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 raja21068/AutoResearch --skill embodiment-descriptiongit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/raja21068/autoresearch/embodiment-description)<a href="https://agentmods.dev/skills/raja21068/autoresearch/embodiment-description"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/embodiment-description/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/raja21068/autoresearch/embodiment-description"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/embodiment-description.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 | $0.00048 | $0.01500 |
| Opus 5 | $0.00024 | $0.00750 |
| Sonnet 5 | $0.00010 | $0.00300 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
embodiment-description 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 6d 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.
This is a copy
100% identical to embodiment-description — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Embodiment Description
Write detailed embodiments for: $ARGUMENTS
Embodiments describe HOW to make and use the invention -- they are the patent equivalent of experiment sections, but describe the invention rather than evaluating it empirically.
Constants
MIN_EMBODIMENTS = 1— At least one complete embodiment requiredMAX_EMBODIMENTS = 3— Practical limit; more embodiments strengthen enablementEMBODIMENT_STYLE = detailed—detailed(full working example) oroutline(sketch)REFERENCE_NUMERAL_PREFIX = 100— Starting reference numeral for first figure's components
Inputs
patent/INVENTION_DISCLOSURE.md— invention decomposition (core/supporting/optional features)patent/CLAIMS.md— drafted claims that the embodiments must support- User-provided figures (if any) in any directory
patent/figures/numeral_index.mdif it exists (from/figure-description)
Workflow
Step 1: Plan Embodiments
For each claim category (method, system, etc.), plan at least one embodiment:
| Embodiment | Covers Claims | Type | Key Variations |
|---|---|---|---|
| 1 | Claims 1, X | Best mode / preferred | [primary implementation] |
| 2 | Claims 2, 3 | Alternative | [different parameters/materials] |
| 3 | Claims 4, 5 | Additional alternative | [different configuration] |
Step 2: Write Each Embodiment
For each embodiment, write a detailed description following this structure:
Opening paragraph: "In one embodiment, [invention summary with reference to what is being described]."
Component/step-by-step description:
For method embodiments:
- Describe each step in order
- Reference figure numerals: "As shown in FIG. 1, at step 202, the processor 102 receives the input data 104..."
- Include specific parameters, ranges, and conditions
- Describe what happens at each decision point
For system/apparatus embodiments:
- Describe each component
- Reference figure numerals: "Referring to FIG. 1, the system 100 comprises a processor 102, a memory 104, and a communication interface 106..."
- Describe interconnections between components
- Describe operation of the system step-by-step
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.
- 6d ago First seen · 130 lines · 48 tokens per session scan A c1ca836a52ae
embodiment-description is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 1,500 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to embodiment-description, differing in 2 lines, and is treated as a copy.
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Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…