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/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 | $0.00013 | $0.00462 |
| Opus 5 | $0.00006 | $0.00231 |
| Sonnet 5 | $0.00003 | $0.00092 |
| Haiku 4.5 | $0.00001 | $0.00046 |
Grade A, and why
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 report command. Generate a signed record of THIS
session and explain it in plain English. Engine:
${CLAUDE_PLUGIN_ROOT}/engine/capture.py.
Steps:
- Ensure the signing packages are installed (once). Prefer the pinned set:
pip install -r "${CLAUDE_PLUGIN_ROOT}/requirements.txt". - Write this session's real facts to
live.json. Use values you actually observe, never invented ones:model_id,model_provider,model_version,builtin_tools(your actual built-in tools),mcp_servers(the MCP servers actually connected now). - Run
python "${CLAUDE_PLUGIN_ROOT}/engine/capture.py" report --live-context live.json --out .This writesmanifest.json,trace.json, andverification_key.json(the public key a third party uses to verify the manifest). The manifest is signed with the persistent key at~/.claude/agentrust/signing_key.json; the private half never leaves the machine. - Optionally confirm the TRACE record passes the suite:
trace-tests verify --record trace.json --level 0(orpython -m trace_tests.cli verify --record trace.json --level 0if the console script is not on PATH).
Then explain the report the user actually cares about:
- what the agent IS: skills, tools, MCP, model, permissions, each fingerprinted.
- what it DID this run: the TRACE record, software-only and Level 0 on a dev box.
- whether anything changed since their approved baseline.
- that the records are shareable:
manifest.jsonverifies on any machine withverification_key.json, andtrace.jsonpasses the public conformance suite.
Be honest about scope. No TEE on a normal laptop means Level 0, not hardware
attestation. The instruction-layer fingerprint covers CLAUDE.md and memory, not
Claude Code's internal system prompt, which is not on disk.
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 · 40 lines · 13 tokens per session scan A 57769ff614cc
trace is a command published in the GitHub repository agentrust-io/integrations (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 13 tokens to every session and 462 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.