The Tool Desk
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Before comparing coding-agent scores, hash the exact task-pack artifact used in the evaluation and publish that digest alongside the benchmark materials. A cryptographic hash can help others verify that they have the same bytes; it cannot show that the tasks are representative, the scoring is sound, or the comparison is fair.
What hashing a task pack does—and does not—prove
A SHA-256 digest is a compact identifier calculated from an artifact’s bytes. If even one byte changes, the resulting digest will ordinarily change, making it useful for checking whether two copies of a task pack match. Python 3.12’s official hashlib documentation includes a file-hashing example using hashlib.file_digest(f, "sha256").
The digest identifies the file or archive you hashed, not the quality of the experiment. It does not establish that the tasks reflect real work, that the scoring function measures useful outcomes, or that each agent received equivalent tools, time, or compute. Those questions require separate evidence about the evaluation design.
Hash the artifact that participants actually receive
First define what “task pack” means for the evaluation: for example, a named directory or a single archive containing the task descriptions, fixtures, and other included inputs. Document the included files, then calculate SHA-256 over the exact artifact that will be distributed or run. Record both the algorithm and full digest in the run metadata.
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For a file, Python’s documented helper can calculate a digest while reading it:
import hashlib
with open("task-pack.tar", "rb") as f:
digest = hashlib.file_digest(f, "sha256").hexdigest()
print(digest)This example hashes the bytes of task-pack.tar. It does not hash an abstract directory independently of how that directory is represented. If you instead distribute a directory, define a reproducible inventory and hashing procedure, or package it into a specific archive and hash that archive. Changes to line endings, file ordering, archive settings, or contents can change the bytes and therefore the digest; after any such change, compute and publish a new digest.
Publish a manifest and the evidence, not just the digest
A useful manifest makes the rest of the comparison inspectable. Keep task-pack identity separate from the configuration used to produce scores, and record at least:
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- Task-pack name or version, file inventory, hash algorithm, and digest.
- Agent provider and model version, plus prompt and configuration versions.
- Tool access and runtime environment.
- Dependencies and package lock or freeze files.
- Scoring implementation and evaluator details.
- Time, token, or compute limits, retry policy, and trial seeds where applicable.
- Run identifiers, exclusions, failures, and any configuration changes.
Preserve the original task pack, raw per-run outputs, analysis code, and dependency records. Make them available with the published scores when licensing and privacy permit. A hash without the corresponding artifact lets readers compare a claimed identity only if they can obtain the artifact; raw outputs and analysis materials are what help them inspect how the result was produced.
BenchClaw describes one concrete evidence bundle that includes a hashed corpus, raw JSONL results, request ledgers, an analysis script, and package freezes. Its page also says its methodology addendum, corpus specification, and workload generator were public before measurement. This is a publisher’s account of its own benchmark and a useful transparency example, not independent validation or a universal required protocol: BenchClaw benchmark examples.
Verify the hash and keep run history
- Before each run: calculate the digest of the artifact about to be used and compare it with the manifest.
- When sharing or downloading: have the recipient calculate the same algorithm over the received artifact and compare the full digest.
- If it differs: stop treating the pack as identical. Identify the changed artifact, document the new version and digest, and do not silently combine its scores with results from the earlier pack.
- When reporting results: document task exclusions, failed runs, configuration changes, and task updates. Preserve the run history rather than presenting only the final selected result.
BenchClaw’s page describes discarding an invalid first pass rather than publishing its results, illustrating why run history and exclusions matter alongside a final score. That example does not establish a general rule for handling every invalid run; the benchmark should state its policy and disclose what it did.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare agents across the whole evaluation
Two scores are meaningfully comparable only when readers can understand more than whether the task files match. Check the following axes before interpreting a ranking:
| Comparison axis | What to inspect |
|---|---|
| Task-pack identity | Version, file inventory, artifact format, and digest. |
| Agent configuration | Provider and model version, prompts, and other configuration. |
| Tools and environment | Available tools, runtime conditions, and dependencies. |
| Scoring | Scoring code and how the evaluator was calibrated or applied. |
| Resources | Compute, token, and time budgets, plus retry policy. |
| Trials and uncertainty | Number of runs, seeds where relevant, variability, and uncertainty reporting. |
| Evidence access | Availability of task artifacts, raw results, run records, analysis code, and dependency files. |
Hashing addresses the first axis: whether an identified artifact is the same. It does not settle the other axes or prescribe one complete protocol for every coding-agent evaluation. Treat the digest as a reproducibility check within a broader, documented benchmark—not as proof that a ranking is meaningful.
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