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 xg-gh-25/SwarmAI --skill s_ddd-distributegit clone --depth 1 https://github.com/xg-gh-25/SwarmAIWrote 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/xg-gh-25/swarmai/s_ddd-distribute)<a href="https://agentmods.dev/skills/xg-gh-25/swarmai/s_ddd-distribute"><img src="https://agentmods.dev/badge/skills/xg-gh-25/swarmai/s_ddd-distribute/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/xg-gh-25/swarmai/s_ddd-distribute"><img src="https://agentmods.dev/badge/skills/xg-gh-25/swarmai/s_ddd-distribute.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00209 | $0.01218 |
| Opus 5 | $0.00105 | $0.00609 |
| Sonnet 5 | $0.00042 | $0.00244 |
| Haiku 4.5 | $0.00021 | $0.00122 |
Grade A, and why
ddd-distribute 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DDD Distribute (s_ddd-distribute) — the distribution step of the DDD lifecycle
Render a grown DDD into a distributable capability package so another agent host (Kiro / Quick / Claude Code / a public consumer) can install and use it.
This skill is a thin human-in-the-loop wrapper — all packaging logic lives in
core/ddd_packager.py and the reach policy in core/ddd_distribution_policy.py.
The skill's job is: read the declaration → confirm the target subset with the human
→ call the packager → surface warnings and the built path.
The reach model (READ BEFORE RUNNING)
A DDD declares its own reach in its aim.json:
"distribution": {
"targets": ["aim-capabilities", "open-plugin"], // the CEILING (0..2)
"visibility": "internal" // internal | external
}
- The declaration is the ceiling. You may emit a SUBSET of the declared targets; you may NEVER add an undeclared target or raise visibility. To widen reach, the DDD owner edits the declaration first (a separate, deliberate change).
- Fail-closed. An absent / malformed declaration →
targets:[]= not distributable. Nothing leaves by inference. - Emit ≠ publish. A
visibility:internalDDD may EMIT an open-plugin tree for a private install, but the public-publish step is refused until visibility is explicitlyexternal(a human-gated change).
Workflow (HITL)
- Locate the DDD — resolve
<workspace>/Projects/<name>/(the DDD dir). - Read the declaration — call the packager's policy; show the human the
declared
targets+visibility+ any warnings (e.g. an unknown target token). - Confirm the subset — ask the human WHICH declared target(s) to emit now, and whether this is an emit-only pass or an external publish. Never propose a target the DDD didn't declare.
- Render — run
scripts/distribute.pywith the confirmed subset. The packager: emits each target tree, runs the content-safety scan over the EMITTED tree, and aborts (fail-closed) on any secret / host-path (any target) or internal-string (external publish). - Return the path(s) — report the built package dir(s) + any scan warnings +
which skills were included (class-B domain) vs excluded (class-A enablement /
unclassified). Point the human at the install command:
- internal → build +
cr/PR into the internal package, thenaim plugins install <Pkg> - external → push to the public code host, then
aim plugins install/bash install.sh
- internal → build +
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.
- today Changed · +10 lines 0b66e4a60667
- 13d ago First seen · 71 lines · 209 tokens per session scan A c4577cad57c7
ddd-distribute is a skill published in the GitHub repository xg-gh-25/SwarmAI (44 stars, last pushed yesterday), licensed MIT. It adds 209 tokens to every session and 1,218 once invoked, about $0.0010 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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