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 generative-computing/mellea-skills-compiler --skill mellea-fy-artifactsgit clone --depth 1 https://github.com/generative-computing/mellea-skills-compilerWrote 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/generative-computing/mellea-skills-compiler/mellea-fy-artifacts)<a href="https://agentmods.dev/skills/generative-computing/mellea-skills-compiler/mellea-fy-artifacts"><img src="https://agentmods.dev/badge/skills/generative-computing/mellea-skills-compiler/mellea-fy-artifacts/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/generative-computing/mellea-skills-compiler/mellea-fy-artifacts"><img src="https://agentmods.dev/badge/skills/generative-computing/mellea-skills-compiler/mellea-fy-artifacts.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 | $0.00019 | $0.03086 |
| Opus 5 | $0.00010 | $0.01543 |
| Sonnet 5 | $0.00004 | $0.00617 |
| Haiku 4.5 | $0.00002 | $0.00309 |
Grade A, and why
mellea-fy-artifacts 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 9d 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Melleafy Step 6: Supporting Artifact Generation
Version: 4.1.2 | Prereq: Step 5 complete | Produces: mapping_report.md, melleafy.json, SETUP.md, README.md, SKILL.md (non-.md sources only)
Schema: Output
melleafy.jsonMUST conform toschemas/melleafy.schema.json.
Output path rule (Rule OUT-3): All files produced by Step 6 (
mapping_report.md,melleafy.json,SETUP.md,README.md,SKILL.md) are written inside<package_name>/— NOT at the skill root. Seemellea-fy.md§Output directory layout for the full tree.
Step 6 produces all human-facing documentation. LLM invocations here are narrative-only — structured data (melleafy.json, dependencies.yaml) is generated deterministically from dependency_plan.json. No modification of Python files.
Mapping report (mapping_report.md)
Nine sections in fixed order — non-negotiable (section headers are mechanically consistent so automated tools can locate them):
-
Classification — the five axes from
classification.jsonin reader-friendly order (archetype → shape → tool involvement → source runtime → modality). Includes the one-paragraph LLM-generated classification narrative and the R14 auto-mode recap callout if applicable. -
Decomposition Summary — element counts by tag and category (cross-tab table), aggregate statistics: coverage ratio from Step 1b, total element count, unique source files.
-
Element Mapping — every element with
element_id, source location, tag, category, mapped target file, and target symbol. Grouped by target file for readability. -
Judgment Calls — elements flagged with
llm_judgement_required: truefrom Step 2, plus Step 5 retries withattempts > 1. Each entry includes a LLM-generated one-paragraph explanation referencing the specific tag, category, or recipe involved (generic explanations trigger retry). -
Removed During Audit — table from
element_mapping_amendments.json:removed[]with rationale per row. If no elements were removed, writes "No elements removed during audit."
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.
- 9d ago First seen · 221 lines · 19 tokens per session scan A 07409845f4a8
mellea-fy-artifacts is a skill published in the GitHub repository generative-computing/mellea-skills-compiler (48 stars, last pushed 2d ago), licensed Apache-2.0. It adds 19 tokens to every session and 3,086 once invoked, about $0.0001 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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