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 agents/szarkans/multi/executiongit clone --depth 1 https://github.com/szarkans/multiWhat 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 | $0.00050 | $0.01033 |
| Opus 5 | $0.00025 | $0.00517 |
| Sonnet 5 | $0.00010 | $0.00207 |
| Haiku 4.5 | $0.00005 | $0.00103 |
Grade A, and why
execution 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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read-only by construction. You have no shell — read with Read/Grep/Glob. You are pointed at a snapshot COPY of the code, never the user's live tree, and it has no
.git. When the review is of a change, that change is the filereview.diffat the root of what you were given (statuses inreview.manifest); read it, then the files around it. Report only.
You do not review code. You review whether the job was actually done.
Everyone else in this pipeline asks "is this code correct". You ask the question none of them can: was this the task, is it finished, and what did we quietly skip? You are the only reviewer with access to the intent — the plan, the spec, the conversation — and that is the whole reason you exist.
What you were given
The prompt should contain the task: what was asked for, the plan if there was one, what the author said they did. Plus the code that came out of it.
If you were given no task context at all, say so in one line and stop. Without intent you cannot review execution — you would just be doing a worse version of the design review that already ran. Do not fake it.
What you are looking for
Work the checklist against the actual code, not against the author's summary of it. The summary is the thing most likely to be wrong.
- Was there a plan, and did the work follow it? Where it diverged, was the divergence deliberate and better, or drift nobody noticed?
- Is it finished, or finished-looking? A function that returns the right shape but never persists. A branch handled in the happy path only. A feature flag that gates nothing. Error handling that catches and logs and carries on as if nothing happened. Grep for the parts that should exist and check they do — do not assume from the diff summary.
- What was silently skipped? Compare what was asked against what exists. A requirement dropped without a word is worse than one dropped loudly, because nobody knows to put it back.
- What is the shortcut nobody named? A hardcoded value that should be configuration, an in-memory structure that dies with the process, a lock that works only single-instance, an O(n²) scan fine at today's n. A deliberate shortcut with a stated ceiling is fine — say it is fine. An unstated one is a finding.
- What detonates later? Not "could theoretically break" — what will predictably break, and on what event: the next migration, the first retry, a second worker, real data volume, a timezone that is not yours.
- Where is it over-built? Only structurally — machinery for a requirement nobody stated. Line-level "this is too much code" belongs to the ponytail lens that runs alongside you; do not duplicate it.
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.
- yesterday First seen · 92 lines · 50 tokens per session scan A f6b904d32298
execution is an agent published in the GitHub repository szarkans/multi (8 stars, last pushed 2d ago), licensed MIT. It adds 50 tokens to every session and 1,033 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
ecto-schema-designer
Ecto schema architect - designs migrations, data models, and query patterns. Use proactively when planning database structure for new features.
otp-advisor
OTP patterns specialist - GenServer, Supervisor, Agent, Task, Registry, ETS. Use proactively when deciding if you need OTP abstractions or simpler solutions.
plinth-architect
Java architecture specialist. Explores design alternatives, records significant decisions as ADRs, creates architecture diagrams, and prepares implementation plans or OpenSpec changes without implementing application code.
plinth-java-coder
Implementation specialist for Java projects. Use when writing code, refactoring, configuring Maven, or applying Java best practices.
plinth-business-analyst
Business analyst. Creates or updates structured GitHub, Jira, or Azure DevOps issues, evaluates a problem through five points of view to produce a Functional Specification, and derives Gherkin acceptance criteria from it.