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 Mathews-Tom/armory --skill package-optimizergit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/package-optimizer)<a href="https://agentmods.dev/skills/mathews-tom/armory/package-optimizer"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/package-optimizer/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/mathews-tom/armory/package-optimizer"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/package-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 84 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00119 | $0.01177 |
| Opus 5.5 | $0.00048 | $0.00471 |
| Sonnet 5.5 | $0.00024 | $0.00235 |
| Haiku 4.5 | $0.00012 | $0.00118 |
Grade A, and why
package-optimizer 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Package Optimizer
Turn existing package evidence into one bounded, reviewable proposal. The optimizer is proposal-only: it never edits a package, changes a profile, or starts a benchmark run.
Scope
| Situation | Action |
|---|---|
| One package has current evaluator evidence | Propose one disposition. |
| A bounded family shares the same evidence/profile | Propose one family plan with a per-package row. |
| Evidence is missing, stale, structurally invalid, or from another profile | Return inconclusive; name the unresolved package decision and request the smallest recertification needed. |
| User asks to apply a proposal | Require explicit approval, preserve before artifacts, then hand the edit to the normal package workflow. |
| User asks for a broad benchmark before identifying a package decision | Refuse the expansion; identify the package decision first. |
Required inputs
- Package scope — one
type/namepackage or an explicitly bounded family. - Current evidence — static conformance and relevant eval-case result or a recorded behavioral result. Cite exact commands, artifacts, or case IDs.
- Capability profile — model/client/tool-surface identity when behavioral evidence depends on one. Structural-only evidence is labelled as such.
- Decision question — what behavior should be retained, simplified, or strengthened.
Do not infer effectiveness from package length, heading count, or a static
conformance report/authored package audit. Report-v2 static conformance is
PASS or FAIL; its live effectiveness remains NOT_MEASURED.
Evidence validity
Reject evidence when any condition holds:
- package path, version, or evaluator case differs from the proposed scope;
- a behavioral result has no model/client/tool-surface identity;
- a profile is older than a recorded package change or cannot be compared to the requested profile;
- the evidence omits a failure, safety outcome, or relevant evaluator result;
- an M4 recertification report pools model targets or lacks complete cells.
What ships with it
2 files 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 · +2 lines · +9 tokens per session 7067b4eacaff
- 1mo ago First seen · 117 lines · 110 tokens per session scan A b65c61c82966
package-optimizer is a skill published in the GitHub repository Mathews-Tom/armory (332 stars, last pushed today), licensed MIT. It adds 119 tokens to every session and 1,177 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
gh-pr-review
Review GitHub Pull Requests using the gh CLI. Use when a user asks to review a PR, perform code review, check a pull request, or provide feedback on proposed changes. Triggers on phrases like "review PR", "review pull request", "check PR", "code review".
code-review
Provides expert code review guidelines and best practices.
mx-flow
Full development workflow orchestrator: brainstorm → plan → worktree → convergent TDD/review loop → verify → PR. One human gate (spec approval); all other gates auto-proceed. Use when starting a feature or significant change from scratch. Usage: /mx-flow ; /mx-flow status [name]; /mx-flow finish .
mx-team-review
Deep code review of a local git diff or files against this user's own engineering standards (Go, C#), written to a report file that /mx-review-triage consumes. Use before commit/merge when you want the standards-based review, not a quick diff scan. Usage: /mx-team-review [diff-spec] | --repo.
mx-pr
Draft a pull request from the feature spec and git log, then publish to GitHub or GitLab (Bitbucket experimental) — or hand off. Use when a feature branch is ready for PR, standalone or from mx-flow. Usage: /mx-pr [name].
mx-brainstorm
Turn a rough idea into an approved design spec: clarifying questions in one batch, 2-3 distinct approaches with trade-offs, spec (and ADR when a real choice was made) written to /.mx/ / /. Hard gate: no implementation until the user approves the spec. Use when starting any feature or non-trivial fix.