decision-eval

decision-eval is a skill for Claude Code from avelikiy/great_cto. It costs 39 tokens per session (916 once invoked), scanned A, original, MIT.

A decision-scoring process for comparing two or more proposed architecture options using a weighted table and producing a recommended choice.

In plain words
What is it for?
Use it with an architecture decision record or architecture document that presents multiple alternatives.
Why use it?
Architecture decisions can otherwise depend on vague preferences or incomplete discussion. Scoring makes the trade-offs explicit before implementation begins.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the great-cto plugin — 40 skills, 44 commands, 70 agents shipped together

Good fit Use it with an architecture decision record or architecture document that presents multiple alternatives.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/avelikiy/great_cto/decision-eval
Install

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.

Any agent
npx skills add avelikiy/great_cto --skill decision-eval
Clone the repo
git clone --depth 1 https://github.com/avelikiy/great_cto

Made for: Claude Code.

Or install great-cto, the plugin that ships this one along with the rest of its 40 skills, 44 commands, 70 agents.

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 decision-eval

README.md
[![agentmods](https://agentmods.dev/badge/skills/avelikiy/great_cto/decision-eval/github.svg)](https://agentmods.dev/skills/avelikiy/great_cto/decision-eval)
Your own site
<a href="https://agentmods.dev/skills/avelikiy/great_cto/decision-eval"><img src="https://agentmods.dev/badge/skills/avelikiy/great_cto/decision-eval/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.

agentmods 80×15 button for decision-eval

Your own site · 80×15
<a href="https://agentmods.dev/skills/avelikiy/great_cto/decision-eval"><img src="https://agentmods.dev/badge/skills/avelikiy/great_cto/decision-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 916 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 pass 7 Sept 2026
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 $0.00039 $0.00916
Opus 5 $0.00019 $0.00458
Sonnet 5 $0.00008 $0.00183
Haiku 4.5 $0.00004 $0.00092

Measured 7d ago against content hash bd3053e02e25, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

decision-eval 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 7d 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.

skills/decision-eval/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Decision Eval — automated scoring for architectural alternatives

Invoke after architect proposes 2+ variants, before creating gate:arch.

When to invoke

Invoke this skill when ALL of these are true:

  1. An ADR (docs/adr/ADR-*.md) or ARCH doc (docs/architecture/ARCH-*.md) contains a section with 2 or more named alternatives (look for ## Alternatives Considered, ## Options, or bold-prefixed options like **Option A:**)
  2. The architect has not yet created gate:arch
  3. The user has not said "skip scoring", "no scoring", or "skip decision-eval"
  4. project_size in PROJECT.md is NOT nano

Skip silently (do not even mention) if any condition fails.

How to invoke

Read the most recent ADR or ARCH doc to confirm 2+ variants exist, then spawn the decision-scorer agent with the file path as context:

# Identify target document
TARGET=$(ls -t docs/adr/ADR-*.md 2>/dev/null | head -1)
[ -z "$TARGET" ] && TARGET=$(ls -t docs/architecture/ARCH-*.md 2>/dev/null | head -1)

# Confirm 2+ variants
VARIANT_COUNT=$(grep -cE "^\*\*[A-Za-z]|^### [A-Za-z]|^- \*\*[A-Za-z]" "$TARGET" 2>/dev/null || echo 0)

If VARIANT_COUNT >= 2, dispatch the agent:

Agent: decision-scorer
Context: <TARGET file path>
Task: Score the architectural variants in <TARGET> against .great_cto/PROJECT.md criteria.
      Save output to docs/decisions/.

Output location

The decision-scorer agent saves results to:

docs/decisions/DECISION-<slug>-<YYYYMMDD>.md

After the agent completes, read the output file and surface the recommendation to the architect:

Decision scoring complete:
  Recommended: <variant name> (<score>/5.00)
  Runner-up:   <variant name> (<score>/5.00)
  Full report: docs/decisions/DECISION-<slug>-<YYYYMMDD>.md

Architect: review the scoring rationale before accepting or overriding the recommendation.

Skip conditions

Output nothing and proceed to the next step if:

  • project_size: nano in PROJECT.md
  • Fewer than 2 variants found in the ADR/ARCH doc
  • User message contains "skip scoring" or "skip decision-eval" or "no scoring"
  • The target document is a bug-fix or docs-only ADR (check title for "fix:", "docs:", "chore:")

Read the full file on GitHub · 103 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. 7d ago First seen · 103 lines · 39 tokens per session scan A bd3053e02e25

Subscribe to this mod's changes

decision-eval is a skill published in the GitHub repository avelikiy/great_cto (92 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 916 once invoked, about $0.0002 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 2026-09-03.

Related

Other skills, from other repositories

spec-drift

Standalone plan-vs-code audit on any branch: runs /ship Step 8's plan-completion section from disk (hash-pinned) against an explicit plan and base. Report, JSON, exit code. Never edits code.

Paretofilm/superpowers-gstack · 51 tokens

third-lens-review

After Claude self-pitfall + Codex on a ship-worthy/architecture/RT/security/contract change: run a third external model house (distant training distribution → different blind spots) on the patched artifact, then adversarial synthesis.

Paretofilm/superpowers-gstack · 54 tokens

quality-review

After a PRD, spec, or plan, before implementation: hunt perceived-quality pitfalls (silent failures, missing loading/empty states, error recovery, state drift) that make a product feel cheap. Complements pitfall-verification.

Paretofilm/superpowers-gstack · 54 tokens

boundaries

Analyze Phoenix context boundaries and module coupling via mix xref. Use when checking cross-context calls, validating dependencies, before splitting modules, or reviewing architecture.

oliver-kriska/claude-elixir-phoenix · 33 tokens

triage

Triage review findings interactively — approve, skip, or prioritize each issue. Use after /phx:review to filter findings before fixing.

oliver-kriska/claude-elixir-phoenix · 32 tokens

review-code

Review a change along one specific quality dimension — bugs, design, simplicity, maintainability, testability, test quality, type safety, contracts, operational readiness, docs, prose value, change intent, defect-class completeness after a fix, or context-file adherence. Loads exactly one dimension reference and…

doodledood/manifest-dev · 101 tokens