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.
npx agentmods add commands/marcusquinn/aidevops/auto-reasongit clone --depth 1 https://github.com/marcusquinn/aidevopsWrote 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.
[](https://agentmods.dev/commands/marcusquinn/aidevops/auto-reason)<a href="https://agentmods.dev/commands/marcusquinn/aidevops/auto-reason"><img src="https://agentmods.dev/badge/commands/marcusquinn/aidevops/auto-reason.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00020 | $0.01248 |
| Opus 5 | $0.00010 | $0.00624 |
| Sonnet 5 | $0.00004 | $0.00250 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
auto-reason 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.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run provider-agnostic subjective self-refinement: create or accept an incumbent answer, generate an adversarial revision and synthesis, then use fresh blind judges to decide whether to change or stop.
Arguments: $ARGUMENTS
Invocation Patterns
| Pattern | Example | Behaviour |
|---|---|---|
| One-liner | /auto-reason "decide the best architecture for X" |
Build a temporary program and run now |
--program <path> |
/auto-reason --program todo/research/reason-product-strategy.md |
Run from a saved program |
--incumbent <path> |
/auto-reason --incumbent draft.md "improve this argument" |
Use existing answer as A |
--judges <list> |
/auto-reason --judges simple,standard,openai/gpt-5.5 "..." |
Override judge tiers/models |
| Bare | /auto-reason |
Interactive setup |
Step 1: Resolve Invocation
if $ARGUMENTS contains "--program ": → Program Mode
elif $ARGUMENTS contains "--incumbent ": → Incumbent Mode
elif $ARGUMENTS is non-empty: → One-Liner Mode
else: → Interactive Setup
Step 2: Interactive Setup
Ask sequentially; show inferred default as option 1; Enter accepts default.
Q1 — What decision, answer, or artifact should be refined?
Capture a clear task prompt. If the user supplies a file path, read it as the incumbent A only after verifying the path exists.
Q2 — What rubric should judges use?
Default rubric:
1. Correctness: answers the actual question and avoids factual errors.
2. Usefulness: gives actionable, decision-grade output.
3. Restraint: avoids scope creep, unnecessary expansion, and decorative rewrites.
4. Clarity: concise structure, explicit trade-offs, no vague hedging.
Domain-specific additions:
| Domain | Extra rubric |
|---|---|
| Architecture | maintainability, reversibility, integration cost |
| Strategy | evidence quality, risk coverage, opportunity cost |
| Prose | audience fit, voice consistency, logical flow |
| Policy | enforceability, edge cases, security posture |
| Review synthesis | finding validity, severity calibration, non-duplication |
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.
- 3d ago First seen · 179 lines · 20 tokens per session scan A 9f367f45bf7f
auto-reason is a command published in the GitHub repository marcusquinn/aidevops (391 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 1,248 once invoked, about $0.0001 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.
Other commands, from other repositories
workflow-classify
Classify task size (S/M/L/XL) and recommend the appropriate workflow pipeline.
verify-loop
자동 재검증 루프 (최대 3회 재시도, 실패 시 자동 수정).
eval
Evaluate and improve one healthcare agent's system prompt. Run up to 5 iterations of: prepare fixed questions -> answer -> judge -> improve -> re-score -> commit if better.
gsd:audit-milestone
Audit milestone completion against original intent before archiving.
gsd:progress
Check project progress, show context, and route to next action (execute or plan).
gsd:map-codebase
Analyze codebase with parallel mapper agents to produce .planning/codebase/ documents.