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 commands/agentrust-io/integrations/schedule-tracegit clone --depth 1 https://github.com/agentrust-io/integrationsWhat 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 | $0.00012 | $0.00363 |
| Opus 5 | $0.00006 | $0.00181 |
| Sonnet 5 | $0.00002 | $0.00073 |
| Haiku 4.5 | $0.00001 | $0.00036 |
Grade A, and why
schedule-trace 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 yesterday.
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.
What it actually says
You are running the AgenTrust scheduled-agent report command. Generate a signed
record of what currently runs without the user watching, and explain it in plain
English. Engine: ${CLAUDE_PLUGIN_ROOT}/engine/capture.py.
Steps:
- Ensure the signing package is installed (once):
pip install -r "${CLAUDE_PLUGIN_ROOT}/requirements.txt". - Run
python "${CLAUDE_PLUGIN_ROOT}/engine/capture.py" report --out .This writestrace.json(a TRACE Trust Record signed with the persistent key at~/.claude/agentrust/scheduled/signing_key.json) andverification_key.json(the public key a third party uses to verify it). The private half never leaves the machine. - Optionally confirm the record passes the public suite:
trace-tests verify --record trace.json --level 0(orpython -m trace_tests.cli verify --record trace.json --level 0).
Then explain what the user cares about:
- what runs without them watching: their routines (schedule, tools, MCP, prompt) and the auto-run hooks, each fingerprinted.
- whether any of it changed since their approved baseline.
- that the record is shareable:
trace.jsonverifies on any machine withverification_key.jsonand passes the public conformance suite at Level 0.
Be honest about scope. This is software-only integrity (Level 0), not hardware attestation, and it fingerprints the declared routine specs and on-disk hooks, not a live cloud routine's runtime behaviour.
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.
- yesterday First seen · 35 lines · 12 tokens per session scan A 5e4ab8e9659e
schedule-trace is a command published in the GitHub repository agentrust-io/integrations (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 363 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.