claude-skills is a large collection of reusable skills, commands, plugins, and supporting resources for coding agents across engineering, business, research, compliance, and productivity work. It is used by people who want agents such as Claude Code, Codex, Gemini CLI, or Cursor to follow specialized workflows. The catalogue contains selected skills and settings from the collection.
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 alirezarezvani/claude-skills --skill grade-iterategit clone --depth 1 https://github.com/alirezarezvani/claude-skillsWrote 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/alirezarezvani/claude-skills/grade-iterate)<a href="https://agentmods.dev/skills/alirezarezvani/claude-skills/grade-iterate"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/grade-iterate/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/alirezarezvani/claude-skills/grade-iterate"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-skills/grade-iterate.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 | $0.00194 | $0.01060 |
| Opus 5.5 | $0.00078 | $0.00424 |
| Sonnet 5.5 | $0.00039 | $0.00212 |
| Haiku 4.5 | $0.00019 | $0.00106 |
Grade A, and why
grade-iterate 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 today.
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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 3 — Grade → Iterate (the bounded loop)
This is the plugin's loop: CMA's outcome primitive self-grades the agent's
work in an isolated context and feeds failing verdicts back for the next attempt.
It is always bounded by max_iterations (1..20) — never "improve forever".
See ../../references/loops-and-workflows.md
and the outcome section of
../../references/cma-primitives.md.
Workflow
- Define the outcome.
The rubric is required;python3 scripts/outcome_builder.py \ --sheet ./my-agent/build-sheet.json --max-iterations 5 \ --out ./my-agent/payloads/outcome.jsonmax_iterationsis clamped to 1..20. Send the payload as auser.define_outcomeevent (append to the running session). - Read every verdict first.
Tables the rubric outcome and recommends: SHIP (python3 scripts/verdict_reader.py --result ./my-agent/last-verdict.jsonsatisfied), SHARPEN then re-run (needs_revision), ESCALATE (max_iterations_reached/failed), RESUME (interrupted). With ≤1 iteration left it flips to "make the single highest-value fix or escalate now". - Loop invariant. Each iteration must move ≥1 rubric line fail→pass, or the run halts at the cap and escalates. Don't burn the budget on cosmetic edits.
- Once a version passes, run held-back eval.
Held-back cases (never seen during iteration) run in parallel, capped at the 25-thread CMA ceiling, each graded against the same rubric.python3 scripts/eval_scaffold.py \ --sheet ./my-agent/build-sheet.json --out ./my-agent/eval.json --concurrency 5 - Decide. SHIP as v0, or promote to a scheduled deployment (Phase 4). Record
the verdict on the goal:
goal_state.py set --phase run-without-you.
Hard rules
- Bounded, always. No outcome without a
max_iterationscap. - Read the verdict before acting. The grader's explanation drives the next move.
- Held-back cases are held back. Never grade generalization on cases the agent already iterated against.
What ships with it
4 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.
- today First seen · 75 lines · 194 tokens per session scan A 601c5e8d0061
grade-iterate is a skill published in the GitHub repository alirezarezvani/claude-skills (28,607 stars, last pushed yesterday), licensed MIT. It adds 194 tokens to every session and 1,060 once invoked, about $0.0008 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 2026-10-24.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…