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/blendsdk/claude-codeops/codeops_statsgit clone --depth 1 https://github.com/blendsdk/claude-codeopsWhat 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 | $0.00083 | $0.00603 |
| Opus 5 | $0.00042 | $0.00302 |
| Sonnet 5 | $0.00017 | $0.00121 |
| Haiku 4.5 | $0.00008 | $0.00060 |
Grade A, and why
codeops_stats scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
codeops_stats — relay the telemetry tables
The utility at "${CLAUDE_PLUGIN_ROOT}/scripts/codeops-events.sh" is the ONLY reader of the
telemetry file (~/.claude/codeops-telemetry/events.jsonl). This command runs it and relays
its output verbatim — never open, grep, or summarize events.jsonl yourself; the utility
pre-aggregates to a table of at most ~40 lines precisely so raw events stay out of context.
Mapping the ask to an invocation
| User asks for | Run |
|---|---|
| overall picture (default) | codeops-events.sh stats |
| per-agent rates | codeops-events.sh stats --by agent |
| per-lens rates | codeops-events.sh stats --by lens |
| per-project / per-event counts | codeops-events.sh stats --by project / --by event |
| planned vs. verified, first-pass rate, rework | codeops-events.sh stats --by delivery |
| runtime ambiguity by owning stage, resume accuracy | codeops-events.sh stats --by drift |
| whether delegated design decisions resolved or escalated | codeops-events.sh stats --by design |
| a time window ("last 2 weeks") | add --since 14d |
| one project only | add --project <name> |
| emission gaps ("reviews without rulings") | codeops-events.sh gaps [--since <Nd>] |
Pass $ARGUMENTS through when the user already typed flags.
Relay rules
- Print the utility's table as-is (a fenced code block keeps the alignment). Add at most a one-line reading of what stands out — no re-derivation, no editorializing beyond the numbers.
no events recorded→ explain the likely causes: telemetry off (CODEOPS_TELEMETRY=0, ortelemetry: offin the repo's quality block),jqmissing, or simply nothing has run yet.- For threshold judgments ("is this acceptance rate bad?") point to
/codeops_retro— that command owns the thresholds; this one only reports.
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 · 43 lines · 83 tokens per session scan A ecf4d1a9b901
codeops_stats is a command published in the GitHub repository blendsdk/claude-codeops (4 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 603 once invoked, about $0.0004 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.