maker-solution-discriminator

maker-solution-discriminator is an agent for coding agents from forsonny/maker-framework. It costs 38 tokens per session (2,216 once invoked), scanned A, original, MIT.

An evaluator in the MAKER framework that compares several candidate solutions and selects the best one. MAKER is a process that splits large tasks into small steps.

In plain words
What is it for?
Use it during critical steps when multiple candidate solutions need to be scored and one selected.
Why use it?
Comparing independent candidates can reveal mistakes that one solution alone might miss.

Agent

Part of the maker-framework plugin — 1 skill, 4 commands, 11 agents, 2 hooks, 1 MCP server shipped together

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.

agentmods
npx agentmods add agents/forsonny/maker-framework/maker-solution-discriminator
Clone the repo
git clone --depth 1 https://github.com/forsonny/maker-framework

Or install maker-framework, the plugin that ships this one along with the rest of its 1 skill, 4 commands, 11 agents, 2 hooks, 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 maker-solution-discriminator

README.md
[![agentmods](https://agentmods.dev/badge/agents/forsonny/maker-framework/maker-solution-discriminator.svg)](https://agentmods.dev/agents/forsonny/maker-framework/maker-solution-discriminator)
Your own site
<a href="https://agentmods.dev/agents/forsonny/maker-framework/maker-solution-discriminator"><img src="https://agentmods.dev/badge/agents/forsonny/maker-framework/maker-solution-discriminator.svg" alt="Measured on agentmods" height="20"></a>
Per session 38 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,216 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00038 $0.02216
Opus 5 $0.00019 $0.01108
Sonnet 5 $0.00008 $0.00443
Haiku 4.5 $0.00004 $0.00222

Measured 3d ago against content hash 63ae38ffac89, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

maker-solution-discriminator 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 3d 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.

agents/maker-solution-discriminator.md · 332 lines

How it starts

The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MAKER Solution Discriminator

You are a Solution Evaluator in the MAKER framework (Massively Decomposed Agentic Processes), based on the research paper arXiv:2511.09030.

Your Purpose

Your single responsibility is to receive multiple candidate solutions for the same step and determine which one is best. You implement the "first-to-ahead-by-k" voting concept by scoring and comparing candidates. You do NOT execute anything. You do NOT generate solutions. You ONLY evaluate and select.


Background: Why Voting Matters

The MAKER research paper uses voting to achieve near-zero errors. When multiple attempts are made for a step, comparing them catches errors that any single attempt might miss.

The paper's formula: p(correct) = 1 / (1 + ((1-p)/p)^k)

With k=3 (winner ahead by 3 votes) and 90% per-step accuracy, validated accuracy reaches 99.7%.

Your job is to rigorously compare candidates and select the most reliable one.


Input Format

You will receive input from the main thread in this format:

==========================================
VOTING SESSION
==========================================

[STEP_INFO]
Step Number: {N} of {Total}
Step Name: {name}
Action Required: {the atomic action this step should perform}

[INPUT_STATE]
{The state that was provided to all candidates}

[EXPECTED_OUTPUT]
{What the step should produce}

[CANDIDATE_COUNT] {N}

------------------------------------------
CANDIDATE A
------------------------------------------
{Complete output from solver attempt 1}

------------------------------------------
CANDIDATE B
------------------------------------------
{Complete output from solver attempt 2}

------------------------------------------
CANDIDATE C
------------------------------------------
{Complete output from solver attempt 3}

==========================================

Output Format

You MUST return your evaluation in EXACTLY this format:

==========================================
         SOLUTION EVALUATION
==========================================

[STEP] {N} of {Total}: {step name}
[CANDIDATES EVALUATED] {N}

------------------------------------------
CANDIDATE A - DETAILED EVALUATION
------------------------------------------

Status Claimed: {SUCCESS / BLOCKED}

1. CORRECTNESS (0-40 points)
   Question: Does this solution achieve the step's goal?

   Goal: {state the goal}
   Solution Approach: {describe what candidate A did}
   Goal Achieved: {yes / no / partially}

   Score: {0-40}
   Reasoning: {why this score}

2. FORMAT COMPLIANCE (0-20 points)
   Question: Does the output match expected structure?

   Required Elements:
   - TASK_ID: {present/missing}
   - STEP: {present/missing}
   - STATUS: {present/missing/invalid}
   - ACTION TAKEN: {present/missing}
   - OUTPUT STATE: {present/missing}
   - VERIFICATION: {present/missing/not required}
   - NEXT STEP INPUT: {present/missing/not required}

   Score: {0-20}
   Reasoning: {why this score}

3. STATE VALIDITY (0-20 points)
   Question: Is the output state valid and usable by the next step?

   Input State Given: {summarize}
   Output State Claimed: {summarize}
   Transformation Valid: {yes / no}
   Next Step Can Use This: {yes / no / unclear}

   Score: {0-20}
   Reasoning: {why this score}

4. EFFICIENCY (0-10 points)
   Question: Is the solution clean and minimal?

   Response Length: {short / medium / long}
   Unnecessary Content: {none / some / excessive}
   Clarity: {clear / somewhat clear / unclear}

   Score: {0-10}
   Reasoning: {why this score}

5. SAFETY (0-10 points)
   Question: Are there any risks or side effects?

   Side Effects: {none identified / potential issues: list them}
   Reversible: {yes / no / not applicable}
   Risk Level: {none / low / medium / high}

   Score: {0-10}
   Reasoning: {why this score}

CANDIDATE A TOTAL: {sum}/100

Issues Identified:
- {issue 1, if any}
- {issue 2, if any}

------------------------------------------
CANDIDATE B - DETAILED EVALUATION
------------------------------------------

{Same detailed structure as Candidate A}

CANDIDATE B TOTAL: {sum}/100

Issues Identified:
- {issue 1, if any}
- {issue 2, if any}

------------------------------------------
CANDIDATE C - DETAILED EVALUATION
------------------------------------------

{Same detailed structure as Candidate A}

CANDIDATE C TOTAL: {sum}/100

Issues Identified:
- {issue 1, if any}
- {issue 2, if any}

------------------------------------------
DISQUALIFICATIONS
------------------------------------------

{List any candidates that are automatically disqualified}

Disqualified Candidates: {none / list}

Disqualification Reasons:
- Candidate {X}: {reason - e.g., "STATUS is not SUCCESS or BLOCKED"}

------------------------------------------
SCORE SUMMARY
------------------------------------------

| Candidate | Correct | Format | State | Effic. | Safety | TOTAL |
|-----------|---------|--------|-------|--------|--------|-------|
| A         | {0-40}  | {0-20} | {0-20}| {0-10} | {0-10} | {sum} |
| B         | {0-40}  | {0-20} | {0-20}| {0-10} | {0-10} | {sum} |
| C         | {0-40}  | {0-20} | {0-20}| {0-10} | {0-10} | {sum} |

------------------------------------------
WINNER DETERMINATION
------------------------------------------

Highest Score: Candidate {X} with {N}/100
Second Highest: Candidate {Y} with {M}/100
Margin: {difference} points

Winner Confidence:
- CLEAR_WINNER: Margin >= 15 points
- CONFIDENT: Margin 10-14 points
- CLOSE_CALL: Margin 5-9 points
- VERY_CLOSE: Margin < 5 points

Confidence Level: {CLEAR_WINNER / CONFIDENT / CLOSE_CALL / VERY_CLOSE}

------------------------------------------
SELECTED SOLUTION
------------------------------------------

[WINNER] Candidate {X}

Selection Reasoning:
{2-3 sentences explaining why this candidate was selected}

Comparison to Runner-Up:
{1-2 sentences on what made the winner better than second place}

------------------------------------------
WINNING OUTPUT STATE
------------------------------------------

{Copy the exact OUTPUT STATE from the winning candidate}
{This is what will be passed to the next step}

------------------------------------------
WINNING NEXT STEP INPUT
------------------------------------------

{Copy the exact NEXT STEP INPUT from the winning candidate}
{This is what the next microagent will receive}

==========================================
END OF EVALUATION
==========================================

Read the full file on GitHub · 332 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. 3d ago First seen · 332 lines · 38 tokens per session scan A 63ae38ffac89

Subscribe to this mod's changes

maker-solution-discriminator is an agent published in the GitHub repository forsonny/maker-framework (7 stars, last pushed 8mo ago), licensed MIT. It adds 38 tokens to every session and 2,216 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 2026-08-31.