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goal-review

goal-review is a skill for Claude Code from logly/mureo. It costs 77 tokens per session (3,303 once invoked), scanned A, original, Apache-2.0.

A review process for checking progress against targets written in STRATEGY.md, a file that describes a project's goals and plan.

In plain words
What is it for?
Use it to review marketing goals, compare current results with targets, and calculate the pace needed to meet deadlines.
Why use it?
It helps show whether goals are on track, at risk, or off track instead of relying on guesses or outdated information.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Claude Code.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is PREREQUISITE: Read `../_mureo-shared/SKILL.md` for auth, security rules, output format, and **Tool Selection** (Read/Write on Code, `mureo_strategy_*` / `mureo_.

Part of the mureo plugin — 27 skills, 1 MCP server shipped together

not rated 43repo changed 4d ago A scan SkillSpector: warn 77 tokens original Apache-2.0

Good fit Use it to review marketing goals, compare current results with targets, and calculate the pace needed to meet deadlines.

Compare 6 skills from other repositories ↓
View source ↗ logly/mureo mureo.io
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/logly/mureo
agentmods
npx agentmods add skills/logly/mureo/goal-review

Made for: Claude Code.

Or install mureo, the plugin that ships this one along with the rest of its 27 skills, 1 MCP server.

Wrote 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.

agentmods badge for goal-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/logly/mureo/goal-review.svg)](https://agentmods.dev/skills/logly/mureo/goal-review)
Your own site
<a href="https://agentmods.dev/skills/logly/mureo/goal-review"><img src="https://agentmods.dev/badge/skills/logly/mureo/goal-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,303 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 55
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.03303
Opus 5 $0.00039 $0.01651
Sonnet 5 $0.00015 $0.00661
Haiku 4.5 $0.00008 $0.00330

Measured 4d ago against content hash 177987df94ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

goal-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 4d 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.

mureo/_data/skills/goal-review/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Goal Review

PREREQUISITE: Read ../_mureo-shared/SKILL.md for auth, security rules, output format, and Tool Selection (Read/Write on Code, mureo_strategy_* / mureo_state_* MCP on Desktop / Cowork).

Review progress toward all marketing goals across all platforms.

Prerequisites

  • STRATEGY.md with at least one Goal section (run the onboard skill first)
  • STATE.json (run /sync-state first)

Steps

Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and consult advisors (mureo_consult_advisor) before drawing conclusions.

  1. Establish today: call mureo_state_get first, on every host (including Claude Code, where you would otherwise Read the file) and take server_now from its response — ISO 8601 with UTC offset, e.g. 2026-07-28T10:12:33+09:00. Its date is the only source of the current date for this run: every "days remaining until deadline" and required-pace calculation in step 4 is measured from it, so a stale date silently flips on-track / at-risk / off-track. Do not shell out (this skill must run in Bash-less headless hosts) and do not read the date off STATE.json — last_synced_at, reports.*.period and action_log timestamps are history, never evidence of what day it is now. Never write server_now into STATE.json: it is a response field, and a persisted copy becomes tomorrow's stale "today".

  2. Load context: Read STRATEGY.md (all Goal sections, Data Sources) and STATE.json (the same mureo_state_get response from step 0 on MCP hosts).

  3. Discover platforms: Identify all configured platforms and available data sources. Also include any hosted official-MCP connector present in the session (e.g. TikTok, key tiktok_ads) — drive it via its own tools and skip mureo-only value-adds; see ../_mureo-shared/SKILL.md → Hosted-connector platforms.

  4. For each Goal, gather current metrics from all relevant platforms and data sources:

    • Ad platforms: Use performance reporting tools for each platform
    • GA4 (if available): Website conversion data, user behavior metrics
    • Search Console (if available): Organic search metrics for SEO-related goals
    • CRM (if available): Lead quality, pipeline data for LTV-related goals

Read the full file on GitHub · 100 lines

Changes

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.

  1. 4d ago Changed 177987df94ca
  2. 8d ago First seen · 100 lines · 77 tokens per session scan A db3447317cb6

Subscribe to this mod's changes

goal-review is a skill published in the GitHub repository logly/mureo (43 stars, last pushed 5d ago), licensed Apache-2.0. It adds 77 tokens to every session and 3,303 once invoked, about $0.0004 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.

Analytics and metricsPlanningadvertisingai-agentscligoogle-adsmarketingmcp
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