Vibecode HikerAPI

You are building a lean indie version of HikerAPI.

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 =====
# HikerAPI indie build

## Goal

Build the smallest trustworthy replacement for the core HikerAPI workflow for one developer or a tiny team.

## Scope

A local FastAPI service that wraps instagrapi with one logged-in session, a proxy, aggressive request pacing, and a SQLite cache so you never fetch the same profile twice.

## 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:
- An account pool that absorbs bans so yours does not
- Residential proxy rotation and device fingerprint management
- Same-day fixes when the platform changes internal endpoints
- Real throughput: concurrent requests instead of one polite call every few seconds
- Coverage beyond Instagram, including TikTok and other networks

If those capabilities are essential, use HikerAPI 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 Instagram data service in an empty folder. Stack: Python 3.11, FastAPI, uvicorn, instagrapi, SQLite via sqlite3 (no ORM), httpx only if needed.

Goal: a small REST API on localhost that returns Instagram profile, media, and follower data for personal research, with heavy caching so I never fetch the same thing twice.

Endpoints:
- GET /user/{username} : profile info (id, full name, bio, counts, is_private, profile pic url)
- GET /user/{username}/media?limit=N : recent posts (id, code, caption, like/comment counts, taken_at, media urls)
- GET /user/{username}/followers?limit=N : follower list, capped at 200 per call
- GET /hashtag/{tag}/top : top media for a hashtag
- GET /cache/stats : row counts and cache hit counters

Rules:
- Credentials and proxy come from .env: IG_USERNAME, IG_PASSWORD, IG_PROXY. Ship a .env.example, never commit secrets.
- Persist the instagrapi session to session.json and reuse it on boot. Only re-login if the session is dead.
- One global request queue with a single worker. Sleep a random 4 to 12 seconds between platform calls. No concurrency, ever.
- Cache every response in SQLite keyed by endpoint plus params, with a fetched_at timestamp and a TTL of 24 hours for profiles and 6 hours for media. Serve cache on hit and mark the response with a "cached": true field.
- On login challenge, 429, or ChallengeRequired, return HTTP 503 with a clear message telling me to log in manually in a browser on the same proxy. Do not retry in a loop.
- Log every platform call to a requests table so I can see exactly how much I hammered them.

Out of scope: no posting, liking, following, or DMs. No web UI beyond FastAPI's built-in docs. No account rotation, no proxy pool, no cloud deploy, no telemetry.

Deliverables: main.py, db.py, client.py, .env.example, requirements.txt, and a README that says plainly that this account will probably get banned and that the TTLs and sleeps are the only thing keeping it alive.

## Required capabilities

- A throwaway Instagram account you are willing to lose
- A residential or mobile proxy, datacenter IPs get flagged fast
- Python 3.11 and patience for session challenge prompts
- Acceptance that any of this can break without warning

## 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 HikerAPI.

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 =====
# HikerAPI indie build

## Goal

Build the smallest trustworthy replacement for the core HikerAPI workflow for one developer or a tiny team.

## Scope

A local FastAPI service that wraps instagrapi with one logged-in session, a proxy, aggressive request pacing, and a SQLite cache so you never fetch the same profile twice.

## 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:
- An account pool that absorbs bans so yours does not
- Residential proxy rotation and device fingerprint management
- Same-day fixes when the platform changes internal endpoints
- Real throughput: concurrent requests instead of one polite call every few seconds
- Coverage beyond Instagram, including TikTok and other networks

If those capabilities are essential, use HikerAPI 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 Instagram data service in an empty folder. Stack: Python 3.11, FastAPI, uvicorn, instagrapi, SQLite via sqlite3 (no ORM), httpx only if needed.

Goal: a small REST API on localhost that returns Instagram profile, media, and follower data for personal research, with heavy caching so I never fetch the same thing twice.

Endpoints:
- GET /user/{username} : profile info (id, full name, bio, counts, is_private, profile pic url)
- GET /user/{username}/media?limit=N : recent posts (id, code, caption, like/comment counts, taken_at, media urls)
- GET /user/{username}/followers?limit=N : follower list, capped at 200 per call
- GET /hashtag/{tag}/top : top media for a hashtag
- GET /cache/stats : row counts and cache hit counters

Rules:
- Credentials and proxy come from .env: IG_USERNAME, IG_PASSWORD, IG_PROXY. Ship a .env.example, never commit secrets.
- Persist the instagrapi session to session.json and reuse it on boot. Only re-login if the session is dead.
- One global request queue with a single worker. Sleep a random 4 to 12 seconds between platform calls. No concurrency, ever.
- Cache every response in SQLite keyed by endpoint plus params, with a fetched_at timestamp and a TTL of 24 hours for profiles and 6 hours for media. Serve cache on hit and mark the response with a "cached": true field.
- On login challenge, 429, or ChallengeRequired, return HTTP 503 with a clear message telling me to log in manually in a browser on the same proxy. Do not retry in a loop.
- Log every platform call to a requests table so I can see exactly how much I hammered them.

Out of scope: no posting, liking, following, or DMs. No web UI beyond FastAPI's built-in docs. No account rotation, no proxy pool, no cloud deploy, no telemetry.

Deliverables: main.py, db.py, client.py, .env.example, requirements.txt, and a README that says plainly that this account will probably get banned and that the TTLs and sleeps are the only thing keeping it alive.

## Required capabilities

- A throwaway Instagram account you are willing to lose
- A residential or mobile proxy, datacenter IPs get flagged fast
- Python 3.11 and patience for session challenge prompts
- Acceptance that any of this can break without warning

## 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 HikerAPI.

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 =====
# HikerAPI product brief

## Problem

The code here is the easy part: instagrapi and friends are open source, and an agent can wrap them in a REST API in an afternoon. What you cannot one-shot is a farm of aged accounts, residential proxy rotation, fingerprint churn, and a team that patches the client every time Instagram quietly changes an internal endpoint or ships a new challenge flow. Your single account behind your home IP will hit rate limits, then checkpoints, then a permanent ban, usually in that order and usually on the day you need data. A personal build is fine for pulling a few hundred profiles once. It is not fine for anything that has to keep working next month.

## Product outcome

A local FastAPI service that wraps instagrapi with one logged-in session, a proxy, aggressive request pacing, and a SQLite cache so you never fetch the same profile twice.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- A throwaway Instagram account you are willing to lose
- A residential or mobile proxy, datacenter IPs get flagged fast
- Python 3.11 and patience for session challenge prompts
- Acceptance that any of this can break without warning

## Explicit non-goals for v1

- An account pool that absorbs bans so yours does not
- Residential proxy rotation and device fingerprint management
- Same-day fixes when the platform changes internal endpoints
- Real throughput: concurrent requests instead of one polite call every few seconds
- Coverage beyond Instagram, including TikTok and other networks

## 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 Instagram data service in an empty folder. Stack: Python 3.11, FastAPI, uvicorn, instagrapi, SQLite via sqlite3 (no ORM), httpx only if needed.

Goal: a small REST API on localhost that returns Instagram profile, media, and follower data for personal research, with heavy caching so I never fetch the same thing twice.

Endpoints:
- GET /user/{username} : profile info (id, full name, bio, counts, is_private, profile pic url)
- GET /user/{username}/media?limit=N : recent posts (id, code, caption, like/comment counts, taken_at, media urls)
- GET /user/{username}/followers?limit=N : follower list, capped at 200 per call
- GET /hashtag/{tag}/top : top media for a hashtag
- GET /cache/stats : row counts and cache hit counters

Rules:
- Credentials and proxy come from .env: IG_USERNAME, IG_PASSWORD, IG_PROXY. Ship a .env.example, never commit secrets.
- Persist the instagrapi session to session.json and reuse it on boot. Only re-login if the session is dead.
- One global request queue with a single worker. Sleep a random 4 to 12 seconds between platform calls. No concurrency, ever.
- Cache every response in SQLite keyed by endpoint plus params, with a fetched_at timestamp and a TTL of 24 hours for profiles and 6 hours for media. Serve cache on hit and mark the response with a "cached": true field.
- On login challenge, 429, or ChallengeRequired, return HTTP 503 with a clear message telling me to log in manually in a browser on the same proxy. Do not retry in a loop.
- Log every platform call to a requests table so I can see exactly how much I hammered them.

Out of scope: no posting, liking, following, or DMs. No web UI beyond FastAPI's built-in docs. No account rotation, no proxy pool, no cloud deploy, no telemetry.

Deliverables: main.py, db.py, client.py, .env.example, requirements.txt, and a README that says plainly that this account will probably get banned and that the TTLs and sleeps are the only thing keeping it alive.

## 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 HikerAPI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# HikerAPI indie build

## Goal

Build the smallest trustworthy replacement for the core HikerAPI workflow for one developer or a tiny team.

## Scope

A local FastAPI service that wraps instagrapi with one logged-in session, a proxy, aggressive request pacing, and a SQLite cache so you never fetch the same profile twice.

## 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:
- An account pool that absorbs bans so yours does not
- Residential proxy rotation and device fingerprint management
- Same-day fixes when the platform changes internal endpoints
- Real throughput: concurrent requests instead of one polite call every few seconds
- Coverage beyond Instagram, including TikTok and other networks

If those capabilities are essential, use HikerAPI instead of pretending the gap is solved.

# 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

## Original build brief

Build a local, single-user Instagram data service in an empty folder. Stack: Python 3.11, FastAPI, uvicorn, instagrapi, SQLite via sqlite3 (no ORM), httpx only if needed.

Goal: a small REST API on localhost that returns Instagram profile, media, and follower data for personal research, with heavy caching so I never fetch the same thing twice.

Endpoints:
- GET /user/{username} : profile info (id, full name, bio, counts, is_private, profile pic url)
- GET /user/{username}/media?limit=N : recent posts (id, code, caption, like/comment counts, taken_at, media urls)
- GET /user/{username}/followers?limit=N : follower list, capped at 200 per call
- GET /hashtag/{tag}/top : top media for a hashtag
- GET /cache/stats : row counts and cache hit counters

Rules:
- Credentials and proxy come from .env: IG_USERNAME, IG_PASSWORD, IG_PROXY. Ship a .env.example, never commit secrets.
- Persist the instagrapi session to session.json and reuse it on boot. Only re-login if the session is dead.
- One global request queue with a single worker. Sleep a random 4 to 12 seconds between platform calls. No concurrency, ever.
- Cache every response in SQLite keyed by endpoint plus params, with a fetched_at timestamp and a TTL of 24 hours for profiles and 6 hours for media. Serve cache on hit and mark the response with a "cached": true field.
- On login challenge, 429, or ChallengeRequired, return HTTP 503 with a clear message telling me to log in manually in a browser on the same proxy. Do not retry in a loop.
- Log every platform call to a requests table so I can see exactly how much I hammered them.

Out of scope: no posting, liking, following, or DMs. No web UI beyond FastAPI's built-in docs. No account rotation, no proxy pool, no cloud deploy, no telemetry.

Deliverables: main.py, db.py, client.py, .env.example, requirements.txt, and a README that says plainly that this account will probably get banned and that the TTLs and sleeps are the only thing keeping it alive.

## Required capabilities

- A throwaway Instagram account you are willing to lose
- A residential or mobile proxy, datacenter IPs get flagged fast
- Python 3.11 and patience for session challenge prompts
- Acceptance that any of this can break without warning

## 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.

# 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.

# HikerAPI product brief

## Problem

The code here is the easy part: instagrapi and friends are open source, and an agent can wrap them in a REST API in an afternoon. What you cannot one-shot is a farm of aged accounts, residential proxy rotation, fingerprint churn, and a team that patches the client every time Instagram quietly changes an internal endpoint or ships a new challenge flow. Your single account behind your home IP will hit rate limits, then checkpoints, then a permanent ban, usually in that order and usually on the day you need data. A personal build is fine for pulling a few hundred profiles once. It is not fine for anything that has to keep working next month.

## Product outcome

A local FastAPI service that wraps instagrapi with one logged-in session, a proxy, aggressive request pacing, and a SQLite cache so you never fetch the same profile twice.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- A throwaway Instagram account you are willing to lose
- A residential or mobile proxy, datacenter IPs get flagged fast
- Python 3.11 and patience for session challenge prompts
- Acceptance that any of this can break without warning

## Explicit non-goals for v1

- An account pool that absorbs bans so yours does not
- Residential proxy rotation and device fingerprint management
- Same-day fixes when the platform changes internal endpoints
- Real throughput: concurrent requests instead of one polite call every few seconds
- Coverage beyond Instagram, including TikTok and other networks

## 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

## Starting brief

Build a local, single-user Instagram data service in an empty folder. Stack: Python 3.11, FastAPI, uvicorn, instagrapi, SQLite via sqlite3 (no ORM), httpx only if needed.

Goal: a small REST API on localhost that returns Instagram profile, media, and follower data for personal research, with heavy caching so I never fetch the same thing twice.

Endpoints:
- GET /user/{username} : profile info (id, full name, bio, counts, is_private, profile pic url)
- GET /user/{username}/media?limit=N : recent posts (id, code, caption, like/comment counts, taken_at, media urls)
- GET /user/{username}/followers?limit=N : follower list, capped at 200 per call
- GET /hashtag/{tag}/top : top media for a hashtag
- GET /cache/stats : row counts and cache hit counters

Rules:
- Credentials and proxy come from .env: IG_USERNAME, IG_PASSWORD, IG_PROXY. Ship a .env.example, never commit secrets.
- Persist the instagrapi session to session.json and reuse it on boot. Only re-login if the session is dead.
- One global request queue with a single worker. Sleep a random 4 to 12 seconds between platform calls. No concurrency, ever.
- Cache every response in SQLite keyed by endpoint plus params, with a fetched_at timestamp and a TTL of 24 hours for profiles and 6 hours for media. Serve cache on hit and mark the response with a "cached": true field.
- On login challenge, 429, or ChallengeRequired, return HTTP 503 with a clear message telling me to log in manually in a browser on the same proxy. Do not retry in a loop.
- Log every platform call to a requests table so I can see exactly how much I hammered them.

Out of scope: no posting, liking, following, or DMs. No web UI beyond FastAPI's built-in docs. No account rotation, no proxy pool, no cloud deploy, no telemetry.

Deliverables: main.py, db.py, client.py, .env.example, requirements.txt, and a README that says plainly that this account will probably get banned and that the TTLs and sleeps are the only thing keeping it alive.

## 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.

# 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.

# 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

## 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 HikerAPI 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