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.
git clone --depth 1 https://github.com/sefaertunc/WorclaudeWrote 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/commands/sefaertunc/worclaude/review-plan)<a href="https://agentmods.dev/commands/sefaertunc/worclaude/review-plan"><img src="https://agentmods.dev/badge/commands/sefaertunc/worclaude/review-plan.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.00663 |
| Opus 5 | $0.00010 | $0.00331 |
| Sonnet 5 | $0.00004 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
review-plan 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 8d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Send an implementation plan to the plan-reviewer agent for staff-level
review. The plan-reviewer acts as a staff engineer and critically
reviews the plan for ambiguity, missing verification steps, unrealistic
scope, edge cases, and SPEC.md alignment.
1. Auto-detect the plan
Read .claude/plans/ and list every file inside (excluding .gitkeep
and README.md). Behavior depends on what's present:
-
Zero plan files: Refuse to dispatch. Report: "No plan files found in
.claude/plans/. Drop your plan there (any filename) and re-run/review-plan." Do not invoke the agent. -
Exactly one plan file: Use it directly without asking.
-
Multiple plan files: Use
AskUserQuestion(2-4 options) to let the user pick which one to review. For >4 candidates, present a numbered list and ask for a number reply.
Do NOT match filename patterns. The folder convention is the discovery
mechanism — anything in .claude/plans/ is a candidate.
2. Auto-load project context
Before dispatching, read these files and pass their contents to the agent so it can check SPEC alignment without a second turn:
CLAUDE.md— project conventions and critical rulesdocs/spec/SPEC.md— the source of truth for what should ship
Include both in the agent's prompt under clearly-labeled sections.
3. Dispatch to plan-reviewer
Agent({
subagent_type: "plan-reviewer",
description: "Staff-level review of <plan-file>",
prompt: "Review the implementation plan below as a senior staff engineer.
PLAN (from .claude/plans/<filename>):
<plan content>
PROJECT CONVENTIONS (from CLAUDE.md):
<CLAUDE.md content>
SPECIFICATION (from docs/spec/SPEC.md):
<SPEC.md content>
Check for: ambiguity, missing verification steps, unrealistic scope,
edge cases, SPEC alignment. Report findings in priority order with
specific file/line references where possible."
})
4. Persist the review output
When the agent returns, write its findings to
.claude/scratch/last-plan-review.md with SHA frontmatter so the
next session can surface the review via /start:
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.
- 8d ago First seen · 93 lines · 20 tokens per session scan A dae954426e62
review-plan is a command published in the GitHub repository sefaertunc/Worclaude (4 stars, last pushed 29d ago), licensed MIT. It adds 20 tokens to every session and 663 once invoked, about $0.0001 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 commands, from other repositories
pull-repos
Pull all repos (parent + marketplace clones + configured project repos).
update-counts
Update all hardcoded counts (skills, agents, marketplace repos/skills) across documentation from filesystem.
pr-description
Generate a PR title and description from the current branch diff against main.
analyze
Force analysis of the most recent run. Reads the latest log, runs the full 5-phase protocol.
runs
Browse run history. Show the comparison table, trends, best/worst runs.
context-budget
Monitor context window usage and optimize token consumption.