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/profsynapse/synaptic-tuner/research-reportingnpx skills add ProfSynapse/Synaptic-Tuner --skill research-reportinggit clone --depth 1 https://github.com/ProfSynapse/Synaptic-TunerWhat 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 | $0.00089 | $0.01234 |
| Opus 5 | $0.00044 | $0.00617 |
| Sonnet 5 | $0.00018 | $0.00247 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
research-reporting 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Reporting
Generate compact research notes that are easy to read and easy to parse later.
Use This Skill When
- The user wants a research note, experiment summary, post-run analysis, or structured markdown output.
- The source of truth is an experiment bundle under
.tracking/experiments/<id>/. - The output should include stable frontmatter and explicit evidence for claims.
- The note should be created early and updated through the lifecycle of one experiment.
Default Workflow
- Resolve the experiment id and open
.tracking/experiments/<id>/experiment.json. - If
spec_pathis present, read the experiment spec so the note captures actual config numbers instead of only outcome artifacts. - Read primary analysis artifacts in this order:
analysis/experiment_summary.jsonanalysis/next_run_candidates.jsonanalysis/hypothesis_context.jsonanalysis/run_matrix.csv
- Read failure slices only if you need representative examples:
analysis/failure_slices/eval_failures.jsonlanalysis/failure_slices/high_loss_examples.jsonl
- Read stage lineage files when you need provenance, timing, commit, hardware, or cost details.
- Write the note from
assets/research_note_template.md.
Load reference/artifact-map.md when you need to know which artifact supports which section.
Lifecycle Modes
Use the same note template for all three modes:
- Launch note:
- Create the note as soon as the experiment is launched or selected.
- Fill identity, config, and known runtime fields.
- Leave future metrics and recommendation fields empty.
- Stage update:
- Re-open the same note after training, evaluation, loss, or analysis completes.
- Update only the fields now supported by artifacts.
- Preserve prior fields unless newer canonical artifacts supersede them.
- Final note:
- After analysis/recommendation, ensure the note contains the final status, observed outcomes, hypotheses, and next-run recommendation.
What ships with it
2 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.
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 · 109 lines · 89 tokens per session scan A 4459e5711f2b
research-reporting is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed 3d ago), licensed MIT. It adds 89 tokens to every session and 1,234 once invoked, about $0.0004 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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