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/understudylabs/understudy-agent-tools/curate-trajectoriesnpx skills add understudylabs/understudy-agent-tools --skill curate-trajectoriesgit clone --depth 1 https://github.com/understudylabs/understudy-agent-toolsWrote 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/understudylabs/understudy-agent-tools/curate-trajectories)<a href="https://agentmods.dev/skills/understudylabs/understudy-agent-tools/curate-trajectories"><img src="https://agentmods.dev/badge/skills/understudylabs/understudy-agent-tools/curate-trajectories.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 | $0.00106 | $0.01927 |
| Opus 5 | $0.00053 | $0.00963 |
| Sonnet 5 | $0.00021 | $0.00385 |
| Haiku 4.5 | $0.00011 | $0.00193 |
Grade A, and why
curate-trajectories 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Curate trajectories
Trajectories pile up as loose per-task JSON across runs (each row roughly
{id, name, score, passed, assertion_results, steps, messages, end_state, finish_reasons, model, toolset, domain, seed, input/output_tokens, cost}). The
moment anyone feeds them to training, distillation, or RL they stop being logs
and become a dataset — and a dataset with no split hygiene silently kills
heldout claims. This worker owns the trajectory dataset as a first-class,
queryable, provenance-tracked, contamination-safe artifact: import → tag splits →
select (hash-stamped) → contamination check → emit a decontaminated pool.
The store is provider-agnostic. The source is Lilac or a local JSON corpus; this skill owns the hygiene and provenance layer on top, not the browser.
Safety Gates
- Block by default. Any selection destined for a train / RL / distill / SFT pool is built excluding frozen dev+holdout. Including holdout (or dev) in such a pool is refused unless the developer passes an explicit override, and every override is logged in the manifest with who/when/why. Never silently include. A blocked selection is the safe outcome, not an error to route around.
- The frozen splits are the source of truth. Split membership comes from
splits.jsonproduced bycapture-evidence, never re-derived by re-seeding or re-hashing here. Ifsplits.jsonis missing or stale, stop and route to capture-evidence — do not guess membership. - Local-first, no exfiltration. Index, query, and manifest stay under
.understudy/. Never upload the corpus, never print message bodies, secrets, or raw payloads — operate on ids, provenance fields, counts, and hashes. - Reproducible or it didn't happen. Every selection is named and hash-stamped (selection hash over the sorted row ids + filter expr + corpus hash). Downstream consumers cite the hash, not a manual grep.
Decision Gate
Use this skill whenever trajectories feed a train / RL / distill / SFT pool, or
whenever you would otherwise hand-filter rows in bash (grep toolset=api, "drop
the seed-7 rows", "keep only passes"). Hand-filtering is the #1 way a heldout
claim silently dies; replace it with a hash-stamped selection. If the trajectories
are only being eyeballed (no downstream training/claim), a manifest is optional —
but the moment a number leaves the building, curate first.
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 · 128 lines · 106 tokens per session scan A 427fe56be9cf
curate-trajectories is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 106 tokens to every session and 1,927 once invoked, about $0.0005 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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