codesage-bench

A command that measures how well CodeSage finds relevant files in prepared test collections. It saves dated result files and shows combined scores.

In plain words
What is it for?
Run retrieval regression checks across all configured collections, review missed searches and result rankings, and compare new scores with earlier runs.
Why use it?
It helps detect when changes make code search less accurate, without rebuilding the search indexes.

Command

Install

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.

agentmods
npx agentmods add commands/iliaal/codesage/codesage-bench
Clone the repo
git clone --depth 1 https://github.com/iliaal/codesage
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 720 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 08de72d6632e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

plugins/codesage-tools/commands/codesage-bench.md · 72 lines

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):

  1. --corpus-dir DIR argument
  2. $CODESAGE_BENCH_CORPUS_DIR environment variable
  3. ./bench-corpora in 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 corpus
  • median_first_hit ≤ 3
  • recall@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:

  1. Update the eval YAML to remove or replace stale references
  2. 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.

Read the full file on GitHub · 72 lines

Changes

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.

  1. 2d ago First seen · 72 lines · 25 tokens per session scan A 08de72d6632e

Subscribe to this mod's changes

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.