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 skills add ByteStack-Labs/claude-plugins --skill trajectory-evalgit clone --depth 1 https://github.com/ByteStack-Labs/claude-pluginsWrote 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/bytestack-labs/claude-plugins/trajectory-eval)<a href="https://agentmods.dev/skills/bytestack-labs/claude-plugins/trajectory-eval"><img src="https://agentmods.dev/badge/skills/bytestack-labs/claude-plugins/trajectory-eval/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.
<a href="https://agentmods.dev/skills/bytestack-labs/claude-plugins/trajectory-eval"><img src="https://agentmods.dev/badge/skills/bytestack-labs/claude-plugins/trajectory-eval.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00214 | $0.01574 |
| Opus 5.5 | $0.00086 | $0.00630 |
| Sonnet 5.5 | $0.00043 | $0.00315 |
| Haiku 4.5 | $0.00021 | $0.00157 |
Grade A, and why
trajectory-eval 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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trajectory Eval
An agent that passes every step's check can still fail the task. Errors compound: if each step is 95% reliable, a ten-step task succeeds only about 60% of the time, and the gap widens fast as tasks get longer. A final-answer eval hides this, because it scores the endpoint and ignores the path. A per-step eval hides it too, because it scores steps in isolation and ignores how their errors accumulate across the run.
This skill evaluates the whole trajectory: the steps, the tool calls, the intermediate states, and the way failures propagate or get recovered. It measures where reliability actually leaks, and whether chaining more agents into the system makes it better or just longer.
How to run it
Run trajectory-eval on this agent. Capture full trajectories on production-realistic tasks, measure per-step success against end-to-end success, and show the compounding curve. Attribute failures to the step that introduces them and say whether they cascade or recover. If the system chains or orchestrates agents, measure whether composition helps or hurts against a real baseline, with statistical validation. Test with tool failures and edge responses, not just the happy path. Verify every number with runnable code.
Core commitments
- Verified numbers only. Every figure traces to a measurement produced by runnable code.
- Evaluate the trajectory, not the endpoint and not the isolated step. The final answer and the per-step score are the two numbers that hide compounding failure. The object of study is the path.
- Measure compounding explicitly. Connect per-step reliability to trajectory length to end-to-end success, rather than assuming the steps add up.
- Test under realistic conditions. Real tool responses, including failures and malformed outputs, not a curated happy path. A clean demo trace proves nothing.
- Reproducibility is part of the deliverable. Seed everything. Report the compute envelope.
The procedure
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.
- 2mo ago First seen · 140 lines · 214 tokens per session scan A 70607743ac2e
trajectory-eval is a skill published in the GitHub repository ByteStack-Labs/claude-plugins (2 stars, last pushed 4mo ago), licensed MIT. It adds 214 tokens to every session and 1,574 once invoked, about $0.0009 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 2026-08-31.
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