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/cobusgreyling/loop-engineering/minimal-fixnpx skills add cobusgreyling/loop-engineering --skill minimal-fixgit clone --depth 1 https://github.com/cobusgreyling/loop-engineeringWhat 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 | $0.00043 | $0.00318 |
| Opus 5 | $0.00022 | $0.00159 |
| Sonnet 5 | $0.00009 | $0.00064 |
| Haiku 4.5 | $0.00004 | $0.00032 |
Grade A, and why
minimal-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 3d 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.
What it actually says
Minimal Fix Skill
You fix one specific problem with the smallest diff that could work.
Inputs
- Exact failure message, reviewer comment, or issue description
- File(s) implicated (if known)
- Project build/test commands (from AGENTS.md or project skills)
- Path denylist (from loop safety policy — never edit
.env,auth/,payments/, secrets)
Process
- Reproduce or confirm the failure locally if possible.
- Identify the minimal root cause — not symptoms in distant files.
- Change only what is required. No drive-by refactors.
- Run tests/lint relevant to the change.
- Summarize: what changed, why, what you ran.
Output
## Minimal Fix Proposal
### Target
(one sentence)
### Diff summary
(files + what changed)
### Verification run
(command + result)
### Risks / human review needed?
(yes/no + why)
Rules
- One problem per invocation. Multiple failures → escalate or triage first.
- Respect denylist paths — escalate instead of editing.
- Prefer worktree isolation when the loop runs unattended.
- Do not mark your own work done — the verifier decides.
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.
- 3d ago First seen · 52 lines · 43 tokens per session scan A 85e2ac0502e6
minimal-fix is a skill published in the GitHub repository cobusgreyling/loop-engineering (10,811 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 318 once invoked, about $0.0002 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.
Other skills, from other repositories
aw-author
Author, validate, and improve GitHub Agentic Workflow (gh-aw) markdown files. Use when the user wants to create a new workflow, validate an existing workflow, improve a workflow, or debug workflow issues. Triggers on: "aw-author", "agentic workflow", "gh-aw workflow", "workflow markdown", "workflow frontmatter"…
aw-daily
Fully autonomous daily pipeline for the aw-author plugin. Executes intelligence research (web search + GitHub activity queries), posts to Discussions, performs gap analysis against reference files, creates issues, implements changes on develop branch, creates PR, requests review, and auto-merges. Designed for…
gh-aw-report
Daily intelligence reporting for the GitHub Agentic Workflows (gh-aw) ecosystem. Executes 8+ targeted web searches, synthesizes findings into a structured Markdown report, updates the persistent knowledge base, and optionally posts to GitHub Discussions. Triggers on: "aw-report", "gh-aw report", "intelligence sweep"…
hive-maintainer
Discipline for developing Hive itself — PR sizing, review handling, merge discipline, release workflow, delegation, and cleanup hygiene. Use this skill when working on Hive repo changes that span multiple PRs or review cycles.
hive-essentials
Hive mental model and orientation. Read this first before using any other Hive skill. Covers the entity hierarchy, observe-and-steer pattern, drivers, sandboxes, console vs CLI, and workspace conventions.
hive-work-loop
The core agent work cycle in Hive — from finding a task through claiming, launching a run, handling approvals, finishing, and promoting. Use this skill for task-first project work, governed runs, and clean handoff.