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 attentiondotnet/davidondrej-skills --skill goal-loopgit clone --depth 1 https://github.com/attentiondotnet/davidondrej-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/attentiondotnet/davidondrej-skills/goal-loop)<a href="https://agentmods.dev/skills/attentiondotnet/davidondrej-skills/goal-loop"><img src="https://agentmods.dev/badge/skills/attentiondotnet/davidondrej-skills/goal-loop/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/attentiondotnet/davidondrej-skills/goal-loop"><img src="https://agentmods.dev/badge/skills/attentiondotnet/davidondrej-skills/goal-loop.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 | $0.00093 | $0.02306 |
| Opus 5 | $0.00046 | $0.01153 |
| Sonnet 5 | $0.00019 | $0.00461 |
| Haiku 4.5 | $0.00009 | $0.00231 |
Grade A, and why
goal-loop 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 10d 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.
This is a copy
98% identical to goal-loop — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent /goal Loop
What /goal is
/goal is a slash command that turns an agent prompt into a persistent agent looping plan → act → test → review → iterate until a stop condition is met, the user pauses, or the token budget runs out. Internally called the "Ralph loop."
Agents with the /goal feature right now: Codex, Claude Code, and Hermes Agent.
Key difference from a normal prompt: when a turn ends but the goal isn't met, the agent auto-continues instead of waiting for input.
Lifecycle states: pursuing, paused, achieved, unmet, budget-limited.
When monitoring a running /goal, every check should include a one-line update to the user: what the agent is doing and whether it is on track. Keep it extremely concise.
Not: a budget command, a safety boundary, "run forever", or a replacement for /plan. It's a contract enforcer with a verification loop.
Requirements
- An agent with the
/goalfeature — right now: Codex, Claude Code, or Hermes Agent - The goals feature enabled in the agent's config
- Subscription auth — API-key auth does not work. A pro-tier plan is the realistic minimum for long runs.
When to use it
Use only when all three are true:
- Task is >30 min of mechanical work.
- There's a verifiable stop condition (tests pass, coverage hit, eval ≥ X, build green).
- Repo is agent-ready (working build, decent tests,
AGENTS.mdpresent).
Fits: migrations, coverage lifts, TDD feature builds, refactors with contract tests, prompt/eval optimization, deploy retry loops, bug-repro-then-fix.
Bad fits: exploratory work, vague "improve this", anything without a "done" definition, prod credentials, destructive shared-infra ops.
The 5-part contract (every goal needs this)
- Objective — one sentence, one concrete outcome.
- Constraints — what must NOT change (public API, files, libs, conventions).
- Validation command — the exact shell command that proves progress (
pytest -q,pnpm test, etc.). - Stop condition — verifiable: "Stop when X passes" OR "when further changes need human/product input."
- Documentation — one sentence instructing the agent to write concise, targeted docs for every change, either creating new
.mdfiles or updating existing ones.
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.
- 10d ago First seen · 160 lines · 93 tokens per session scan A 2f8b007493f8
goal-loop is a skill published in the GitHub repository attentiondotnet/davidondrej-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 2,306 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to goal-loop, differing in 2 lines, and is treated as a copy.
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