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budget-optimizer

Cross-platform budget optimizer that reallocates spend based on ROAS across Google, Meta, LinkedIn, X, Reddit, TikTok, Amazon, The Trade Desk, and Amazon DSP. Use for budget reallocation, ROAS optimization, or cross-platform spend efficiency.

Quality

83%

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SecuritybySnyk

Medium

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SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body excels at actionability and workflow safety: fully executable commands, a well-sequenced preview/dry-run/confirm pipeline, and thorough error recovery guidance. It is weakened by token bloat (sample JSON, per-platform trivia, a promotional footer) and by inlining everything in one monolithic file instead of splitting platform notes and schemas into reference files.

Suggestions

Move the nine platform-specific notes, the recommendations JSON schema, and the minimum-budget/environment-variable tables into one-level-deep reference files (e.g. references/platforms.md, references/output-format.md) and link them from a concise overview section.

Delete or drastically shorten the promotional 'About this skill' footer and the 50-line sample recommendations JSON — replace with a few key field annotations to cut padding.

Trim the description's shortfall in the body's favor: since the description names only reallocation, add the analysis/reporting capabilities ('Pull and compare ROAS', 'Generate reallocation recommendations') to the description's action list for full coverage.

DimensionReasoningScore

Conciseness

Command blocks and tables are token-dense, but the ~50-line sample recommendations JSON, nine per-platform notes sections, and the promotional "About this skill" footer ("Try Synter... running on autopilot against your live ad accounts") are unnecessary padding. Not 4 because several sections could clearly be trimmed; not 2 because most of the body is efficient reference material rather than explanation of known concepts.

3 / 5

Actionability

Every workflow step and common operation is a complete, copy-paste-ready command with real flags (e.g. "python3 scripts/analyze_budgets.py --input metrics.json --target-roas 3.0 --output recommendations.json"), backed by a full parameter table and error/solution table. Matches the 'fully executable' anchor.

5 / 5

Workflow Clarity

The four-step workflow (pull → analyze → preview → apply) is clearly sequenced with explicit validation gates for a batch, semi-destructive operation: a dedicated preview step, --dry-run, and --confirm gating, plus an error-handling table with recovery actions ("Retry with backoff", "Check platform-specific error, retry"). Not 4 because checkpoints are explicit and recovery paths are documented, satisfying the feedback-loop requirement.

5 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent), and everything — platform-specific notes for nine platforms, the full output JSON schema, minimum-budget tables, environment variables — is inlined in a single ~340-line file. Clear section headers keep it navigable (not 2), but substantial content that belongs in one-level-deep reference files is inline (not 4).

3 / 5

Total

16

/

20

Passed

Description

82%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: third-person voice, concrete action, explicit 'Use for' triggers, and a distinctive platform-scoped niche. Its only weakness is that it names a single action (reallocation) rather than the fuller set of capabilities the skill body demonstrates.

DimensionReasoningScore

Specificity

"reallocates spend based on ROAS" names the domain and one concrete action, but the description stops there — it omits analysis, recommendation generation, reporting, and applying changes that the skill actually performs. Not 4 because 'lists several specific actions' is not met; not 2 because the action given is concrete, not generic.

3 / 5

Completeness

Explicitly answers "what" ("reallocates spend based on ROAS across Google, Meta, ... Amazon DSP") and "when" ("Use for budget reallocation, ROAS optimization, or cross-platform spend efficiency") with concrete trigger phrases. Not 4 because the when-clause is specific and multi-trigger rather than merely serviceable.

5 / 5

Trigger Term Quality

"budget reallocation", "ROAS optimization", and "cross-platform spend efficiency" are phrases users would naturally say. Not 5 because common variants like "ad spend", "advertising budget", or "improve ROAS" are absent; not 3 because coverage goes beyond a single keyword.

4 / 5

Distinctiveness Conflict Risk

A clear niche — ad-budget reallocation driven by ROAS across nine named ad platforms — with distinct triggers that would not fire for sibling skills (bid optimization, reporting, campaign launching). Not 4 because overlap risk is minimal, not minor-but-present.

5 / 5

Total

17

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 9 missing

Warning

Total

15

/

16

Passed

Repository

Synter-Media-AI/free-skills

Path

skills/budget-optimizer/SKILL.md

Reviewed

Table of Contents

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