Borrowing it
Nothing to install: this file belongs to PackmindHub/packmind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/PackmindHub/packmind/main/.agents/skills/agent-skill-frontmatter-audit/SKILL.mdgit clone --depth 1 https://github.com/PackmindHub/packmindWrote 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/packmindhub/packmind/agent-skill-frontmatter-audit)<a href="https://agentmods.dev/skills/packmindhub/packmind/agent-skill-frontmatter-audit"><img src="https://agentmods.dev/badge/skills/packmindhub/packmind/agent-skill-frontmatter-audit/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/packmindhub/packmind/agent-skill-frontmatter-audit"><img src="https://agentmods.dev/badge/skills/packmindhub/packmind/agent-skill-frontmatter-audit.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 | $0.00038 | $0.03982 |
| Opus 5.5 | $0.00015 | $0.01593 |
| Sonnet 5.5 | $0.00008 | $0.00796 |
| Haiku 4.5 | $0.00004 | $0.00398 |
Grade A, and why
agent-skill-frontmatter-audit 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 1mo 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Skill Frontmatter Audit
Packmind renders skills (one of the three artefact types along with standards and commands) as a SKILL.md file with YAML frontmatter deployed into each AI coding agent's expected folder (.claude/skills/, .cursor/skills/, .github/skills/, .agents/skills/, .opencode/skills/, etc.).
There is a shared Agent Skills baseline specification that all supporting agents commit to (the "core" fields every agent understands), and then each agent may publish its own documentation extending that baseline with agent-specific "additional properties". Both layers evolve over time: the baseline spec ships new versions, and vendors add, rename, or deprecate extensions.
This skill audits Packmind's rendering against both layers and produces a dated report at the project root so drift can be tracked release over release.
Context
The audit is a four-way comparison per agent:
- Baseline spec — the current Agent Skills specification at https://agentskills.io/specification.md. Defines the set of fields every compliant agent is expected to accept (typically
name,description, and similar core keys). - Upstream agent spec — the agent's own public documentation page. May extend the baseline with agent-specific properties, or may be baseline-only.
- Packmind declared support — the per-agent constants in
packages/types/src/skills/skillAdditionalProperties.ts. - Packmind actual rendering — what each agent's deployer in
packages/coding-agent/src/infra/repositories/{agent}/{Agent}Deployer.tsactually writes into the YAML frontmatter.
The four pairs can disagree silently:
-
Baseline vs. upstream — a vendor page that omits a field the baseline requires is a vendor bug to note, not a Packmind action item.
-
Upstream vs. constants — Packmind is behind (or ahead of) the vendor on agent-specific fields.
-
Constants vs. deployer — internal drift; the "supported" promise in the codebase is a lie.
-
A dead doc URL — the audit can no longer be trusted automatically for that agent.
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.
- 1mo ago First seen · 234 lines · 38 tokens per session scan A 238539f34b6b
agent-skill-frontmatter-audit is a skill published in the GitHub repository PackmindHub/packmind (319 stars, last pushed 4d ago), licensed Apache-2.0. It adds 38 tokens to every session and 3,982 once invoked, about $0.0002 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 2026-09-22.
Other skills, from other repositories
ce-compound
Document a recently solved problem or durable project vocabulary in docs/solutions/ or CONCEPTS.md. Use when capturing a learning after work.
ce-work
Execute a plan or concrete work prompt end-to-end. Use when implementing from docs/plans, a spec path, or a clear build request; use ce-debug for open-ended bugs.
sdd
Execute the Liatrio Spec-Driven Development (SDD) workflow when explicitly invoked by the user. NOTE: this skill is NOT intended to be dynamically loaded or automatically triggered; it should only ever be explicitly called by the user.
loadout-import-workflow
Import another repo's command/skill suite into loadout as a workflow. Use when the user points at an agent framework, plugin, or command pack (e.g. a GitHub repo, a .claude/commands/ directory, a skills pack) and wants its process turned into a loadout [[workflows]] entry — "import this workflow", "turn this plugin…
ce-commit-push-pr
Commit, push, and open a PR. Use when asked to ship/open a PR, or for PR-description-only flows like writing, rewriting, or describing a PR body.
loadout-migrate
Migrate existing global AI agent instructions into loadout. Use when adopting loadout, importing a CLAUDE.md / AGENTS.md / global agent rules into loadout, setting up /.config/loadout/config.toml, or turning prose agent instructions into reusable fragments and stack-targeted profiles.