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/jayvee/aigon/aigon-feature-createnpx skills add jayvee/aigon --skill aigon-feature-creategit clone --depth 1 https://github.com/jayvee/aigonWrote 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/jayvee/aigon/aigon-feature-create)<a href="https://agentmods.dev/skills/jayvee/aigon/aigon-feature-create"><img src="https://agentmods.dev/badge/skills/jayvee/aigon/aigon-feature-create.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 | $0.00015 | $0.01827 |
| Opus 5 | $0.00008 | $0.00914 |
| Sonnet 5 | $0.00003 | $0.00365 |
| Haiku 4.5 | $0.00002 | $0.00183 |
Grade A, and why
aigon-feature-create 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 4d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aigon-feature-create
Run this command followed by the feature name.
aigon feature-create $ARGUMENTS
This creates a new feature spec in ./docs/specs/features/01-inbox/.
IMPORTANT: Do not launch or foreground an editor, terminal, or Markdown preview after creation. Continue by reading and editing the spec through the current agent session; use the exact path printed by the CLI.
Step 1: Read Project Conventions
Tip: If you are running this command in your own Claude Code session (no
--agentflag), press Shift+Tab now to enter plan mode before drafting — this keeps your session read-only while you explore the codebase and propose the spec.
Explore the codebase to understand the existing architecture, patterns, and code relevant to this feature. Use that investigation both to plan the approach and to resolve technical facts without asking the user. Consider:
- What existing code will this feature interact with?
- Are there patterns or conventions in the codebase to follow?
- What technical constraints or dependencies exist?
Use this understanding to write a well-informed spec — especially the Technical Approach, Dependencies, and Acceptance Criteria sections.
Step 2: Deepen — interview the user before writing
Follow this procedure literally. Deepen applies to this installed agent command;
the bare aigon feature-create CLI remains a noninteractive scaffolder.
-
Apply the gate. Inspect the raw invocation arguments in the command above. If they contain
--quick, skip the rest of Deepen and continue to Step 3. Otherwise run:aigon config get deepen.enabledSkip the rest of Deepen only when the effective value is
false. Remember whether a value oftruewas reported as(from default)or came from explicit project/global configuration; this controls the final hint below. -
Build a coverage map before asking anything. Read the user's request, the bare-bones spec just created, any available planning context, and the relevant code. If the in-flight spec has
planning_context:, read that plan and use it as the source of recommended answers. Do not re-interview the user about decisions already resolved there. Investigate discoverable technical facts — including existing patterns, dependencies, constraints, and file paths — instead of asking the user for them. -
Check coverage internally in dependency order: Summary → User Stories → Acceptance Criteria → Technical Approach → Dependencies → Out of Scope →
complexity:. This is a decision dependency order, not a requirement to ask one question for every section. Ask only when the answer could materially change scope, acceptance criteria, or approach. -
Ask the highest-leverage unresolved question, one question per message. Every question must challenge a consequential assumption or surface a meaningful alternative; do not ask a template-completion question. Attach a proposed answer on its own clearly labelled line:
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.
- 4d ago First seen · 158 lines · 15 tokens per session scan A eaac285da6f8
aigon-feature-create is a skill published in the GitHub repository jayvee/aigon (24 stars, last pushed 2d ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,827 once invoked, about $0.0001 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 2026-08-30.
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