Vibecode BlackLine
track this build5 steps, step by step0%NOT REALLY · don't bother
You can absolutely build a transaction matcher: read two CSVs, fuzzy match on amount and date, flag the leftovers. That is maybe five percent of what BlackLine is being paid for. The rest is certified reconciliations with named preparers and reviewers, immutable audit trails an external auditor will accept, SOX control evidence, multi-entity intercompany elimination, and live connectors into SAP, Oracle and NetSuite that survive a chart-of-accounts change. A personal replacement is a category error here: nobody buys this for themselves, a controller buys it so the audit does not become a quarter-long forensic exercise. If you are a solo operator reconciling one bank account against one ledger, you never needed it anyway.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of BlackLine. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # BlackLine indie build ## Goal Build the smallest trustworthy replacement for the core BlackLine workflow for one developer or a tiny team. ## Scope Ingest a bank CSV and a ledger CSV, auto-match on amount plus date tolerance plus reference fuzz, and produce a reconciliation report listing matched pairs, unmatched items on each side, and a running difference. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Audit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification - SOX and internal-control reporting that auditors already know how to read - Live ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports - Multi-entity and intercompany handling, FX, and consolidation-scale volumes - Someone accountable when the numbers are wrong at year end If those capabilities are essential, use BlackLine instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local, single-user bank reconciliation tool. No accounts, no cloud, no telemetry, no hosted anything. Stack, non-negotiable: - Python 3.11 - Typer for the CLI - pandas for CSV handling - SQLite via sqlite3 for persistence (file: recon.db) - Jinja2 to render a static HTML report - pytest for tests No web server, no auth, no Docker, no external API calls. What it does: 1. `recon import --side bank --file path.csv` and `--side ledger` ingest CSVs into SQLite. Support column mapping via a mapping.yml so the user can point date/amount/description/reference at their own headers. Store a stable row hash so re-importing the same file does not duplicate rows. 2. `recon match --period 2026-07` runs matching in passes, most confident first: (a) exact amount + exact reference, (b) exact amount within a configurable date window (default 3 days), (c) exact amount + fuzzy description via difflib ratio above a threshold, (d) many-to-one sums where several ledger lines total one bank line, capped at 4 lines to keep it tractable. Every match records the pass that made it and a confidence score. 3. `recon review --period 2026-07` interactive CLI: step through low-confidence and unmatched items, accept, reject, or tag as a known timing difference or fee. Decisions persist so re-running match does not undo human calls. 4. `recon report --period 2026-07 --out report.html` renders: opening balance, matched total, unmatched bank items, unmatched ledger items, tagged differences, and the closing difference that must reconcile to zero. Include a plain-text summary printed to stdout. 5. `recon rules` reads rules.yml for auto-tagging patterns (regex on description to category), applied during match. Explicitly out of scope: multi-entity consolidation, FX revaluation, journal entry posting, approval workflows, ERP connectors, anything claiming to be audit evidence. Deliverables: README with a worked example using two generated sample CSVs, mapping.yml and rules.yml examples, tests covering each matching pass plus the many-to-one case, and a Makefile with install/test/demo targets. Config paths and thresholds in .env or config.yml, never hardcoded. Print a one-line disclaimer in the report footer: this is a personal tool, not audit evidence. ## Required capabilities - Python 3.11 and a local terminal - CSV exports from your bank and your accounting system - Willingness to hand-tune matching rules for your own transaction descriptions - No auditor who needs to sign off on the result ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of BlackLine. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # BlackLine indie build ## Goal Build the smallest trustworthy replacement for the core BlackLine workflow for one developer or a tiny team. ## Scope Ingest a bank CSV and a ledger CSV, auto-match on amount plus date tolerance plus reference fuzz, and produce a reconciliation report listing matched pairs, unmatched items on each side, and a running difference. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Audit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification - SOX and internal-control reporting that auditors already know how to read - Live ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports - Multi-entity and intercompany handling, FX, and consolidation-scale volumes - Someone accountable when the numbers are wrong at year end If those capabilities are essential, use BlackLine instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local, single-user bank reconciliation tool. No accounts, no cloud, no telemetry, no hosted anything. Stack, non-negotiable: - Python 3.11 - Typer for the CLI - pandas for CSV handling - SQLite via sqlite3 for persistence (file: recon.db) - Jinja2 to render a static HTML report - pytest for tests No web server, no auth, no Docker, no external API calls. What it does: 1. `recon import --side bank --file path.csv` and `--side ledger` ingest CSVs into SQLite. Support column mapping via a mapping.yml so the user can point date/amount/description/reference at their own headers. Store a stable row hash so re-importing the same file does not duplicate rows. 2. `recon match --period 2026-07` runs matching in passes, most confident first: (a) exact amount + exact reference, (b) exact amount within a configurable date window (default 3 days), (c) exact amount + fuzzy description via difflib ratio above a threshold, (d) many-to-one sums where several ledger lines total one bank line, capped at 4 lines to keep it tractable. Every match records the pass that made it and a confidence score. 3. `recon review --period 2026-07` interactive CLI: step through low-confidence and unmatched items, accept, reject, or tag as a known timing difference or fee. Decisions persist so re-running match does not undo human calls. 4. `recon report --period 2026-07 --out report.html` renders: opening balance, matched total, unmatched bank items, unmatched ledger items, tagged differences, and the closing difference that must reconcile to zero. Include a plain-text summary printed to stdout. 5. `recon rules` reads rules.yml for auto-tagging patterns (regex on description to category), applied during match. Explicitly out of scope: multi-entity consolidation, FX revaluation, journal entry posting, approval workflows, ERP connectors, anything claiming to be audit evidence. Deliverables: README with a worked example using two generated sample CSVs, mapping.yml and rules.yml examples, tests covering each matching pass plus the many-to-one case, and a Makefile with install/test/demo targets. Config paths and thresholds in .env or config.yml, never hardcoded. Print a one-line disclaimer in the report footer: this is a personal tool, not audit evidence. ## Required capabilities - Python 3.11 and a local terminal - CSV exports from your bank and your accounting system - Willingness to hand-tune matching rules for your own transaction descriptions - No auditor who needs to sign off on the result ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of BlackLine. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # BlackLine product brief ## Problem You can absolutely build a transaction matcher: read two CSVs, fuzzy match on amount and date, flag the leftovers. That is maybe five percent of what BlackLine is being paid for. The rest is certified reconciliations with named preparers and reviewers, immutable audit trails an external auditor will accept, SOX control evidence, multi-entity intercompany elimination, and live connectors into SAP, Oracle and NetSuite that survive a chart-of-accounts change. A personal replacement is a category error here: nobody buys this for themselves, a controller buys it so the audit does not become a quarter-long forensic exercise. If you are a solo operator reconciling one bank account against one ledger, you never needed it anyway. ## Product outcome Ingest a bank CSV and a ledger CSV, auto-match on amount plus date tolerance plus reference fuzz, and produce a reconciliation report listing matched pairs, unmatched items on each side, and a running difference. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Python 3.11 and a local terminal - CSV exports from your bank and your accounting system - Willingness to hand-tune matching rules for your own transaction descriptions - No auditor who needs to sign off on the result ## Explicit non-goals for v1 - Audit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification - SOX and internal-control reporting that auditors already know how to read - Live ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports - Multi-entity and intercompany handling, FX, and consolidation-scale volumes - Someone accountable when the numbers are wrong at year end ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a local, single-user bank reconciliation tool. No accounts, no cloud, no telemetry, no hosted anything. Stack, non-negotiable: - Python 3.11 - Typer for the CLI - pandas for CSV handling - SQLite via sqlite3 for persistence (file: recon.db) - Jinja2 to render a static HTML report - pytest for tests No web server, no auth, no Docker, no external API calls. What it does: 1. `recon import --side bank --file path.csv` and `--side ledger` ingest CSVs into SQLite. Support column mapping via a mapping.yml so the user can point date/amount/description/reference at their own headers. Store a stable row hash so re-importing the same file does not duplicate rows. 2. `recon match --period 2026-07` runs matching in passes, most confident first: (a) exact amount + exact reference, (b) exact amount within a configurable date window (default 3 days), (c) exact amount + fuzzy description via difflib ratio above a threshold, (d) many-to-one sums where several ledger lines total one bank line, capped at 4 lines to keep it tractable. Every match records the pass that made it and a confidence score. 3. `recon review --period 2026-07` interactive CLI: step through low-confidence and unmatched items, accept, reject, or tag as a known timing difference or fee. Decisions persist so re-running match does not undo human calls. 4. `recon report --period 2026-07 --out report.html` renders: opening balance, matched total, unmatched bank items, unmatched ledger items, tagged differences, and the closing difference that must reconcile to zero. Include a plain-text summary printed to stdout. 5. `recon rules` reads rules.yml for auto-tagging patterns (regex on description to category), applied during match. Explicitly out of scope: multi-entity consolidation, FX revaluation, journal entry posting, approval workflows, ERP connectors, anything claiming to be audit evidence. Deliverables: README with a worked example using two generated sample CSVs, mapping.yml and rules.yml examples, tests covering each matching pass plus the many-to-one case, and a Makefile with install/test/demo targets. Config paths and thresholds in .env or config.yml, never hardcoded. Print a one-line disclaimer in the report footer: this is a personal tool, not audit evidence. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted BlackLine capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# BlackLine indie build ## Goal Build the smallest trustworthy replacement for the core BlackLine workflow for one developer or a tiny team. ## Scope Ingest a bank CSV and a ledger CSV, auto-match on amount plus date tolerance plus reference fuzz, and produce a reconciliation report listing matched pairs, unmatched items on each side, and a running difference. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - Audit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification - SOX and internal-control reporting that auditors already know how to read - Live ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports - Multi-entity and intercompany handling, FX, and consolidation-scale volumes - Someone accountable when the numbers are wrong at year end If those capabilities are essential, use BlackLine instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a local, single-user bank reconciliation tool. No accounts, no cloud, no telemetry, no hosted anything. Stack, non-negotiable: - Python 3.11 - Typer for the CLI - pandas for CSV handling - SQLite via sqlite3 for persistence (file: recon.db) - Jinja2 to render a static HTML report - pytest for tests No web server, no auth, no Docker, no external API calls. What it does: 1. `recon import --side bank --file path.csv` and `--side ledger` ingest CSVs into SQLite. Support column mapping via a mapping.yml so the user can point date/amount/description/reference at their own headers. Store a stable row hash so re-importing the same file does not duplicate rows. 2. `recon match --period 2026-07` runs matching in passes, most confident first: (a) exact amount + exact reference, (b) exact amount within a configurable date window (default 3 days), (c) exact amount + fuzzy description via difflib ratio above a threshold, (d) many-to-one sums where several ledger lines total one bank line, capped at 4 lines to keep it tractable. Every match records the pass that made it and a confidence score. 3. `recon review --period 2026-07` interactive CLI: step through low-confidence and unmatched items, accept, reject, or tag as a known timing difference or fee. Decisions persist so re-running match does not undo human calls. 4. `recon report --period 2026-07 --out report.html` renders: opening balance, matched total, unmatched bank items, unmatched ledger items, tagged differences, and the closing difference that must reconcile to zero. Include a plain-text summary printed to stdout. 5. `recon rules` reads rules.yml for auto-tagging patterns (regex on description to category), applied during match. Explicitly out of scope: multi-entity consolidation, FX revaluation, journal entry posting, approval workflows, ERP connectors, anything claiming to be audit evidence. Deliverables: README with a worked example using two generated sample CSVs, mapping.yml and rules.yml examples, tests covering each matching pass plus the many-to-one case, and a Makefile with install/test/demo targets. Config paths and thresholds in .env or config.yml, never hardcoded. Print a one-line disclaimer in the report footer: this is a personal tool, not audit evidence. ## Required capabilities - Python 3.11 and a local terminal - CSV exports from your bank and your accounting system - Willingness to hand-tune matching rules for your own transaction descriptions - No auditor who needs to sign off on the result ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# BlackLine product brief ## Problem You can absolutely build a transaction matcher: read two CSVs, fuzzy match on amount and date, flag the leftovers. That is maybe five percent of what BlackLine is being paid for. The rest is certified reconciliations with named preparers and reviewers, immutable audit trails an external auditor will accept, SOX control evidence, multi-entity intercompany elimination, and live connectors into SAP, Oracle and NetSuite that survive a chart-of-accounts change. A personal replacement is a category error here: nobody buys this for themselves, a controller buys it so the audit does not become a quarter-long forensic exercise. If you are a solo operator reconciling one bank account against one ledger, you never needed it anyway. ## Product outcome Ingest a bank CSV and a ledger CSV, auto-match on amount plus date tolerance plus reference fuzz, and produce a reconciliation report listing matched pairs, unmatched items on each side, and a running difference. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Python 3.11 and a local terminal - CSV exports from your bank and your accounting system - Willingness to hand-tune matching rules for your own transaction descriptions - No auditor who needs to sign off on the result ## Explicit non-goals for v1 - Audit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification - SOX and internal-control reporting that auditors already know how to read - Live ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports - Multi-entity and intercompany handling, FX, and consolidation-scale volumes - Someone accountable when the numbers are wrong at year end ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a local, single-user bank reconciliation tool. No accounts, no cloud, no telemetry, no hosted anything. Stack, non-negotiable: - Python 3.11 - Typer for the CLI - pandas for CSV handling - SQLite via sqlite3 for persistence (file: recon.db) - Jinja2 to render a static HTML report - pytest for tests No web server, no auth, no Docker, no external API calls. What it does: 1. `recon import --side bank --file path.csv` and `--side ledger` ingest CSVs into SQLite. Support column mapping via a mapping.yml so the user can point date/amount/description/reference at their own headers. Store a stable row hash so re-importing the same file does not duplicate rows. 2. `recon match --period 2026-07` runs matching in passes, most confident first: (a) exact amount + exact reference, (b) exact amount within a configurable date window (default 3 days), (c) exact amount + fuzzy description via difflib ratio above a threshold, (d) many-to-one sums where several ledger lines total one bank line, capped at 4 lines to keep it tractable. Every match records the pass that made it and a confidence score. 3. `recon review --period 2026-07` interactive CLI: step through low-confidence and unmatched items, accept, reject, or tag as a known timing difference or fee. Decisions persist so re-running match does not undo human calls. 4. `recon report --period 2026-07 --out report.html` renders: opening balance, matched total, unmatched bank items, unmatched ledger items, tagged differences, and the closing difference that must reconcile to zero. Include a plain-text summary printed to stdout. 5. `recon rules` reads rules.yml for auto-tagging patterns (regex on description to category), applied during match. Explicitly out of scope: multi-entity consolidation, FX revaluation, journal entry posting, approval workflows, ERP connectors, anything claiming to be audit evidence. Deliverables: README with a worked example using two generated sample CSVs, mapping.yml and rules.yml examples, tests covering each matching pass plus the many-to-one case, and a Makefile with install/test/demo targets. Config paths and thresholds in .env or config.yml, never hardcoded. Print a one-line disclaimer in the report footer: this is a personal tool, not audit evidence. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted BlackLine capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
Because the buyer is not optimizing for cost, they are optimizing for not being the reason the audit slips. BlackLine sits between the ERP and the auditor and gives a controller a defensible story: every reconciliation was prepared by someone, reviewed by someone else, on a date, with the supporting file attached and the period locked afterward. Rebuilding the matching logic is easy; rebuilding fifteen years of auditors accepting the artifact is not. Add ERP connectors that have already survived thousands of messy chart-of-accounts migrations, and the switching cost is the entire close calendar.
xAudit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification
xSOX and internal-control reporting that auditors already know how to read
xLive ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports
xMulti-entity and intercompany handling, FX, and consolidation-scale volumes
xSomeone accountable when the numbers are wrong at year end
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced BlackLine with one prompt"
Vibecode BlackLine
Not really. BlackLine's value is not the code: The moat is compliance: BlackLine sells an artifact external auditors already trust, and no local script inherits that. See the honest breakdown above.
How much does BlackLine cost?
BlackLine's pricing is usage-based or varies by plan · No pricing page exists: www.blackline.com/pricing/ returns 500/Not Found. Only a 'Schedule a demo' path..
What do I lose by replacing BlackLine?
Honestly: Audit-defensible evidence: preparer/reviewer sign-off, timestamps, locked periods, nothing editable after certification; SOX and internal-control reporting that auditors already know how to read; Live ERP integrations (SAP, Oracle, NetSuite, Dynamics) instead of manual CSV exports; Multi-entity and intercompany handling, FX, and consolidation-scale volumes; Someone accountable when the numbers are wrong at year end. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to BlackLine?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.