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/iliaal/codesage/codesage-benchgit clone --depth 1 https://github.com/iliaal/codesageWhat 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 | $0.00025 | $0.00720 |
| Opus 5 | $0.00013 | $0.00360 |
| Sonnet 5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
codesage-bench 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 2d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeSage retrieval regression check
Wraps ${CLAUDE_PLUGIN_ROOT}/bin/codesage-bench, which runs codesage-bench-runner against every *-eval.yaml corpus in the corpus directory, saves timestamped scorecards under history/, and prints an aggregates table.
Corpus directory resolution (first match wins):
--corpus-dir DIRargument$CODESAGE_BENCH_CORPUS_DIRenvironment variable./bench-corporain the current working directory (fallback default)
Step 1: Run the benchmark
${CLAUDE_PLUGIN_ROOT}/bin/codesage-bench $ARGUMENTS
No re-indexing happens — this only runs codesage search against existing indexes. Each corpus takes 10-60 seconds depending on case count. Background and poll if total runtime exceeds ~2 minutes.
Step 2: Report the summary
Parse the summary table and surface per corpus:
- Cases count
- Miss rate (% of queries with no ground-truth file in top-10)
- Median first-hit rank (1 is ideal)
- Mean recall@5 and recall@10
Flag anything off — reasonable healthy thresholds across application codebases:
miss_rate ≤ ~15%per corpusmedian_first_hit ≤ 3recall@10 ≥ 0.55
Compare the fresh numbers against the most recent prior run of the same corpus (saved under history/). A deviation of more than ~5 points in r@10 on the same corpus signals a regression.
Step 3: Identify stale corpora
If any corpus returns FAIL or produces unexpectedly high miss rates, check whether the eval YAML's expected_files still exist in the indexed project. Renames and deletions upstream stale out the ground truth and distort metrics.
Read the corresponding scorecard file under history/ to see failed queries. Present the list to the user and offer to:
- Update the eval YAML to remove or replace stale references
- Leave it alone — the surprise is the benchmark doing its job
Do not silently "fix" the YAML without explicit direction.
Step 4: Compare to the previous run (only if asked)
If the user says "compare to last run" or similar, take the two most recent scorecards per corpus in <corpus-dir>/history/, compute deltas on the aggregates, and report them.
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.
- 2d ago First seen · 72 lines · 25 tokens per session scan A 08de72d6632e
codesage-bench is a command published in the GitHub repository iliaal/codesage (20 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 720 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 2026-08-30.
Other commands, from other repositories
rb-setup
First-time setup. Configure the LLM API key, or a no-API-key local host runner (Codex / Trae / Claude / any headless CLI) that RepoBrain uses for codebase Q&A and refresh. / 首次 setup,配置 RepoBrain 代码问答与 refresh 所需的 LLM API key,或无需 API key 的本地 host runner(Codex / Trae / Claude / 任意无头 CLI)。.
rb-ask
Ask a question about the current project's codebase via the repobrain knowledge hub. / 通过 repobrain 知识库询问当前项目代码。.
rb-refresh
Rebuild the repobrain project knowledge base after significant changes. / 在重要改动后重建 repobrain 项目知识库。.
rb-init
Scaffold a new multi-agent repository from the RepoBrain template (invokes agent-repo-init skill). / 基于 RepoBrain 模板创建新的多智能体仓库。.
rp-build-cli
Build with rp-cli context builder → chat → implement.
rp-investigate-cli
Deep codebase investigation and architecture research with rp-cli commands.