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 agentmods add skills/fridrichmethod/awesome-skills/magenpx skills add FridrichMethod/awesome-skills --skill magegit clone --depth 1 https://github.com/FridrichMethod/awesome-skillsWrote 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/fridrichmethod/awesome-skills/mage)<a href="https://agentmods.dev/skills/fridrichmethod/awesome-skills/mage"><img src="https://agentmods.dev/badge/skills/fridrichmethod/awesome-skills/mage.svg" alt="Measured on agentmods" 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 | $0.00008 | $0.00425 |
| Opus 5 | $0.00004 | $0.00212 |
| Sonnet 5 | $0.00002 | $0.00085 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
mage-antibody-generator 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 2d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
36 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.
- README.md 1.6 KB
- repo/Data cleaning/ASAb_database_metadata_v2_24-03-12.xlsx 31 KB
- repo/Data cleaning/CoVAbDab/covabdab_gen_curation_allBinders_allVariants_v2_24-03-12.ipynb 41 KB
- repo/Data cleaning/CoVAbDab/NTD_SA.npy 550 KB
- repo/Data cleaning/CoVAbDab/RBD_SA.npy 330 KB
- repo/Data cleaning/crowe_ebola/crowe_ebola_processing_24-03-07.ipynb 7.1 KB
- repo/Data cleaning/denovo_absci/denovo_trastuzamab_24-02-29.ipynb 7.8 KB
- repo/Data cleaning/final_processing/detagging_script_24-03-04.ipynb 191 KB
- repo/Data cleaning/final_processing/final_concat_v2_24-03-12.ipynb 6.4 KB
- repo/Data cleaning/flu_LLM_paper/wang_preprint_flu_cleaning_24-01-03.ipynb 39 KB
- repo/Data cleaning/Other_notebooks/10618/10618_antigen_alignment_v1_24-03-06.ipynb 27 KB
- repo/Data cleaning/Other_notebooks/10618/AA_10618_LLM-clean_24-03-06.ipynb 36 KB
- repo/Data cleaning/Other_notebooks/atlas_antigen_alignment_v1_24-03-04.ipynb 11 KB
- repo/Data cleaning/Other_notebooks/published_LIBRA-seq_concatenation_24-05-31.ipynb 23 KB
- repo/Data cleaning/Other_notebooks/SAbDab/detagging_script_24-03-04.ipynb 233 KB
- repo/Data cleaning/Other_notebooks/SAbDab/SAbDab_final_clean_v1_24-03-04.ipynb 17 KB
- repo/Data cleaning/Other_notebooks/signalP/detagging_nonSAbDab_24-03-12.ipynb 26 KB
- repo/Data cleaning/Other_notebooks/signalP/signalP_summary_output_24-03-12.txt 6.2 KB
- repo/Data cleaning/PlAbDab/ebola_Bornholdt_24-03-07.ipynb 7.2 KB
- repo/Data cleaning/PlAbDab/ebola_EhrHardt_24-01-03.ipynb 66 KB
- repo/Data cleaning/PlAbDab/gilman_RSV_24-01-04.ipynb 8.9 KB
- repo/Data cleaning/PlAbDab/hcv_24-01-03.ipynb 3.2 KB
- repo/Data cleaning/zurbuchen/zurbuchen_data_23-03-08.ipynb 56 KB
- repo/Fine_tuning/example_training_data.csv 741 KB
- repo/Fine_tuning/full_model_training_24-03-12.py 8.0 KB runs code
- repo/Fine_tuning/selected_abs_Figure_2-3_24-06-10.ipynb 212 KB
- repo/generate_antibodies.py 2.9 KB runs code
- repo/LICENSE 11 KB
- repo/MAGE_annotated_training_data.zip 2184 KB
- repo/Output_analysis/figure1_plots_24-06-10.ipynb 324 KB
- repo/Output_analysis/output_filtering_annotation.ipynb 15 KB
- repo/Output_analysis/RBD_1K_sequences_24-03-16.csv 1589 KB
- repo/Output_analysis/selected_abs_Figure_2-3_24-06-10.ipynb 212 KB
- repo/Output_analysis/selected_abs_for_testing.csv 27 KB
- repo/Output_analysis/selection_pipeline_24-03-16.ipynb 11 KB
- repo/README.md 3.4 KB
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.
- 2d ago First seen · 55 lines · 8 tokens per session scan A 8d91ac949a8c
mage-antibody-generator is a skill published in the GitHub repository FridrichMethod/awesome-skills (14 stars, last pushed 6d ago), with no licence file. It adds 8 tokens to every session and 425 once invoked, about $0.0000 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 2026-09-03.
Other skills, from other repositories
cell-free-protein-expression
Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
binding-characterization
Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
cell-free-expression
Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).
spr-bli-binding-characterization
SPR and BLI assay planning, kinetic interpretation, and troubleshooting guidance. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
chai1-structure-prediction
Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC…
alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction…