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 bengous/claude-code-plugins --skill goalifygit clone --depth 1 https://github.com/bengous/claude-code-pluginsWrote 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/bengous/claude-code-plugins/goalify)<a href="https://agentmods.dev/skills/bengous/claude-code-plugins/goalify"><img src="https://agentmods.dev/badge/skills/bengous/claude-code-plugins/goalify/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/bengous/claude-code-plugins/goalify"><img src="https://agentmods.dev/badge/skills/bengous/claude-code-plugins/goalify.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 | $0.00089 | $0.00979 |
| Opus 5.5 | $0.00036 | $0.00392 |
| Sonnet 5.5 | $0.00018 | $0.00196 |
| Haiku 4.5 | $0.00009 | $0.00098 |
Grade A, and why
goalify 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 24d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goalify
Convert messy intent into the smallest useful payload to hand to a fresh Claude Code agent.
You are not doing the downstream work. You produce goal text the human can paste
into a new session, hand to a subagent, or feed to /loop, or a local file path
they can pass along. Do not start, run, pause, resume, or complete the work
yourself, and do not launch a subagent to execute it.
Modes
Default mode is draft-first. Use it for /goalify, make this a goal, and
similar requests. Infer reasonable details and ask only when a missing detail
materially changes scope, risk, or acceptance criteria.
Interactive mode is question-first. Use it when the user says
/goalify interactive, interroge-moi, pose les questions une par une,
build this goal with me, or equivalent. Use the AskUserQuestion tool, one
decision at a time, and put your recommended option first. If a question can be
resolved by inspecting the codebase or local docs, inspect instead of asking.
Core Principle
A goal needs the durable destination, not a runbook.
Include only information that changes execution:
- objective
- relevant context
- constraints and side-effect boundaries, including explicit approval before any destructive or outward-facing side effect
- observable success criteria
- validation or evidence
- stop condition
- pause/blocker condition
Do not include generic role text such as "You are Claude Code". Do not restate obvious working-directory or repository facts unless they materially affect the goal.
Prompt Shape
Use a compact structure. Put the objective as the first line without an
Objective: label, because the consuming agent reads the first line as the task
statement. Prefer these labels for later sections when they carry real
information:
<objective sentence>
Context:
Constraints:
Success means:
Validate with:
Stop when:
Pause if:
Only include useful sections. For simple goals, fewer sections are better.
Output Rules
The payload is meant to be handed to a fresh agent, so keep it copy-pasteable.
What ships with it
1 file 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.
- 24d ago Changed · -13 lines 4bab83a4232e
- 1mo ago First seen · 147 lines · 89 tokens per session scan A 0db813586bfe
goalify is a skill published in the GitHub repository bengous/claude-code-plugins (5 stars, last pushed today), licensed MIT. It adds 89 tokens to every session and 979 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
development
An index of programming guidance for Python, Go, Rust, TypeScript, Java, C++, and shell scripting.
tencent-docs
A Tencent Docs assistant for creating, reading, editing, organizing, importing, and exporting online documents, spreadsheets, presentations, diagrams, and forms.
wechat-search
Search WeChat Official Account articles using OpenClaw's web search, Tavily API, and web fetch capabilities with compliance-focused design.
debugging-executions
Debug failed or wrong-output workflow executions using executions tools. Load when the user reports execution failures, unexpected node output, empty parameter values after a successful run, or a node showing a red or failed expression error. Also load when a live run you started for the user fails with an error from…
flow-next-tracker-sync
Project a flow-next spec to a tracker issue (Linear, GitHub, GitLab, Jira) and reconcile two-way. Use when asked to sync to a tracker. NOT plan-sync.