For repeated coding-assistant work, a prompt hash can key a local draft cache: assemble the prompt and selected file contents, compute a SHA-256 digest, and check for a matching draft before considering any remote request. A cache hit can stay on disk; a miss should be eligible for remote filling only under an explicit send policy and after local checks. Hashing identifies content for the cache—it does not detect secrets, encrypt data, authenticate a server, or make transmission safe.
How the proposed workflow works
Riley Wu describes a Python preflight script that reads prompt text from standard input and file paths from command-line arguments. It sorts the paths, combines their contents using separators, joins that material with the prompt, and computes a SHA-256 digest. The script records a Git HEAD value alongside the digest, byte count, file count, secret-marker hits, and a timestamp in a local ledger. The proposed ledger and draft files live in .prompt-cache, which should be excluded from Git. Read Wu’s article.
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- Build the key: provide the prompt and chosen files; the sample uses their exact content, with file paths sorted before assembly.
- Check locally: look for a draft named from the digest. If one exists, the intended behavior is to return that draft without a remote fill.
- Gate a miss: if no draft exists, the sample refuses to proceed when its marker scan finds a match, when
FILL_URLis empty, or when a health probe fails or exceeds the configured example threshold. - Stop at the sample boundary: after those checks, the code stops with “fill client not wired in this sample.” It does not implement or demonstrate sending a remote payload.
The author suggests including only changed files to limit context and unnecessary payload. That is a workflow choice, not proof that the selected context is complete or that the resulting cache is correct.
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What the hash does—and does not—establish
It keys exact content, not meaning
The digest is an exact-content identity for the prompt and file material assembled by the script. A paraphrase, reordered prompt, or changed file content produces a different key; semantically similar requests will not automatically reuse one another’s draft. The sample also records Git HEAD, but a commit value alone may not distinguish worktrees or detached states that share the same commit. Wu raises this context risk and suggests adding the repository’s top-level path. The local cache should not be shared across machines.
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It is not a security control for transmission
A plain digest is not encryption or authentication. It does not conceal data that is separately sent, nor does it establish that a remote endpoint is trustworthy. The decision to transmit on a miss must be enforced by the client and reflect what the prompt and selected files contain. Wu’s line, “A prompt that never leaves disk cannot leak,” describes the local-only case; it is not a guarantee once a remote fill is configured.
Secret checks are a guardrail, not a guarantee
The sample scans for a short, hard-coded set of secret markers and refuses filling if one matches. Wu describes this as a toy check: a real scanner needs project-specific patterns and entropy checks. A miss in that scan does not prove that credentials or other sensitive material are absent. Keep credentials out of prompts and context where possible, and do not treat the marker result as authorization to send.
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Probe results do not predict generation time
The sample includes a health check before a possible fill. Its example cutoff of 0.8 seconds is Wu’s local policy, not a universal threshold or a benchmark. A responsive health endpoint does not show how quickly a full generation will complete; the author explicitly says the probe is not a load test.
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Measure the workflow in your repository
Wu reports no measured speedup or benchmark. The article presents a method: run it on your own repository and record local timings rather than treating the example numbers as results. For a useful evaluation, distinguish local cache hits from misses and record the network conditions and the context included. Compare the policy and behavior on these dimensions:
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- Data boundary: whether prompt and context remain local, and what rules permit a send after a miss.
- Cache correctness: which exact content enters the key, how repository and worktree identity are represented, and how changed context invalidates a draft.
- Secret coverage: what is scanned and what happens when a check matches or cannot run.
- Availability and latency: whether work can continue offline and how a health probe differs from full-generation latency.
- Evidence: whether any performance claim comes from timings you recorded locally; the source provides no benchmark.
What the example does not establish
The proposal is a path for reusing exact repeated context, not a comparison of model quality. Its sample leaves the actual fill client unwired, so it cannot establish how a completed remote transfer behaves. It also does not establish that the implementation has been independently reviewed, that its cache keys cover every relevant context distinction, or that its secret scan is sufficient. Wu discloses that the article was prepared as part of MonkeyCode product outreach; details in that article about free model access or a free server option are claims there, not independently verified current service terms.
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