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/transcript-distillationnpx skills add ProfSynapse/Synaptic-Tuner --skill transcript-distillationgit 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 | $0.00136 | $0.01617 |
| Opus 5 | $0.00068 | $0.00809 |
| Sonnet 5 | $0.00027 | $0.00323 |
| Haiku 4.5 | $0.00014 | $0.00162 |
Grade A, and why
transcript-distillation 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 2d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcript Distillation
Mine local agent transcripts into training data. You point it at a directory of conversation logs; it emits one row per assistant turn, scrubs secrets, labels each turn (accept / borderline / reject), scores each session by how it ended up (tests passed, clean build, commit, PR, approval), drops ceremony/filler/ duplicates, and stamps a quality tier (gold/silver/bronze) on every row.
Format-agnostic. The engine knows nothing about Claude or Codex — that lives
in small pluggable adapters. Two ship built-in (claude_code, codex); add
more in ~40 lines (see reference/writing-adapters.md).
Quickstart
SKILL=.skills/transcript-distillation
# 1. copy the template config and (optionally) edit scope/paths
cp $SKILL/configs/template.yaml my_distill.yaml
# 2. smoke test on a few files first — ALWAYS do this before a full run
python $SKILL/scripts/distill.py --config my_distill.yaml --smoke --limit 20 --show 3
# 3. full run
python $SKILL/scripts/distill.py --config my_distill.yaml
Out of the box the template grabs all local Claude Code (~/.claude/projects)
and Codex (~/.codex/sessions) transcripts and writes to
./transcript_distill_out/rows.jsonl. Only dependency: pyyaml.
CLI
| Flag | Meaning |
|---|---|
--config PATH |
distill config YAML (required) |
--smoke |
write to a smoke/ subdir (keeps full runs separate) |
--limit N |
cap files per source (0 = all) — use for smoke tests |
--sources a,b |
restrict to named sources from config |
--show N |
print N sample rows to eyeball quality |
--max-context-tokens N |
override context_budget.max_context_tokens for this run |
Pipeline (all config-driven)
adapter.parse(file) # format-specific -> normalized events
-> one row per assistant turn
-> secret scrub at emit time (raw secrets never hit disk)
-> context filled most-recent-first under a TOKEN budget
-> deterministic label: accept / borderline(->judge) / reject
-> quality funnel: drop ceremony tools, trivial filler, duplicates
-> session-outcome tiering: gold / silver / bronze
-> JSONL
What ships with it
11 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.
- configs/template.yaml 4.7 KB
- reference/case-study-claude-codex.md 3.5 KB
- reference/config-schema.md 4.7 KB
- reference/writing-adapters.md 3.1 KB
- scripts/adapters/__init__.py 661 B runs code
- scripts/adapters/base.py 2.6 KB runs code
- scripts/adapters/claude_code.py 4.8 KB runs code
- scripts/adapters/codex.py 4.3 KB runs code
- scripts/distill.py 19 KB runs code
- scripts/project_rows.py 16 KB runs code
- scripts/sanitize.py 2.0 KB runs code
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.
- 2d ago First seen · 137 lines · 136 tokens per session scan A a701fa3e6d6b
transcript-distillation is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed 2d ago), licensed MIT. It adds 136 tokens to every session and 1,617 once invoked, about $0.0007 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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