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/tessaryai/plugins/instrumentnpx skills add tessaryai/plugins --skill instrumentgit clone --depth 1 https://github.com/tessaryai/pluginsWhat 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 | $0.00096 | $0.02928 |
| Opus 5 | $0.00048 | $0.01464 |
| Sonnet 5 | $0.00019 | $0.00586 |
| Haiku 4.5 | $0.00010 | $0.00293 |
Grade A, and why
instrument scanned grade A with 1 finding 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 3d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
(`subprocess.run`/`Popen`, `child_process.spawn`/`execa`, `sh -c`) whose argv names `claude`, How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
instrument — bind this repo's LLM calls to call sites
The eval machinery is call-site-keyed: graders bind to call sites, synthesis grounds per call site, the observer reasons per call site. So an ingested span must answer which call site am I?
The platform answers that from exactly one thing — the explicit tessary.call_site.id span
attribute. There is no filepath, span-name, or prompt-shape inference. An untagged span ingests
fully and is visible in the trace viewer, but it is invisible to grader generation: it resolves
to null and every call-site-scoped feature skips it. Explicit-or-nothing, deliberately — guessing
mis-attributes production traffic against an authoritative source.
This skill closes that gap: discover the repo's LLM call sites, give each a stable id, and write
that id into the code as a span attribute. Afterwards the tagged call sites materialize
themselves in the project the first time a tagged span arrives — no upload, no .tessary/
bundle, no pipeline import.
What this skill changes
It edits source files. That is the point, and it is the only skill here that does. Every edit is shown as a diff and confirmed before it is written. It never touches prompt text, model parameters, or control flow — it adds one attribute to a span that already exists, or wraps a call in a span when none does.
Prerequisites
- The repo is linked (
/evals:connect). Tagging without a project to send spans to is busywork. - The repo emits OpenTelemetry, or can.
/evals:connectstep 2 wires the OTLP export.
OTLP is the only supported ingestion path, and this skill installs no SDK. The tag is a plain OpenTelemetry span attribute; it needs nothing beyond the tracer the repo already has. Never propose adding a client library to make tagging work.
Resolve the plugin path once
PLUGIN="${CLAUDE_PLUGIN_ROOT:-$(find ~/.claude -name SKILL.md -path '*/evals/skills/instrument/*' 2>/dev/null \
| xargs -I{} dirname {} | xargs -I{} dirname {} | xargs -I{} dirname {} \
| sort -V | tail -1)}"
echo "PLUGIN=$PLUGIN"
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.
- 3d ago First seen · 242 lines · 96 tokens per session scan A f46af3cb5baa
instrument is a skill published in the GitHub repository tessaryai/plugins (3 stars, last pushed 16d ago), licensed MIT. It adds 96 tokens to every session and 2,928 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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review
5-pass structured code review — correctness, security, performance, readability, consistency.
scaffold
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marshal
Meta-orchestrator that takes any direction — broad, specific, or vague — and autonomously chains skills and context into actionable work. Gathers context from codebase, docs, and memory. Only asks the user when it genuinely cannot proceed. Single-session orchestrator.
organize
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