (ultrathink) through triage, analysis, design, and decomposition. Plan is the one think-heavy phase; do not shortcut the reasoning to save tokens.
- Always produce the artefacts on disk. Every run that reaches decomposition uses tools to
Writethe spec (when the design phase ran) and the task file + briefs. A plan described only in chat — no.hyperflow/tasks/<slug>.mdwritten — is a failed run, not a plan. - No code in the design phase. Plan produces a prompt, a spec, and a task file;
dispatchexecutes them. - Author build-ready briefs (
briefs=auto). Every non-trivial sub-task gets a full, self-contained implementation brief at plan time (Step 9c), stored at.hyperflow/tasks/<slug>/T<id>.md. The strong planning model pays the authoring cost once so the build runs faithfully on a cheaper model or a second session — dispatch transcribes, it doesn't re-derive. Trivial sub-tasks stay terse. - Project rules win on conflict. A rule in
CLAUDE.md/AGENTS.md/.hyperflow/memory/overrides a generic persona standard — it is the user's explicit instruction. - Economy is mandatory. Amplify enhances to the task's level, never inflates a one-line ask into a spec (rubric dim 8).
- Names responsible specialists; never runs their web-research. The
Responsible specialists:annotation is an announcement — each specialist's web-research-first pass fires later insidedispatch, not here. - No AI attribution in any prompt, spec, task file, or memory entry — describe the work, never the author.
- Failure recovery (rule 14). Worker/Reviewer errors, malformed output, and NEEDS_REVISION cadence follow
../hyperflow/failure-recovery.md. Retry → escalate → abort. Chain budget: 3 cumulative aborts.
What ships with it
2 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.
- 13d ago First seen · 278 lines · 144 tokens per session scan A 06355295fe4e
plan is a skill published in the GitHub repository jeremylongshore/tons-of-skills-marketplace (2,717 stars, last pushed today), licensed MIT. It adds 144 tokens to every session and 9,462 once invoked, about $0.0007 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.
Other skills, from other repositories
bim-classification-ai
Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.
ai-wrapper-product
You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.
ai-evals
Create an AI Evals Pack (eval PRD, test set, rubric, judge plan, results + iteration loop). See also: building-with-llms (build), ai-product-strategy (strategy).
ai-product-strategy
Create an AI Product Strategy Pack (thesis, use cases, system plan, eval plan, roadmap).
behavioral-product-design
Apply behavioral science to product design: target behavior, intervention map, experiment plan.
building-with-llms
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). See also: ai-evals (eval only), ai-product-strategy (strategy only).