Adds a full MkDocs documentation site under docs/ with Material theme. Getting Started: installation walkthrough, 7-step first-run wizard guide, Docker Compose profile reference with GPU memory guidance and preflight.py description. User Guide: job discovery (search profiles, custom boards, enrichment), job review (sorting, match scores, batch actions), apply workspace (cover letter gen, PDF export, mark applied), interviews (kanban stages, company research auto-trigger, survey assistant), email sync (IMAP, Gmail App Password, classification labels, stage auto-updates), integrations (all 13 drivers with tier requirements), settings (every tab documented). Developer Guide: contributing (dev env setup, code style, branch naming, PR checklist), architecture (ASCII layer diagram, design decisions), adding scrapers (full scrape() interface, registration, search profile config, test patterns), adding integrations (IntegrationBase full interface, auto- discovery, tier gating, test patterns), testing (patterns, fixtures, what not to test). Reference: tier system (full FEATURES table, can_use/tier_label API, dev override, adding gates), LLM router (backend types, complete() signature, fallback chains, vision routing, __auto__ resolution, adding backends), config files (every file with field-level docs and gitignore status). Also adds CONTRIBUTING.md at repo root pointing to the docs site.
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Tier System
Peregrine uses a three-tier feature gate system defined in app/wizard/tiers.py.
Tiers
free < paid < premium
| Tier | Description |
|---|---|
free |
Core discovery pipeline, resume matching, and basic UI — no LLM features |
paid |
All AI features: cover letters, research, email, integrations, calendar, notifications |
premium |
Adds fine-tuning and multi-user support |
Feature Gate Table
Features listed here require a minimum tier. Features not in this table are available to all tiers (free by default).
Wizard LLM generation
| Feature key | Minimum tier | Description |
|---|---|---|
llm_career_summary |
paid | LLM-assisted career summary generation in the wizard |
llm_expand_bullets |
paid | LLM expansion of resume bullet points |
llm_suggest_skills |
paid | LLM skill suggestions from resume content |
llm_voice_guidelines |
premium | LLM writing voice and tone guidelines |
llm_job_titles |
paid | LLM-suggested job title variations for search |
llm_keywords_blocklist |
paid | LLM-suggested blocklist keywords |
llm_mission_notes |
paid | LLM-generated mission alignment notes |
App features
| Feature key | Minimum tier | Description |
|---|---|---|
company_research |
paid | Auto-generated company research briefs pre-interview |
interview_prep |
paid | Live reference sheet and practice Q&A during calls |
email_classifier |
paid | IMAP email sync with LLM classification |
survey_assistant |
paid | Culture-fit survey Q&A helper (text + screenshot) |
model_fine_tuning |
premium | Cover letter model fine-tuning on personal writing |
shared_cover_writer_model |
paid | Access to shared fine-tuned cover letter model |
multi_user |
premium | Multiple user profiles on one instance |
Integrations (paid)
| Feature key | Minimum tier | Description |
|---|---|---|
notion_sync |
paid | Sync jobs to Notion database |
google_sheets_sync |
paid | Sync jobs to Google Sheets |
airtable_sync |
paid | Sync jobs to Airtable |
google_calendar_sync |
paid | Create interview events in Google Calendar |
apple_calendar_sync |
paid | Create interview events in Apple Calendar (CalDAV) |
slack_notifications |
paid | Pipeline event notifications via Slack |
Free integrations (not gated)
The following integrations are free for all tiers and are not in the FEATURES dict:
google_drive_sync— upload documents to Google Drivedropbox_sync— upload documents to Dropboxonedrive_sync— upload documents to OneDrivemega_sync— upload documents to MEGAnextcloud_sync— upload documents to Nextclouddiscord_notifications— pipeline notifications via Discord webhookhome_assistant— pipeline events to Home Assistant REST API
API Reference
can_use(tier, feature) -> bool
Returns True if the given tier has access to the feature.
from app.wizard.tiers import can_use
can_use("free", "company_research") # False
can_use("paid", "company_research") # True
can_use("premium", "company_research") # True
can_use("free", "unknown_feature") # True — ungated features return True
can_use("invalid", "company_research") # False — invalid tier string
tier_label(feature) -> str
Returns a display badge string for locked features, or "" if the feature is free or unknown.
from app.wizard.tiers import tier_label
tier_label("company_research") # "🔒 Paid"
tier_label("model_fine_tuning") # "⭐ Premium"
tier_label("job_discovery") # "" (ungated)
Dev Tier Override
For local development and testing without a paid licence, set dev_tier_override in config/user.yaml:
tier: free
dev_tier_override: premium # overrides tier locally for testing
UserProfile.tier returns dev_tier_override when set, falling back to tier otherwise.
!!! warning
dev_tier_override is for local development only. It has no effect on production deployments that validate licences server-side.
Adding a New Feature Gate
- Add the feature to
FEATURESinapp/wizard/tiers.py:
FEATURES: dict[str, str] = {
# ...existing entries...
"my_new_feature": "paid", # or "free" | "premium"
}
- Guard the feature in the UI:
from app.wizard.tiers import can_use, tier_label
from scripts.user_profile import UserProfile
user = UserProfile()
if can_use(user.tier, "my_new_feature"):
# show the feature
pass
else:
st.info(f"My New Feature requires a {tier_label('my_new_feature').replace('🔒 ', '').replace('⭐ ', '')} plan.")
- Add a test in
tests/test_tiers.py:
def test_my_new_feature_requires_paid():
assert can_use("free", "my_new_feature") is False
assert can_use("paid", "my_new_feature") is True
assert can_use("premium", "my_new_feature") is True
Future: Ultra Tier
An ultra tier is reserved for future use (e.g. enterprise SLA, dedicated inference). The tier ordering in TIERS = ["free", "paid", "premium"] can be extended without breaking can_use(), since it uses list.index() for comparison.