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 agents/amdresearch/ai4science-studio/synthesizergit clone --depth 1 https://github.com/AMDResearch/ai4science-studioWrote 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/agents/amdresearch/ai4science-studio/synthesizer)<a href="https://agentmods.dev/agents/amdresearch/ai4science-studio/synthesizer"><img src="https://agentmods.dev/badge/agents/amdresearch/ai4science-studio/synthesizer.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 | $0.00000 | $0.01206 |
| Opus 5 | $0.00000 | $0.00603 |
| Sonnet 5 | $0.00000 | $0.00241 |
| Haiku 4.5 | $0.00000 | $0.00121 |
Grade A, and why
synthesizer 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
synthesizer subagent
Merge tracelens/verified_claims.json and omnistat/verified_claims.json into a single ranked, deduplicated bottleneck report.
Inputs
<perf_run_dir>/manifest.json<perf_run_dir>/tracelens/verified_claims.json<perf_run_dir>/omnistat/verified_claims.json- (Optional)
<perf_run_dir>/tracelens/report_summary.md,<perf_run_dir>/omnistat/report_summary.md
Outputs
<perf_run_dir>/combined_report.md
Steps
1. Load and filter
import json
tl = json.load(open(f"{perf_run}/tracelens/verified_claims.json"))
om = json.load(open(f"{perf_run}/omnistat/verified_claims.json"))
all_claims = [{"src":"TL", **c} for c in tl] + [{"src":"OS", **c} for c in om]
Drop verdict == "refuted". Keep inconclusive claims but tag them so the user sees the gap.
2. Deduplicate by class + topic
Group claims by class. Within each group, merge claims that look like the same finding from two sources. Heuristic:
- Both have
class=comm_scaleout→ almost certainly the same finding viewed from trace (NCCL kernels) and telemetry (network rates). Merge into one entry that lists bothsrcvalues and both magnitudes. - One says
gpu_computelow TFLOP/s, the other saysgpu_memory_hbmhigh HBM% — these are likely the same root cause (memory-bound kernel) seen two ways. The synthesizer should call this out as a single finding with both signatures.
3. Rank
Score = magnitude.value × confidence_weight × (corroborated ? 1.5 : 1.0).
confidence_weight: high=1.0, medium=0.7, low=0.4. corroborated = both TL and OS contributed to the merged entry.
4. Tag "system limit reached"
A claim is a system limit if any of:
proposed_remedy is null- both
verdict=verifiedandremedy_probe.delta_pctis small (< 5%) - the metric matches a documented MI355X spec ceiling (e.g. fp64 39 TFLOP/s, HBM 8 TB/s, ANP scale-out ~25 GB/s)
Mark these explicitly so the report doesn't promise a fix that won't materialize.
5. Write combined_report.md
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 · 132 lines · 0 tokens per session scan A 989dd91c66e4
synthesizer is an agent published in the GitHub repository AMDResearch/ai4science-studio (4 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,206 tokens. 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.
Other agents, from other repositories
sft
Supervised fine-tuning pipeline reference. For config templates and dataset format examples, see getting-started.qmd and dataset-formats/.
by-liability-engineer
Actively propose mutations to fix identified sequence liabilities. Score mutations for structural impact, consider framework vs CDR context, generate ranked mutation panels with predicted impact, and output mutation tables with position, original, proposed, motif removed, and risk level.
by-campaign
Plan design campaigns. Analyze research, select modality, choose scaffolds, estimate costs, create campaign state, and present structured plan for user approval.
by-plan-checker
Review campaign plans for completeness and correctness before user approval. Checks fold validation, cost estimates, modality selection, parameters, and scaffold justification.
by-knowledge
Query and update the learning system. Store campaign outcomes, query similar campaigns, get scaffold rankings, record failures, and generate recommendations.
grounded-review-writer
Apply reviewer-approved repairs to the research report draft for grounded-review while preserving substance.