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synthetic-parallel-reasoning

synthetic-parallel-reasoning is a skill for Claude Code from winstonkoh87/Athena-Public. It costs 64 tokens per session (545 once invoked), scanned A, original, MIT.

A procedure for getting four separate external analyses of a difficult problem and combining them through a final review gate. The four perspectives are domain expertise, skeptical checking, cross-domain pattern matching, and first-principles reasoning.

In plain words
What is it for?
Use it for strategic bottlenecks or other problems where the procedure's trigger phrases apply, using the included orchestration script and Gemini API calls.
Why use it?
It reduces reliance on a single answer by making different lines of analysis challenge and compare one another.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 .agent/scripts/parallel_orchestrator.py "<query>" \.

not rated 620repo yesterday A scan SkillSpector: pass 64 tokens original MIT

Good fit Use it for strategic bottlenecks or other problems where the procedure's trigger phrases apply, using the included orchestration script and Gemini API calls.

Compare 6 skills from other repositories ↓
View source ↗ winstonkoh87/Athena-Public winstonkoh87.com
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/winstonkoh87/Athena-Public
agentmods
npx agentmods add skills/winstonkoh87/athena-public/synthetic-parallel-reasoning

Made for: Claude Code.

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.

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README.md
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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 synthetic-parallel-reasoning

Your own site · 80×15
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Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 545 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 $0.00064 $0.00545
Opus 5.5 $0.00026 $0.00218
Sonnet 5.5 $0.00013 $0.00109
Haiku 4.5 $0.00006 $0.00055

Measured 2mo ago against content hash 17ba1045ae57, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

synthetic-parallel-reasoning 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 2mo 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.

examples/skills/research/synthetic-parallel-reasoning/SKILL.md · 49 lines

What it actually says

Parallel Synthetic Architect (Protocol 75 Engine v4.0)

Deploys true parallel reasoning via parallel_orchestrator.py to evaluate a strategic bottleneck. Refuses single-shot answers to complex problems.

Triggers

"difficult problem", "what's the best strategy", "how should I handle this", "analyze this", /ultrathink

Core Mechanics (v4.0)

  1. Phase 1 (Prime): Run semantic search, build internal CoT hypothesis, write context file.
  2. Phase 2 (Execute): Run parallel_orchestrator.py — this dispatches 4 parallel Gemini API calls:
    • Track A: Domain Expert (applies user's frameworks)
    • Track B: Adversarial Skeptic (attacks premises, checks Law #1)
    • Track C: Cross-Domain Pattern Matcher (finds isomorphic patterns)
    • Track D: Zero-Point First Principles (inversion, RETO lens)
  3. Phase 3 (Deposit): Read output, present synthesis, quicksave.

Enforcement

[!CAUTION] The script execution is MANDATORY. If the LLM writes a single-pass essay instead of running the script, it has violated this protocol. A single LLM checking its own homework hits a quality ceiling (Trilateral Feedback Loop principle).

Execution

python3 .agent/scripts/parallel_orchestrator.py "<query>" \
  --context-file /tmp/ultrathink_context.md \
  --output .context/state/ultrathink/ultrathink_$(date +%Y%m%d_%H%M%S).md

Reference Paths

  • .agent/workflows/ultrathink.md (v4.0)
  • .agent/scripts/parallel_orchestrator.py (v4.0)
  • Athena-Public/examples/protocols/decision/75-synthetic-parallel-reasoning.md
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. 2mo ago First seen · 49 lines · 64 tokens per session scan A 17ba1045ae57

Subscribe to this mod's changes

synthetic-parallel-reasoning is a skill published in the GitHub repository winstonkoh87/Athena-Public (620 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 545 once invoked, about $0.0003 per session on Opus 5.5. 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.

Backend and APIsPythonResearchai-assistantautomationframeworkgeminigoogle-antigravity
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