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.
git clone --depth 1 https://github.com/ololand-ai/ololand-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/commands/ololand-ai/ololand-plugins/inspect-run)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/inspect-run"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/inspect-run/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/commands/ololand-ai/ololand-plugins/inspect-run"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/inspect-run.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 | $0.00063 | $0.00846 |
| Opus 5.5 | $0.00025 | $0.00338 |
| Sonnet 5.5 | $0.00013 | $0.00169 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
inspect-run 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 1mo 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/inspect-run
You are reading the audit trail for a single OloLand agent run. Everything an analyst, compliance reviewer, or auditor needs to reconstruct what happened and why lives in agent_runs + agent_spans. This command surfaces both.
Required inputs
- run_id — the agent_runs row id. If the user doesn't have one, offer to list recent runs:
- "I don't have a run_id. Want me to list recent runs for your company first?" → call
mcp__ololand__list_agent_runs(optionally filtered bydeal_id,status_filter,model_version,entry_point).
- "I don't have a run_id. Want me to list recent runs for your company first?" → call
Optional:
- kind — filter spans by kind (
model_call,tool_call,compaction,sse_event). Useful when a run has hundreds of spans and the user wants to focus. - show_grader — default
true. Include the grader verdict in the output.
Action
- Call
mcp__ololand__get_agent_runwithrun_idfor the run header + per-kind span counts + grader fields. - If the user wants the per-span detail (default: yes for runs with <50 spans, ask for runs with more), call
mcp__ololand__list_agent_spanswithrun_idand optionalkind. - If
show_graderand the run has agrader_passedvalue, also surface the rationale verbatim frommcp__ololand__get_grader_verdict(or directly from thegrader_rationalefield already inget_agent_run's payload).
Output
Render in this order:
Run <run_id>
Agent: <agent_name>
Status: <status>
Entry point: <entry_point>
Model: <model_provider> / <model_version>
Harness: <harness_version>
Deal: <deal_id>
Started: <started_at>
Ended: <ended_at>
Tokens
Input: <total_input_tokens>
Output: <total_output_tokens>
Cache creation: <total_cache_creation_tokens>
Cache read: <total_cache_read_tokens>
Cost (cents): <cost_cents>
Spans (<total>)
<kind>: <count>
...
Grader verdict (if any)
Passed: <grader_passed>
Rationale: <grader_rationale>
Artifact run: <grader_artifact_run_id>
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.
- 1mo ago First seen · 79 lines · 63 tokens per session scan A a7acd8399cf9
inspect-run is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 63 tokens to every session and 846 once invoked, about $0.0003 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.
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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.