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 AkshitIreddy/agent-skills --skill ambitious-implementationgit clone --depth 1 https://github.com/AkshitIreddy/agent-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/akshitireddy/agent-skills/ambitious-implementation)<a href="https://agentmods.dev/skills/akshitireddy/agent-skills/ambitious-implementation"><img src="https://agentmods.dev/badge/skills/akshitireddy/agent-skills/ambitious-implementation.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 | $0.00060 | $0.00870 |
| Opus 5 | $0.00030 | $0.00435 |
| Sonnet 5 | $0.00012 | $0.00174 |
| Haiku 4.5 | $0.00006 | $0.00087 |
Grade A, and why
ambitious-implementation 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write the ambitious version
The default failure mode in craft work is under-building: one gradient where a painting needs twelve, three leaves where a vine needs forty, a linear tween where motion needs a spring with follow-through. The result compiles, matches the description, and looks cheap.
Length is not a cost here. Thinness is. For UI, animation, art and sound, a 2,000-line module that layers detail is usually more correct than a 200-line one that gestures at the idea. Do not ration effort to look efficient.
What ambitious means concretely
Layers, not a layer. Real visual richness comes from stacking many low-opacity passes: base tone → large-scale variation → mid detail → fine grain → directional light → contact shadow → ambient occlusion → edge highlight → colour grade. Each pass may be a few lines; the beauty is in having all of them.
Variation everywhere. Anything repeated (leaves, books, bricks, particles, notes) needs per-instance variation in size, rotation, hue, opacity, spacing, and shape — ideally from a seeded PRNG so it is deterministic yet never uniform. Uniformity is the loudest signal of machine-made art.
Hierarchy of scale. Include large, medium, and small elements. Art that reads as amateur is usually missing one tier — all-medium leaves, all-same-width books, all-same-duration animations.
Push the parameter past comfortable. Thin lines, tiny details and subtle effects photograph as "nothing there". When unsure, make it bigger, bolder, more contrasty — then dial back after looking.
Specify, do not abstract. Twelve hand-authored theme definitions beat one parameterised theme with a colour input. Bespoke beats generic in craft work; save the abstraction for the plumbing.
Sound
Same rule: layer. A convincing sound is 3–6 stacked elements (body, transient, texture, air, tail), each shaped by its own envelope and filter, then bussed through gentle compression, EQ and a short reverb. Give every one-shot several variants and randomise selection, pitch and level per play, or repetition becomes fatiguing. Prefer longer, softer envelopes for calm; harsh, clicky results usually mean missing fades, no lowpass, or peaks left unmastered.
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 · 49 lines · 60 tokens per session scan A fbdc071b1c22
ambitious-implementation is a skill published in the GitHub repository AkshitIreddy/agent-skills (1 stars, last pushed 9d ago), licensed MIT. It adds 60 tokens to every session and 870 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
lov-professional-portrait
Turn a single-person photo into a polished professional portrait while preserving the person's identity. Use restrained skin retouching, exposure cleanup, optional hat removal and hairstyle reconstruction, background polish, and an explicit quality gate. Trigger when the user asks for a professional headshot, business…
lov-find-logo
A tool for collecting company or product logos from public web sources such as a website’s icon or social-preview image.
lov-business-card
Generate a professional, editorial-style business card (2:1) as a high-resolution PNG and a self-contained interactive HTML. Collects the user's identity (name, roles, tagline, interests, locations, optional avatar), fills a parameterized design template with a chosen theme, and renders it via headless Chrome. Trigger…
lov-visual-clone
Analyze a reference design image and extract visual DNA — layout, style, color palette, texture, typography, copy tone, spacing, etc. — into a structured, reusable replication prompt that can be applied to new scenarios. Trigger when: user provides a reference image and asks to "extract style", "replicate this"…
lov-riso-portrait
An image-editing workflow that redraws a single-person photo as a recognisable Risograph-style portrait. Risograph is a print style using limited ink colours, visible dots, paper grain, and slight colour misalignment.
lov-cover-package
A visual package generator for WeChat public-account articles. It creates a wide sharing cover and a separate 4:3 opening image, then adds the official publisher logo and checks their dimensions and composition.