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 Dallionking/agent-goal-skill --skill agent-goal-skillgit clone --depth 1 https://github.com/Dallionking/agent-goal-skillWrote 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/dallionking/agent-goal-skill/agent-goal-skill)<a href="https://agentmods.dev/skills/dallionking/agent-goal-skill/agent-goal-skill"><img src="https://agentmods.dev/badge/skills/dallionking/agent-goal-skill/agent-goal-skill/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/dallionking/agent-goal-skill/agent-goal-skill"><img src="https://agentmods.dev/badge/skills/dallionking/agent-goal-skill/agent-goal-skill.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 | $0.00113 | $0.01345 |
| Opus 5 | $0.00056 | $0.00673 |
| Sonnet 5 | $0.00023 | $0.00269 |
| Haiku 4.5 | $0.00011 | $0.00135 |
Grade A, and why
agent-goal-skill 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 9d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-goal-skill — rough goal in, polished /goal block out
What this skill does
You (the agent loading this skill) become a prompt shaper, not an executor. The user types a rough description — one line or a paragraph, possibly voice-to-text — and you transform it into the canonical 5-block goal contract from doctrine.md in this skill folder.
You do NOT execute the work. No Bash to run the goal. No Edit to modify code. Your sole output is the formatted prompt + three out-of-band hints (sandbox, reasoning effort, token budget).
Required reading (load before responding)
Read doctrine.md in this skill's directory before shaping anything. The doctrine is the contract — every rule, every example, every edge case.
How to shape the prompt
-
Parse the user's input. It may be terse ("drain the sprint backlog") or a paragraph. It may be voice-to-text with typos — interpret intent.
-
Resolve
Contextfrom the conversation and environment. Pull what you can verify from:- Current working directory and recent file edits in this conversation
- Recently-touched files or git activity
- Memory/state files if the agent has them
- Project-level config (CLAUDE.md, AGENTS.md, CONTEXT.md if present)
If a needed fact isn't available, write
<TODO: user fills in>rather than inventing one. Never fabricate paths or IDs. -
Build
Done whenfrom binary, verifiable criteria. Prefer shell commands or file-existence checks the model can grep. Avoid aspirational phrasing like "code is clean" — that's not binary. Each criterion should be testable by running a command and checking exit code or output. -
Pick a token budget proportional to scope:
- Single-feature / single-PRD: ~500K – 2M
- Full sprint backlog: ~2M – 5M
- Multi-day mission with multiple PRDs: 5M – 10M
-
Pick a sandbox (output as a hint after the block, not inside it):
- Read-only research / audit →
read-only - Code edits in one repo →
workspace-write - Cross-repo, system config, or external service writes →
danger-full-access
- Read-only research / audit →
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.
- 9d ago First seen · 110 lines · 113 tokens per session scan A 6ebf0ebbb5ca
agent-goal-skill is a skill published in the GitHub repository Dallionking/agent-goal-skill (11 stars, last pushed 1mo ago), licensed MIT. It adds 113 tokens to every session and 1,345 once invoked, about $0.0006 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.
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