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 svy04/ballast --skill goalgit clone --depth 1 https://github.com/svy04/ballastWrote 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/svy04/ballast/goal)<a href="https://agentmods.dev/skills/svy04/ballast/goal"><img src="https://agentmods.dev/badge/skills/svy04/ballast/goal.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.01930 |
| Opus 5 | $0.00034 | $0.00965 |
| Sonnet 5 | $0.00014 | $0.00386 |
| Haiku 4.5 | $0.00007 | $0.00193 |
Grade A, and why
goal 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 8d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal engine
Premise: don't trust what you already think you know. Big goals rarely fail loudly — they fail quietly, on foundations that were guessed instead of verified. This pipeline replaces confidence with structure. It is domain-agnostic: a marketing campaign, a codebase, a field you have never touched.
Phase 0 — Intake
- Restate the goal and its definition of done, one line each. Confirm only what is genuinely the user's to decide: direction, taste, budget, deadlines, and who a deliverable is for. Tools and order are yours — do not ask about them.
- Check what already exists before planning to build anything: prior art, standard tools, the user's own assets. Adopting beats rebuilding.
- Ensure the memory structure exists (run brain-init if not). From here on, decisions are recorded the moment they happen, and unresolved items are registered, not remembered.
Phase 0.5 — Mobilize (summon what you already hold)
A goal never starts from zero: by the time it arrives you already hold rules, recorded decisions, verified notes, and skills. This phase exists because of a real, quiet failure: work can proceed competently from a blank page while the exact knowledge it needed sits unread in a file — knowledge held but not summoned degrades the result as surely as knowledge missing.
- Split the goal into branches that do not overlap and together cover the whole of it (MECE). Keep this coarse — a handful of branches, not a skeleton; Phase 2 does the fine cutting.
- For each branch, write down what it needs to be done well.
- Judge each branch against what already exists: the rules catalog,
memory/knowledge/, the decision ledger, existing skills. Where an asset exists, using it is mandatory — open it now and let it steer the branch, don't defer it to "later". Where nothing exists, learning is that branch's first task, and it enters the terrain scan first. - Leave a mobilization table the user can glance at: branch · what it needs · what's held (with the file) · gap → first move.
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.
- 8d ago First seen · 81 lines · 69 tokens per session scan A 4500b8c93490
goal is a skill published in the GitHub repository svy04/ballast (71 stars, last pushed 14d ago), licensed MIT. It adds 69 tokens to every session and 1,930 once invoked, about $0.0003 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
alive:demo
Generate a believable, lived-in ALIVE world from a free-text persona description (custom path) or a deterministic sandbox preset. Routes the create/list/activate/deactivate/delete/status surface and orchestrates the 5-stage subagent generation pipeline.
alive:system-upgrade
Upgrade ALIVE to the current version. Handles v1/v2/v3.x source states, multi-surface aware (alive-mcp / Hermes / Codex), retroactive version detection, partial-failure resume, dry-run previews, and rollback inspection.
alive:session-history
Revive sessions (quick or heavy), browse, and search — 'what happened recently?', 'find the session where we discussed X', 'revive yesterday's session'. For single-session recall and multi-session browsing. If the human needs to merge multiple sessions into one working context or detect conflicts between parallel…
alive:world
The human doesn't know what to work on, or wants to see everything at once. They need the big picture — what's active, what's stale, what needs attention. Renders a live world view grouped by ALIVE domain, then routes to open, tidy, find, history, or map.
alive:mine-for-context
Deep context extraction from source material. Creates reference bundles, builds extraction plans, tracks what's been extracted, and discovers new targets — people, subjects, patterns, connections. The archaeologist that turns raw sources into structured knowledge. Can be invoked by alive:session-history for targeted…
alive:settings
The human wants to adjust how the system behaves — not what it contains, but how it feels. Voice, rhythm, preferences, walnut-level config, full permissions toggleable, named squirrel config, action logging toggle. The system adapting to them. Routes to preferences.yaml or walnut config.yaml depending on scope. For…