Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Pratham-Mishra04/trail/plugin install trailWrote 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/pratham-mishra04/trail/debug-with-trail)<a href="https://agentmods.dev/skills/pratham-mishra04/trail/debug-with-trail"><img src="https://agentmods.dev/badge/skills/pratham-mishra04/trail/debug-with-trail/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/pratham-mishra04/trail/debug-with-trail"><img src="https://agentmods.dev/badge/skills/pratham-mishra04/trail/debug-with-trail.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 | $0.00052 | $0.09631 |
| Opus 5 | $0.00026 | $0.04816 |
| Sonnet 5 | $0.00010 | $0.01926 |
| Haiku 4.5 | $0.00005 | $0.00963 |
Grade D, and why
debug-with-trail scanned grade D with 3 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 9d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
Before pinging the user for a curl/argv/enqueue command, spend a minute trying to construct it from the code. The user already told you what's failing — that usually points at a route, a handler, or a function whose call Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
> curl -fsSL https://raw.githubusercontent.com/Pratham-Mishra04/trail/main/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Repro command (curl / test invocation / enqueue): How it starts
The opening of the file, as written. The whole thing — 539 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug with trail
Use this workflow when investigating a runtime issue with the help of trail — a local CLI that captures process stdout/stderr into per-session JSONL files and exposes them via the MCP tools list_sessions and get_logs.
The workflow applies to two broad cases:
- Long-running processes — servers, daemons, workers, anything that stays up and emits logs continuously. The user reproduces the issue by sending a request / triggering the code path, and you query the captured stream.
- Test failures — a test (or a small subset) is failing and you need to figure out why. The "process" trail captures is the test invocation itself (
trail run -- go test ./...,trail run -- npm test,trail run -- pytest path/to/test.py). The test runner's stdout/stderr (assertion failures, panics, framework output, plus anyprint/console.log/fmt.Printlnfrom the code under test) lands in a session you can query exactly the same way. Instrumentation goes into either the test file or the production code the test exercises; the marker-based cleanup rules are identical.
Most phases below are written with a server in mind because that's the more common case, but the test-debugging variant is called out wherever the flow differs (mainly around how the session is launched and how iteration works without a long-running daemon).
The workflow has six phases. Steps 1, 2, 4, 5, and 6 are mandatory. Step 3 is conditional — only do it when the existing logs don't already explain the issue. Cleanup (Phase 6) only happens after the fix is verified in Phase 5 — never clean up instrumentation while the user might still want to keep digging or while a fix is unverified.
Supporting files:
reference.md— auto-reloader / test-runner watcher tables, framework "ready" indicators, per-language instrumentation syntax. Load when the inline shortlists don't cover the user's stack.examples.md— three end-to-end worked traces (existing-logs diagnosis, closed-loop variant, failing test). Read when you want a concrete picture of phase compression.
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.
- 9d ago First seen · 539 lines · 52 tokens per session scan D 13d9f645b80f
debug-with-trail is a skill published in the GitHub repository Pratham-Mishra04/trail (8 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 9,631 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it D with 3 findings (sends data to an external url, downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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