Best fit
Provider bills, gateway logs, model-price tables, retry and cache events, and customer usage often disagree at invoice close or margin review.
// AI Agents & LLM Ops
LLM Usage Cost Attribution Reconciler
Reconcile provider bills, usage events, model prices, retries, and customer allocation into deterministic AI cost findings.
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Package requirements: SKILL.md-compatible agents that can run a local Python helper. Python 3.10 or newer on Windows, macOS, or Linux using only the standard library. One documented UTF-8 JSON evidence packet; no API key or provider account.
You get: findings.csv with stable codes and evidence sources review-manifest.csv with every reviewed record
Licence and purchase details
Review the package licence at the destination.
Review the offer and access method at your chosen marketplace. Agensi provides connected-agent access, not a download. Buying a skill does not include an AI-agent subscription or setup service.
Agensi access: This product’s bundled-script workflow through Agensi has not been verified here. Tests of other products do not establish this package’s compatibility. Local setup examples apply to separately obtained downloadable packages.
What problem does LLM Usage Cost Attribution Reconciler solve?
Provider bills, gateway logs, model-price tables, retry and cache events, and customer usage often disagree at invoice close or margin review.
Use it to
- Reconcile an LLM provider invoice against gateway usage
- Find retry and fallback costs without customer allocation
- Catch model price-table drift before a margin review
What it returns
- findings.csv with stable codes and evidence sources
- review-manifest.csv with every reviewed record
- result.json with READY, REVIEW, or BLOCK gate and SHA-256 receipt
- report.md with a human-review handoff
A representative input and result
input `billing_items[]` uses `billing_id`, `amount`, and optional `usage_event_id`; `usage_events[]` uses `event_id`, `provider`, `model`, retry evidence, and customer allocation; `price_table[]` uses `price_id`, `provider`, and `model`.
result A deterministic READY, REVIEW, or BLOCK result with stable finding codes, a complete review manifest, a human-readable report, and a SHA-256 receipt.
Access and approval boundaries
- Terminal permission to run the bundled local Python reconciler.
- Read access to one user-selected normalized UTF-8 JSON packet.
- Write access only to one user-selected local output directory.
- No browser, network, credential, account, environment-variable, messaging, payment, deletion, access-change, filing, or publication permission.
Known limitations
- Owner-supplied price tables can be stale and must be refreshed by the responsible operator.
- The package does not query providers, calculate a bill from raw tokens, or authenticate export completeness.
- READY does not prove margin, billing accuracy, or profitability.
// choose with context
Is LLM Usage Cost Attribution Reconciler the right skill?
It returns
findings.csv with stable codes and evidence sources. review-manifest.csv with every reviewed record.
Do not use it as
Owner-supplied price tables can be stale and must be refreshed by the responsible operator.
Compatibility, access, version, and licence
- Reads or accesses
- Declared local scope: one owner-selected local JSON input, one owner-selected local output directory.
- Compatibility
- SKILL.md-compatible agents that can run a local Python helper. Python 3.10 or newer on Windows, macOS, or Linux using only the standard library. One documented UTF-8 JSON evidence packet; no API key or provider account.
- Version
- 1.0.0
- Licence
- Review the package licence and seller terms at the linked destination.
Questions
Does it connect to a live account or provider?
No. It reads one normalized local JSON packet and makes no network request.
Does it make the final decision or external change?
No. It produces evidence findings; the responsible owner makes every decision and action outside the package.
Is the result deterministic?
Yes. Stable finding codes, sorted records, and a SHA-256 receipt make repeated review inspectable.
What happens with malformed input?
Unreadable JSON, missing arrays, duplicate core identifiers, invalid timestamps, and invalid core numbers fail closed.
Does READY prove the underlying evidence is true?
No. READY means no automated finding appeared in the supplied normalized packet; source truth still requires human review.
LLM Usage Cost Attribution Reconciler keeps proof and approval boundaries visible.
The listing includes the tested package, realistic samples, declared permissions, and known limitations.
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