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/fix/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/fix)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/fix"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/fix/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/fix"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/fix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.01213 |
| Opus 5.5 | $0.00020 | $0.00485 |
| Sonnet 5.5 | $0.00010 | $0.00243 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
fix 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix (guided intake)
IMPORTANT: Start your response by telling the user:
Fix — Composing an outcome-first fix: goal, scope, and verification probes, then a preview before anything runs.
What It Does
Interactive intake for attune fix (the outcome-first Fix surface,
docs/specs/outcome-first-fix/): one form gathers the goal, the
--scope the diff must stay confined to, and the --probe
commands that verify the fix — with scope and probe options DERIVED
from the working tree (changed paths and matching test files), not
typed from memory. The composed CLI command is previewed before any
execution; the receipt independently verifies every probe.
Relationship to /fix-test: that skill diagnoses and fixes a
FAILING TEST in-session. This skill drives the attune fix CLI
contract — goal + scope + probes + receipt — for any code fix.
Neither replaces the other.
Step 1 — Derive candidates and build the form
python -m attune.elicitation.fix_intake
The JSON payload contains a validated form definition
(attune.elicitation.fix_intake.build_fix_intake_form) plus the
derived scopes and probes lists. Changed paths lead; on a clean
tree the candidates fall back to recently-touched directories from
git history, so pickers render in either state — empty lists mean
the repo has no usable history at all. If the user's invocation
already stated the goal, carry it into the request field as the
default rather than asking again.
Step 2 — Render the form (communication grammar)
Render ONE form — request, scope, probes. The enhanced
widget is the DEFAULT surface: build the FormSchema from the
Step 1 payload, route it through select_form_surface, and when
it returns "widget" render form_to_widget_html(form) on the
widget surface — answers post back as an
__elicitation_response__ payload; parse them with
collect_form_response. Carry an already-stated goal into the
request field as its default.
Fall back to AskUserQuestion ONLY when no widget surface exists
(batch the questions; metadata.source containing "form" opts
into the batch). Never ask these as sequential single questions —
and never hand-write the ask turn without consulting
select_form_surface first: steering that names the fallback
concretely gets the fallback executed. When a field came back with
no derived options it is free text — accept a path or command, do
not invent options.
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 · 120 lines · 51 tokens per session scan A b052d455125a
fix is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 1,213 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.
Other skills, from other repositories
ripple
Analyzing pre-change impact across vertical (dependency chains, files) and horizontal (pattern consistency, naming) dimensions. Use to estimate blast radius before a refactor. No code.
sweep
Detecting unnecessary files, unused code, and orphaned files, and proposing safe deletion. Not for removal execution (Builder), repo structure (Grove), or scope cutting (Void).
bolt
Optimizing frontend (re-render, memoization, lazy loading) and backend (N+1, indexing, caching, async) performance, plus continuous auto-tuning loops for GC/threadpool/cache/worker settings.
lens
Comprehending and investigating codebases: structure mapping, feature discovery, data flow tracing for 'does X exist?' or 'how does Y work?'. Includes a conversational ask mode. Does not write code.
scout
Investigating bugs via root cause analysis, reproduction steps, and impact assessment. Investigation-only — finds why bugs occur and where to fix them, no code. Use when a bug needs RCA before a fix.
trail
Investigating git history, analyzing regression root causes, and performing code archaeology. Time-travels through commits to uncover truth. Use for git history investigation.