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Synaptic-Tuner: Skill for Claude Code

.agents/skills/research-reporting/SKILL.md

research-reporting is a skill for Claude Code from ProfSynapse/Synaptic-Tuner. It costs 89 tokens per session (1,234 once invoked), scanned A, original, MIT.

A workflow for turning machine-learning experiment runs and analysis files into structured research notes.

In plain words
What is it for?
Use it to start and update notes, summarize completed runs, compare results, extract hypotheses from evaluation or loss data, and suggest evidence-based actions for the next run.
Why use it?
It keeps experiment findings, evidence, failures, and next steps in one consistent record as a run progresses.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: installed under .agents/ (shared by several agents).

This is ProfSynapse/Synaptic-Tuner's own configuration. It tells Claude Code how to work on Synaptic-Tuner itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Synaptic-Tuner configures →

not rated 28repo 3d ago A scan SkillSpector: pass 89 tokens original MIT
View source ↗ ProfSynapse/Synaptic-Tuner
Reuse

Borrowing it

Nothing to install: this file belongs to ProfSynapse/Synaptic-Tuner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/ProfSynapse/Synaptic-Tuner/main/.agents/skills/research-reporting/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ProfSynapse/Synaptic-Tuner

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.

agentmods badge for research-reporting

README.md
[![agentmods](https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/research-reporting/github.svg)](https://agentmods.dev/skills/profsynapse/synaptic-tuner/research-reporting)
Your own site
<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/research-reporting"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/research-reporting/github.svg" alt="Measured on agentmods" height="20"></a>

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 research-reporting

Your own site · 80×15
<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/research-reporting"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/research-reporting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,234 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 $0.00089 $0.01234
Opus 5.5 $0.00036 $0.00494
Sonnet 5.5 $0.00018 $0.00247
Haiku 4.5 $0.00009 $0.00123

Measured 2mo ago against content hash 4459e5711f2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.agents/skills/research-reporting/SKILL.md · 109 lines

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

  1. Resolve the experiment id and open .tracking/experiments/<id>/experiment.json.
  2. If spec_path is present, read the experiment spec so the note captures actual config numbers instead of only outcome artifacts.
  3. Read primary analysis artifacts in this order:
    • analysis/experiment_summary.json
    • analysis/next_run_candidates.json
    • analysis/hypothesis_context.json
    • analysis/run_matrix.csv
  4. Read failure slices only if you need representative examples:
    • analysis/failure_slices/eval_failures.jsonl
    • analysis/failure_slices/high_loss_examples.jsonl
  5. Read stage lineage files when you need provenance, timing, commit, hardware, or cost details.
  6. 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:

  1. 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.
  2. 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.
  3. Final note:
    • After analysis/recommendation, ensure the note contains the final status, observed outcomes, hypotheses, and next-run recommendation.

Read the full file on GitHub · 109 lines

Files

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.

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 · 109 lines · 89 tokens per session scan A 4459e5711f2b

Subscribe to this mod's changes

research-reporting is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (28 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 5.5. 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.

ResearchScientific
Related

research-reporting compared with skills from other repositories

research-reporting next to 6 skills from other repositories
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research-reportingProfSynapse/Synaptic-TunerSkill283d agoMITA89Claude Code
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