Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/author-feature/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/author-feature)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/author-feature"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/author-feature/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/smart-ai-memory/attune-ai/author-feature"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/author-feature.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 Rogue Agent · line 87 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00052 | $0.02180 |
| Opus 5.5 | $0.00021 | $0.00872 |
| Sonnet 5.5 | $0.00010 | $0.00436 |
| Haiku 4.5 | $0.00005 | $0.00218 |
Grade A, and why
author-feature 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 1mo 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Author-feature — single-source authoring, verified
IMPORTANT: Start your response by telling the user:
Author-feature — I'll author a single-source master for this feature, grounding every claim in live code and running the gates as I go, then project it to the help kinds + docs pages. No LLM generation, no API credits.
This skill makes the driving session a disciplined author of a
single-source master (content/features/<feature>.md). One master
projects deterministically to 10 .help kinds + 4 docs/ pages — no
generator, no API. The empirical result that motivates it: the session
is a superior polish layer to an LLM pass and the only one that
catches correctness bugs (PR #1188). The risk it manages: a master is a
single point of failure, so verification is the spine — grep the
symbol before you write it, run the audit before you commit, fix the
master not the output.
This is judgment, not plumbing. It calls the deterministic tools (the in-repo projector, the audits); it never re-implements them and never emits prose for blind rubber-stamping.
The flow
locate/scaffold → author section-by-section (grounded in code)
→ verify continuously → project → preview → commit
Step 1 — locate or scaffold the master
The master lives at content/features/<feature>.md. If it exists,
you're revising — open it. If not, create it by copying a canonical
projected page for structure — e.g. content/features/security-audit.md
— then replacing its content. Do not invent the layout; the projector
expects a fixed section contract (below).
Frontmatter (required):
---
feature: <slug>
summary: <one line>
tags: [<tag>, <tag>]
source_globs:
- src/attune/workflows/<feature>.py
nav:
help: <slug>
mkdocs:
how-to: how-to/<slug>
architecture: architecture/<slug>
reference: reference/<slug>
---
Declare how-to / architecture / reference but not tutorial:
the projector drops tutorial (a guided tutorial resists pure section
projection — it stays hand-authored).
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.
- 1mo ago First seen · 202 lines · 52 tokens per session scan A 7e3ddf3cbf65
author-feature is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 2,180 once invoked, about $0.0002 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.
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