Lody is a shared workspace that connects coding agents running on different computers and lets people view and direct their work from desktop, mobile, web, or a command line. Teams use it to share agent conversations, monitor progress and code changes, and assign work to connected machines while retaining their existing agents and configurations.
Borrowing it
Nothing to install: this file belongs to LodyAI/Lody. 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/LodyAI/Lody/main/.agents/skills/failure-containment-review/SKILL.mdgit clone --depth 1 https://github.com/LodyAI/LodyWrote 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/lodyai/lody/failure-containment-review)<a href="https://agentmods.dev/skills/lodyai/lody/failure-containment-review"><img src="https://agentmods.dev/badge/skills/lodyai/lody/failure-containment-review/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/lodyai/lody/failure-containment-review"><img src="https://agentmods.dev/badge/skills/lodyai/lody/failure-containment-review.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00051 | $0.02004 |
| Opus 5.5 | $0.00020 | $0.00802 |
| Sonnet 5.5 | $0.00010 | $0.00401 |
| Haiku 4.5 | $0.00005 | $0.00200 |
Grade A, and why
failure-containment-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 15d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Failure-containment review
Find changes that make one owner stricter at the cost of everything around it.
The rules are in failure containment; this
skill is the procedure. Repository review rules still decide what gets reported:
read the root AGENTS.md and
.github/codex-review.md first.
1. Fix the comparison
Record the reviewed revision or working-tree snapshot and the comparison base.
Use the requested base; these examples assume origin/main. Read the base version
of each cited file. A path already strict on the base is not a new regression.
For a PR, resolve its head and merge base:
gh pr view <PR> --json headRefOid,baseRefName,files
git fetch origin pull/<PR>/head:pr-<PR>
REVIEW_HEAD=$(git rev-parse pr-<PR>)
BASE=$(git merge-base "$REVIEW_HEAD" origin/main)
Without a PR, use the local branch and git merge-base HEAD origin/main (or the
requested base). For uncommitted work, record git status --short and inspect
staged, unstaged and relevant untracked files. git diff "$BASE" -- covers tracked
working-tree changes; read new files separately without staging them. Cite local
paths/lines as a working-tree snapshot, not as a commit that lacks those changes.
If the base is unavailable, state the limit rather than inventing a comparison.
2. Collect candidates
Search the diff, not the whole tree, for changed failure semantics:
git diff "$BASE" "$REVIEW_HEAD" -U0 -- 'apps/*/src' 'packages/*/src' | rg -n \
-e '^-.*\.catch\(\(\) =>' -e '^-.*catch \{' -e 'allSettled' -e 'AggregateError' \
-e 'retain|fence|retir|failure[s]?\b' -e 'Promise\.reject\(' \
-e 'dispose|destroy|release|close|unload|teardown' \
-e 'timeoutMs|syncRoom|waitFor|handoff|confirm|receipt'
For working-tree review omit "$REVIEW_HEAD", include the task's actual paths
(including guidance/skills when those are the change), and inspect untracked files.
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.
- 15d ago First seen · 173 lines · 51 tokens per session scan A 339b7b9c110c
failure-containment-review is a skill published in the GitHub repository LodyAI/Lody (1,640 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 2,004 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 2026-10-18.
Other skills, from other repositories
coordinate-review
Review a Coordinate Agents implementation as the Codex Reviewer. Verify the real commit, diff, tests, validation evidence, and specification without modifying the Implementer's product code.
fabric-spec
Starts a persistent Pi Fabric spec supervisor that audits the main session against a feature design spec and steers only when a requirement lacks verified evidence. Use for strict, unblocked spec compliance while the main agent keeps full freedom to orchestrate.
karpathy-guidelines
A set of coding guidelines based on observations about common mistakes made by language models. It emphasizes simple solutions, small targeted edits, clear assumptions, and checkable results.
fabric-advisor
Starts a persistent Pi Fabric peer advisor that reviews the main agent at decision points and surfaces only concrete, material advice. Use for ambient correctness review without another extension.
bmad-code-review
Review code changes with several independent reviewers in parallel, then triage and present the findings. Use when the user says "run code review" or "review this code".
bmad-walkthrough
Guide a human review of a commit, PR, file, or directory. Use when invoked by name.