Clawshier

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name: clawshier description: Process receipt or invoice images into structured expenses and log them to Google Sheets. Use when the user wants to scan, log, track, or record an expense from a receipt or invoice image, or when they provide a local file path to a receipt/invoice image. OCR uses OpenAI by default; set CLAWSHIER_VISION_PROVIDER=ollama to use local Ollama instead. metadata: {"openclaw":{"requires":{"env":["GOOGLE_SHEETS_ID","GOOGLE_SERVICE_ACCOUNT_KEY"]},"primaryEnv":"OPENAI_API_KEY"}}

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Clawshier

Process a receipt or invoice image through a four-step pipeline, then reply with a short summary of what was added.

Input handling

If the user provides a local file path to the image, use that path directly.

If the user sends an image in chat and a local attachment path is available, use that path.

If no local file path is available for the image, ask the user to resend it as a file or provide a path you can execute against.

If the user explicitly gives the receipt date, preserve it and pass it to step 3 with --date YYYY-MM-DD.

Workflow

Run the safe pipeline runner. If it fails, retry it up to 2 times before surfacing the error.

Primary path — Safe pipeline runner

Run:

node {baseDir}/scripts/run_pipeline.js --image 

If the user explicitly provided a date, always pass it in ISO format:

node {baseDir}/scripts/run_pipeline.js --image  --date 2026-03-25

This runner performs OCR → structure → validate/deduplicate → store internally using JSON files, not shell-interpolated pipeline strings.

It writes to:

the monthly expense tab (MM-YY)

Invoice Archive Breakdown

Summary

It also removes the default Sheet1 tab if present.

Handler compatibility note

The individual handlers still support stdin/stdout for testing, but when automating the skill, prefer scripts/run_pipeline.js or the handlers' --input-file/--output-file options instead of embedding untrusted receipt/LLM output into shell commands.

If OCR reports that the image is not a receipt or invoice, tell the user:

> I couldn't detect a receipt or invoice in that image. Could you try again with a clearer photo?

If the validator reports a duplicate, stop and tell the user:

> This receipt appears to already be logged (vendor, date, total match an existing entry). Skipping.

Success reply

After a successful run, reply in this format:

> Added expense: {vendor} — {total} {currency} on {date} ({category}). Row #{row} in your spreadsheet (tab {MM-YY}).

If the user explicitly asks for tracing/debugging/cost tracing, append a compact per-step trace summary using the last recorded trace file. Otherwise keep the normal success reply short.

Failure reply

If a step still fails after retries, say which step failed and include the error message.

Notes

Use {baseDir} exactly so the commands do not depend on the current working directory.

For old invoices, prefer --date YYYY-MM-DD instead of relying on same-day date inference.

OCR backend selection is machine-level: CLAWSHIER_VISION_PROVIDER=openai|ollama|auto (default: openai).

auto tries local Ollama first and falls back to OpenAI. Set to ollama to force local-only OCR.

Use CLAWSHIER_OLLAMA_MODEL, CLAWSHIER_OLLAMA_HOST, and CLAWSHIER_OLLAMA_MAX_DIMENSION to control the Ollama OCR backend.

When CLAWSHIER_TEST_MODE=1 is present in the environment, the handlers use local test fixtures and a local mock sheet store. Use that for safe smoke tests before touching real APIs.

Optional tracing: set CLAWSHIER_TRACE=1 to record per-step timing/usage metadata to .clawshier-last-trace.json. Show that trace in chat only when the user explicitly asks for tracing/debugging/cost tracing.

Use {baseDir} exactly so the commands do not depend on the current working directory.

For old invoices, prefer --date YYYY-MM-DD instead of relying on same-day date inference.

OCR backend selection is machine-level: CLAWSHIER_VISION_PROVIDER=openai|ollama|auto (default: openai).

auto tries local Ollama first and falls back to OpenAI. Set to ollama to force local-only OCR.

Use CLAWSHIER_OLLAMA_MODEL, CLAWSHIER_OLLAMA_HOST, and CLAWSHIER_OLLAMA_MAX_DIMENSION to control the Ollama OCR backend.

When CLAWSHIER_TEST_MODE=1 is present in the environment, the handlers use local test fixtures and a local mock sheet store. Use that for safe smoke tests before touching real APIs.

Optional tracing: set CLAWSHIER_TRACE=1 to record per-step timing/usage metadata to .clawshier-last-trace.json. Show that trace in chat only when the user explicitly asks for tracing/debugging/cost tracing.