What Flows just did
Based on your request to build Vector vs Markdown Memory: Make the Decision, Flows created 3 implementation stages and 9 checks.
You asked for: Run a structured decision process: profile my memory workload, benchmark file-based vs vector recall on my real data, and commit with revisit triggers.. Flows generated 3 stages because the application requires A workload profile: entry count, growth rate, query styles, inspectability needs; A benchmark on my real memory data: file/lexical recall vs vector recall; A decision matrix scoring both (plus the hybrid) on quality, ops, and debuggability. Each stage tells your AI builder what to do, tests whether it actually worked, and gives a repair instruction when it fails. These checks are designed to catch: Adopting a vector stack for 80 memories a grep would search perfectly; Dismissing vectors while users paraphrase every query and lexical misses them; Benchmarking on toy data that flatters one side.
Example failure
The form reported success, but no database record was created.
What Flows does
Flows detects the failure, generates a repair instruction, reruns the test, and stores the passing evidence.
Stages — Plan → Build → Test → Fix → Prove → Ship
3 stages · 9 checks · progressive disclosure — open a stage for details
Copy to external builder
Copies a prompt for Claude/Cursor/etc. Records prompt_copied only — not execution, commits, or checks.
Execute through Oort
Requires Sign in with Oort + a provider connection (BYOK). Keys stay on Oort. A model name in the dropdown is not the same as a live connection.
Next
Check off: Counts and growth measured, not guessed
Add a proof note on each check — a checkbox alone is weak proof.
Probes & advanced tools are under “More verification tools”. Experience level: Settings.
Project (saved with proof)
Project binding: Unbound — complete project binding before trusting this run · missing repositoryUrl, repositoryStartCommit, environment, stack
More verification tools (probes, timeline, adapters)Show
App persistence (create → read → assert token)
Body template: {"probe":"{{token}}","title":"flows-probe-{{token}}"}. Needs CORS-readable public create/list endpoints (or inconclusive). Badge only when create→read→refresh finds the token (detects false success).
Two-account authorization (User B cannot see/change User A)
Authorization evidence source: Cross-account ownership test (trust level 5/6). These are not equivalent. Unauthenticated probe alone is weaker (source: unauthenticated API probe). Tokens stay in this browser session only — not uploaded to Flows servers.
Generated edge-case tests (review / approve)
- api_probe Create then refresh still shows the record
- api_probe Edit then refresh keeps the edit
- api_probe Delete then refresh removes the record
- api_probe Nonexistent record ID fails cleanly
- cross_account_probe Cross-user record identifier is rejected
Generated from this route’s signals — you approve; you do not invent security tests.
Content probe (GET body contains text)
In-product content probe reads response text when CORS allows — not full DOM/click automation. Playwright script is optional and external.
Proof strength
thin · 0/100
Heuristic from notes + probes + gates — not a production certification.
Verification adapters
- ● Deploy URL probe · idle
- ● API path probe · idle
- ● Authz probe (unauth → 401/403) · idle
- ● Browser persistence · idle
- ● App persistence (create→read) · idle
- ● Content / body text probe · idle
- ○ In-product Playwright DOM · not embedded (download external script)
Filled circles = available here. Empty = not shipped. Probes are not a production audit. Adapter docs
Step 1 of 3
Not startedProfile the workload honestly
Scale and query style decide this - measure both.
Flows keeps plan, checks, failures, and next steps connected
Your AI tool writes or changes the code
Repository grounds the plan only after inspect
Checks always carry a validation tier
How this plan was created
Plan from project details
Flows creates steps using the goal, requirements, stack, platform, and details you provide.
- Project description
- Selected template / route
- User-entered stack
- Constraints and notes
- Vector vs Markdown Memory: Make the Decision
No repository inspection implied. Connect and inspect a codebase to ground steps in real files.
Codebase access
Checking GitHub…
Or paste a repository URL manually
Use these instructions in Claude, ChatGPT, Cursor, Emergent, or another builder.
Profile my memory workload. Quantify: current entry count and average length; growth per week (from session frequency); query styles from real usage - what fraction are exact lookups (names, errors, commands) vs paraphrases vs broad topical asks (sample 20 real queries if logs exist, else write 20 realistic ones); latency needs; and inspectability requirements (do humans read/edit memory? does it live in git?). Research context: practitioner systems report plain files serving well into the hundreds of entries when queries skew exact; vector value concentrates in paraphrase-heavy, large-store recall. Deliver the profile with the 20-query sample labeled by style.
Project context
Copies the project goal, technical details, current step, previous progress, and checks so your AI tool understands what it is working on.
Expected after this step
A quantified workload profile with a labeled query sample.
Should not happen
- ✕Adopting a vector stack for 80 memories a grep would search perfectly
- ✕Dismissing vectors while users paraphrase every query and lexical misses them
- ✕Benchmarking on toy data that flatters one side
- ✕A decision without triggers, silently outgrown a year later
Verify gate — prove this step works before continuing
Do not move on until every check is true. Add proof notes — a checked box alone is weak proof. Checks are manual by default; deploy/API probes live under “More verification tools” and do not auto-check boxes.
Do not continue if…
- !Adopting a vector stack for 80 memories a grep would search perfectly
- !Dismissing vectors while users paraphrase every query and lexical misses them
- !Benchmarking on toy data that flatters one side
- !A decision without triggers, silently outgrown a year later
Repair path — if this step fails
Use the Repair Prompt when a verify gate fails. Loop: Detected → Diagnosed → Repair issued → Changed → Retested → Resolved
Show repair prompt textShow
The query sample is invented to be interesting. Pull real queries from transcripts or recall logs - the exact/paraphrase ratio is the single most decision-relevant number here.
Your notes for this step
Definition of Done
Final route-level requirements. Manual checks — separate from per-step verify gates. Completing step gates does not auto-check these. Route completion is not a production-ready claim.