AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 ufy2024/AuC --skill intent-driven-developmentgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/intent-driven-development)<a href="https://agentmods.dev/skills/ufy2024/auc/intent-driven-development"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/intent-driven-development/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/ufy2024/auc/intent-driven-development"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/intent-driven-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 25 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00098 | $0.03510 |
| Opus 5 | $0.00049 | $0.01755 |
| Sonnet 5 | $0.00020 | $0.00702 |
| Haiku 4.5 | $0.00010 | $0.00351 |
Grade A, and why
intent-driven-development 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 7d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- intent-driven-development — 89% identical, 30 lines differ
- intent-driven-development — 89% identical, 30 lines differ
- intent-driven-development — 89% identical, 30 lines differ
- intent-driven-development — 89% identical, 30 lines differ
How it starts
The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent-Driven Development
Produce useful acceptance criteria without turning specification into ceremony. Inspect available context first, expose genuine ambiguity, and choose verification methods that fit the work and its risk.
When to Activate
- User asks to clarify a feature, define acceptance criteria, or de-risk a change before implementation
- Request touches security, authentication, persistent data, migrations, external APIs, or compliance
- User wants to prepare a handoff artifact for another agent or team
- Request is ambiguous enough that the expected outcome is not yet observable or testable
- User explicitly invokes this skill with
/intent-driven-development
Do not activate for trivial edits, straightforward one-line fixes, active debugging sessions, code review requests, or implementation requests whose acceptance conditions are already clear.
How It Works
- Inspect context first — reads the repository, docs, schemas, and test infrastructure for technical facts before asking any question, while treating product/business constraints as something only the user or a product artifact can supply
- Choose depth — selects Quick Capture (3-7 criteria, low/moderate risk) or Full Acceptance Brief (security, data, migration, cross-system changes) based on the risk profile
- Ask minimally — only asks questions whose answers cannot be inferred and that materially change scope or behavior
- Write observable criteria — each AC-NNN describes a starting condition, trigger, expected outcome, prohibited side effect, verification method, and priority; no vague words like "correctly" or "securely" without evidence
- Proceed or hand off — for clear requests with no blocking risks, records criteria and continues; for risky changes, presents blockers and waits for confirmation
- Handle revision — if an AC fails mid-implementation due to architectural constraints, marks it
[revised], updates scope or verification method, increments the revision number, and re-presents only the changed criteria
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.
- 7d ago First seen · 387 lines · 98 tokens per session scan A 16f00f4920bf
intent-driven-development is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 3,510 once invoked, about $0.0005 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
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.