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 0xhughs/director-skills --skill model-adaptationgit clone --depth 1 https://github.com/0xhughs/director-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/0xhughs/director-skills/model-adaptation)<a href="https://agentmods.dev/skills/0xhughs/director-skills/model-adaptation"><img src="https://agentmods.dev/badge/skills/0xhughs/director-skills/model-adaptation/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/0xhughs/director-skills/model-adaptation"><img src="https://agentmods.dev/badge/skills/0xhughs/director-skills/model-adaptation.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 | $0.00088 | $0.00854 |
| Opus 5.5 | $0.00035 | $0.00342 |
| Sonnet 5.5 | $0.00018 | $0.00171 |
| Haiku 4.5 | $0.00009 | $0.00085 |
Grade A, and why
model-adaptation 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 2mo 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
model-adaptation
When to use
- The user names a specific image or video model/tool.
- The user asks to convert a prompt from one model to another.
- The user asks why a prompt works in one model but fails in another.
- The user wants best settings, a model-agnostic package, or exports for multiple tools.
- The prompt uses model-dependent features: negative prompts, reference images, seeds, duration, aspect ratio, resolution, camera control, audio, or text rendering.
When not to use
- The user only wants story ideation with no target tool.
- The user asks for unsafe policy bypasses.
- The user wants live, up-to-date official specs and source verification; in that case browse or check current docs before asserting changed capabilities.
Required inputs
- Universal prompt brief or existing prompt
- Target model(s)
- Modality: image, T2V, I2V, editing, extension, upscaling, etc.
- Desired aspect ratio/duration/resolution if relevant
Optional inputs
- Reference assets and their roles
- Negative constraints
- Seed/settings
- Known failure from previous output
- Current docs supplied by user
Workflow
- Parse the universal brief into creative intent, cinematic execution, and model-dependent controls.
- Look up the relevant model profile and capability matrix. If a capability is not in the references, mark it unknown rather than guessing.
- Choose prompt structure: natural prose, slot template, time-blocked prompt, shot list, or parameterized export.
- Translate negatives correctly: separate field where supported, positive restatement where unsupported, capped constraints for video.
- Map reference assets to supported syntax and roles. If unsupported, describe a workaround or mark unknown.
- Map settings: aspect ratio, duration, resolution, seed/reference controls, audio, and text/logo constraints.
- Produce a translation report noting preserved intent, changed syntax, unsupported fields, unknowns, and likely failure modes.
- For multi-model exports, keep one universal brief plus per-model prompts/settings.
What ships with it
22 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.
- checklists/model_export_checklist.md 333 B
- examples/failed_model_transfer_diagnosis.md 418 B
- examples/image_prompt_translation.md 646 B
- examples/same_scene_across_models.md 559 B
- examples/universal_prompt_to_model_specific_exports.md 1.1 KB
- examples/video_prompt_translation.md 627 B
- references/failure_modes_by_model.md 1.2 KB
- references/image_model_profiles.md 14 KB
- references/model_capability_matrix.md 4.0 KB
- references/model_update_protocol.md 697 B
- references/parameter_compatibility.md 553 B
- references/prompt_translation_rules.md 1004 B
- references/video_model_profiles.md 15 KB
- templates/model_comparison_table.md 152 B
- templates/model_specific_prompt_export.md 321 B
- templates/multi_model_export_package.md 208 B
- templates/prompt_translation_report.md 281 B
- templates/universal_prompt_brief.md 446 B
- tests/model_routing_tests.md 423 B
- tests/prompt_translation_tests.md 322 B
- tests/research_provenance_tests.md 355 B
- tests/unknown_capability_tests.md 220 B
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.
- 2mo ago First seen · 83 lines · 88 tokens per session scan A 98fba9bef326
model-adaptation is a skill published in the GitHub repository 0xhughs/director-skills (18 stars, last pushed 5mo ago), licensed MIT. It adds 88 tokens to every session and 854 once invoked, about $0.0004 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 2026-08-31.
model-adaptation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| model-adaptation0xhughs/director-skills | Skill | 18 | 5mo ago | MIT | A | 88 | Claude Code, Codex |
| ai-engineering-toolkitsickn33/agentic-awesome-skills | Skill | 49k | today | MIT | A | 47 | Claude Code |
| prompt-engineering-outlinesdavila7/claude-code-templates | Skill | 33k | today | MIT | B | 53 | Claude Code, Codex |
| prompt-masternidhinjs/prompt-master | Skill | 16k | 15d ago | MIT | B | 78 | Claude Code, Codex |
| create-simple-promptpnp/copilot-prompts | Skill | 902 | 3d ago | MIT | A | 80 | Claude Code, Codex |
| higgsfield-seedanceOSideMedia/higgsfield-ai-prompt-skill | Skill | 863 | 13d ago | MIT | A | 100 | Claude Code |
| higgsfieldOSideMedia/higgsfield-ai-prompt-skill | Skill | 863 | 13d ago | MIT | A | 170 | Claude Code |