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 skills/opendcai/dataflow-loopai/complete-math-reasoning-sft-skillnpx skills add OpenDCAI/Dataflow-LoopAI --skill complete-math-reasoning-sft-skillgit clone --depth 1 https://github.com/OpenDCAI/Dataflow-LoopAIWrote 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/skills/opendcai/dataflow-loopai/complete-math-reasoning-sft-skill)<a href="https://agentmods.dev/skills/opendcai/dataflow-loopai/complete-math-reasoning-sft-skill"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-loopai/complete-math-reasoning-sft-skill.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 | $0.00037 | $0.00336 |
| Opus 5 | $0.00018 | $0.00168 |
| Sonnet 5 | $0.00007 | $0.00067 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
dataflow-pattern-math-reasoning-sft 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 5d 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.
What it actually says
Math Reasoning SFT Pattern
Use this pattern for short, verifiable math problems whose training output requires a standalone instruction, regenerated reasoning, and a final answer compatible with strict benchmark evaluation. Do not use it for proof-only tasks, open-ended tutoring, or records without a trustworthy answer signal.
The recommended case is
gsm8k-reasoning-v1. It accepts
question/answer and problem/final_answer schema branches, normalizes them
to canonical fields, regenerates reasoning without exposing the gold answer to
the generator, validates the derived final answer against gold, and applies an
LLM quality evaluation after deterministic correctness checks.
Preserve these invariants when adapting the case:
sample_idand source dataset provenance survive every stage.- The reasoning generator sees the problem but not the gold answer.
- Generated reasoning is accepted only when its parsed conclusion matches gold.
- Quality thresholds are justified by the trial score distribution.
- Output always provides nonempty
instruction,reasoning, andoutput. - Benchmark answer formatting is handled explicitly rather than left to chance.
For a new schema or benchmark, retain the staged validation pattern but adapt normalization, correctness parsing, difficulty coverage, and final formatting to the actual task.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/gsm8k-reasoning-v1/benchmark_samples.jsonl 1.1 KB
- examples/gsm8k-reasoning-v1/case_report.md 2.4 KB
- examples/gsm8k-reasoning-v1/pipeline_review.json 922 B
- examples/gsm8k-reasoning-v1/pipeline.py 3.5 KB runs code
- examples/gsm8k-reasoning-v1/trial_input.jsonl 869 B
- examples/gsm8k-reasoning-v1/trial_output.jsonl 1.5 KB
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.
- 5d ago First seen · 33 lines · 37 tokens per session scan A 1e7c56d4e300
dataflow-pattern-math-reasoning-sft is a skill published in the GitHub repository OpenDCAI/Dataflow-LoopAI (24 stars, last pushed 3d ago), licensed Apache-2.0. It adds 37 tokens to every session and 336 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 2026-08-30.
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