feat: add progression readiness api

This commit is contained in:
user
2026-04-26 16:39:19 +09:00
parent 600acf7303
commit a413f1ef15
16 changed files with 637 additions and 15 deletions

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@@ -38,14 +38,14 @@ interview-ready after each short practice loop.
### Progression
- [ ] **PROG-01**: User can see a role-specific readiness map.
- [ ] **PROG-02**: Concepts have challenge ladders from definition to interview
- [x] **PROG-01**: User can see a role-specific readiness map.
- [x] **PROG-02**: Concepts have challenge ladders from definition to interview
pressure.
- [ ] **PROG-03**: System selects next challenge based on learner memory and
- [x] **PROG-03**: System selects next challenge based on learner memory and
grading evidence.
- [ ] **PROG-04**: System unlocks boss-style integrated questions after
- [x] **PROG-04**: System unlocks boss-style integrated questions after
prerequisite stability.
- [ ] **PROG-05**: Streaks and rewards avoid punitive or gambling-like mechanics.
- [x] **PROG-05**: Streaks and rewards avoid punitive or gambling-like mechanics.
### Ontology and Learning Materials
@@ -97,7 +97,7 @@ interview-ready after each short practice loop.
| BACK-01..BACK-05 | Phase 1 | Complete |
| INT-01..INT-06 | Phase 2 | Complete |
| MEM-01..MEM-05 | Phase 3 | Complete |
| PROG-01..PROG-05 | Phase 4 | Pending |
| PROG-01..PROG-05 | Phase 4 | Complete |
| ONTO-01..ONTO-04 | Phase 5 | Pending |
| ASSET-01..ASSET-03 | Phase 6 | Pending |
@@ -108,4 +108,4 @@ interview-ready after each short practice loop.
---
*Requirements defined: 2026-04-26*
*Last updated: 2026-04-26 after Phase 3 execution.*
*Last updated: 2026-04-26 after Phase 4 execution.*

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@@ -7,7 +7,7 @@ See: `.planning/PROJECT.md` (updated 2026-04-26)
**Core value:** The user should feel and prove that they are becoming more
interview-ready after each short practice loop.
**Current focus:** Phase 4 planning: Progression.
**Current focus:** Phase 5 planning: Ontology and Learning Materials.
## Current Decisions
@@ -27,14 +27,16 @@ interview-ready after each short practice loop.
- Phase 3 learner memory is implemented and verified with evidence-backed
in-memory profiles, mastery, misconceptions, interventions, and review
schedules.
- Phase 4 progression is implemented and verified with readiness map and next
challenge APIs derived from learner memory evidence.
## Next Actions
1. Plan Phase 4 progression with GSD.
1. Plan Phase 5 ontology and learning material ingestion with GSD.
2. Keep `docs/planning/WORKFLOW_CONTRACTS.md` aligned with Go structs during
future workflow implementation.
3. Decide whether Phase 4 readiness map reads directly from learner memory or
introduces a derived progression projection.
3. Decide the MVP ontology storage boundary before accepting uploaded source
materials.
## Validation Log
@@ -51,6 +53,9 @@ interview-ready after each short practice loop.
- 2026-04-26: Phase 3 implementation verified with `go test ./...`,
`openspec validate bootstrap-job-tutor-platform --strict`, live diagnostic
answer to learner-memory smoke, and Go source line-count check.
- 2026-04-26: Phase 4 implementation verified with `go test ./...`,
`openspec validate bootstrap-job-tutor-platform --strict`, live readiness and
next-challenge smoke, and Go source line-count check.
---
*State initialized: 2026-04-26.*

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@@ -0,0 +1,39 @@
# Phase 4 Context: Progression
**Status:** Ready for execution
**Started:** 2026-04-26
## Goal
Expose visible, evidence-backed progression after diagnostic practice.
## Inputs
- OpenSpec `learning-progression` requirements.
- `docs/planning/GAMIFICATION.md`.
- Phase 3 learner memory snapshots.
- Existing workflow contracts for `NextChallenge` and `ReadinessUpdate`.
## Decisions
- Derive MVP readiness directly from learner memory.
- Keep progression read-only except for future workflow outputs.
- Do not add streak persistence yet.
- Rewards and unlocks must be deterministic and evidence-backed.
## Boundaries
In scope:
- Role readiness map API.
- Concept ladder level calculation.
- Next challenge selection API.
- Boss-style unlock when prerequisite concepts are stable.
- Tests and verification.
Out of scope:
- Frontend map UI.
- Persistent campaign/streak storage.
- Social leaderboards.
- Random reward economy.

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@@ -0,0 +1,46 @@
# Phase 4 Plan: Progression
**Status:** Ready for execution
**Phase Goal:** Show evidence-backed readiness and select the next challenge.
## Requirements Covered
- PROG-01: User can see a role-specific readiness map.
- PROG-02: Concepts have challenge ladders from definition to interview
pressure.
- PROG-03: System selects next challenge based on learner memory and grading
evidence.
- PROG-04: System unlocks boss-style integrated questions after prerequisite
stability.
- PROG-05: Streaks and rewards avoid punitive or gambling-like mechanics.
## Tasks
### 1. Add progression package
- Define readiness map, concept progress, reward, and unlock types.
- Compute readiness from learner memory snapshots.
### 2. Add next challenge selection
- Select the weakest evidenced concept first.
- Use review schedule or misconception evidence to choose recovery difficulty.
- Produce typed `workflows.NextChallenge`.
### 3. Add HTTP endpoints
- `GET /api/v1/learners/{userID}/readiness-map`
- `GET /api/v1/learners/{userID}/next-challenge`
### 4. Add tests and verification
- Test readiness map projection.
- Test next challenge selection from weak memory.
- Test HTTP readiness flow after diagnostic answer.
- Run Go tests, OpenSpec validation, line-count check, and smoke.
## Out of Scope
- Frontend visualization.
- Persistent streak history.
- Multi-track progression graph.

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@@ -0,0 +1,32 @@
# Phase 4 Research: Progression
## Findings
Learner memory already stores the minimum evidence needed for progression:
- concept mastery state
- evidence references
- misconceptions
- review schedules
- interventions
The MVP progression surface can therefore be computed as a projection rather
than a new durable source of truth.
## Recommended Shape
- `internal/progression` owns readiness projection and challenge selection.
- `learnermemory.Service` remains the source for learner state.
- Readiness percentage should be simple and explainable.
- Challenge ladder should map readiness state to the next useful task:
- unknown/fragile: define or recovery
- improving: tradeoffs
- interview-ready: design constraints
- strong signal: interview pressure
- Boss unlock requires at least two stable concepts with evidence.
## Risks
- Too much gamification logic can become speculative. Keep it deterministic.
- Readiness percentages can feel fake if not traceable. Include evidence.
- Missing memory should return a normal 404, not invented progress.

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@@ -0,0 +1,35 @@
# Phase 4 Summary
**Status:** Complete
**Completed:** 2026-04-26
## Delivered
- Added `internal/progression` for readiness projection and next challenge
selection.
- Added role readiness map calculation from learner memory evidence.
- Added deterministic challenge ladder mapping.
- Added evidence-backed rewards and boss-question unlocks.
- Added HTTP endpoints:
- `GET /api/v1/learners/{userID}/readiness-map`
- `GET /api/v1/learners/{userID}/next-challenge`
- Added progression unit tests and HTTP flow coverage.
## Verification
```powershell
gofmt -w cmd internal
go test ./...
openspec validate bootstrap-job-tutor-platform --strict
```
Additional smoke check:
- Diagnostic create/answer followed by readiness-map and next-challenge reads
returned readiness `75`, one concept, and a typed challenge.
## Deferred
- Frontend readiness visualization.
- Persistent campaign and streak state.
- Multi-concept cluster graph beyond simple stable-count boss unlock.

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@@ -0,0 +1,28 @@
# Phase 4 Verification
## Verdict
PASS
## Requirement Coverage
- PROG-01: PASS. Readiness map API returns learner concept readiness.
- PROG-02: PASS. Each concept maps to a challenge ladder level.
- PROG-03: PASS. Next challenge selection targets the weakest evidenced
learner-memory concept.
- PROG-04: PASS. Boss unlocks are produced only from stable evidenced concepts.
- PROG-05: PASS. Rewards are deterministic, evidence-backed, and do not punish
missed days or use random reward mechanics.
## Evidence
- `go test ./...` passed.
- `openspec validate bootstrap-job-tutor-platform --strict` passed.
- Live diagnostic create/answer plus readiness and next-challenge smoke passed.
- Go source line-count check passed.
## Residual Risk
Progression is currently an in-memory projection. It is enough for MVP proof but
will need persisted campaign state before real streaks or long-running
readiness histories.

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@@ -7,6 +7,7 @@ import (
"tutor/internal/httpapi"
"tutor/internal/interview"
"tutor/internal/learnermemory"
"tutor/internal/progression"
"tutor/internal/workflows"
)
@@ -14,8 +15,9 @@ func NewServer(cfg config.Config) *http.Server {
runner := workflows.NewStubRunner()
store := interview.NewMemoryStore()
memory := learnermemory.NewService(learnermemory.NewMemoryStore())
progress := progression.NewService(memory)
service := interview.NewService(store, runner, memory)
handler := httpapi.NewHandler(cfg, service, memory)
handler := httpapi.NewHandler(cfg, service, memory, progress)
return &http.Server{
Addr: cfg.HTTPAddr,

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@@ -10,13 +10,15 @@ import (
"tutor/internal/config"
"tutor/internal/interview"
"tutor/internal/learnermemory"
"tutor/internal/progression"
"tutor/internal/workflows"
)
func TestDiagnosticHTTPFlow(t *testing.T) {
memory := learnermemory.NewService(learnermemory.NewMemoryStore())
service := interview.NewService(interview.NewMemoryStore(), workflows.NewStubRunner(), memory)
handler := NewHandler(config.Config{Environment: "test", ModelKey: "deepseek-v4-flash"}, service, memory)
progress := progression.NewService(memory)
handler := NewHandler(config.Config{Environment: "test", ModelKey: "deepseek-v4-flash"}, service, memory, progress)
routes := handler.Routes()
createBody := bytes.NewBufferString(`{
@@ -93,4 +95,20 @@ func TestDiagnosticHTTPFlow(t *testing.T) {
if len(snapshot.Mastery) == 0 {
t.Fatal("expected mastery entries")
}
readinessReq := httptest.NewRequest(http.MethodGet, "/api/v1/learners/user-1/readiness-map", nil)
readinessRec := httptest.NewRecorder()
routes.ServeHTTP(readinessRec, readinessReq)
if readinessRec.Code != http.StatusOK {
t.Fatalf("readiness status = %d, body = %s", readinessRec.Code, readinessRec.Body.String())
}
challengeReq := httptest.NewRequest(http.MethodGet, "/api/v1/learners/user-1/next-challenge", nil)
challengeRec := httptest.NewRecorder()
routes.ServeHTTP(challengeRec, challengeReq)
if challengeRec.Code != http.StatusOK {
t.Fatalf("challenge status = %d, body = %s", challengeRec.Code, challengeRec.Body.String())
}
}

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@@ -7,19 +7,27 @@ import (
"tutor/internal/config"
"tutor/internal/interview"
"tutor/internal/learnermemory"
"tutor/internal/progression"
)
type Handler struct {
cfg config.Config
diagnostic *interview.Service
memory *learnermemory.Service
progress *progression.Service
}
func NewHandler(cfg config.Config, diagnostic *interview.Service, memory *learnermemory.Service) Handler {
func NewHandler(
cfg config.Config,
diagnostic *interview.Service,
memory *learnermemory.Service,
progress *progression.Service,
) Handler {
return Handler{
cfg: cfg,
diagnostic: diagnostic,
memory: memory,
progress: progress,
}
}
@@ -30,6 +38,8 @@ func (h Handler) Routes() http.Handler {
mux.HandleFunc("GET /api/v1/diagnostic-sessions/{id}", h.getDiagnosticSession)
mux.HandleFunc("POST /api/v1/diagnostic-sessions/{id}/answers", h.submitDiagnosticAnswer)
mux.HandleFunc("GET /api/v1/learners/{userID}/memory", h.getLearnerMemory)
mux.HandleFunc("GET /api/v1/learners/{userID}/readiness-map", h.getReadinessMap)
mux.HandleFunc("GET /api/v1/learners/{userID}/next-challenge", h.getNextChallenge)
return mux
}

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@@ -9,6 +9,7 @@ import (
"tutor/internal/config"
"tutor/internal/interview"
"tutor/internal/learnermemory"
"tutor/internal/progression"
"tutor/internal/workflows"
)
@@ -19,7 +20,8 @@ func TestHealth(t *testing.T) {
}
memory := learnermemory.NewService(learnermemory.NewMemoryStore())
service := interview.NewService(interview.NewMemoryStore(), workflows.NewStubRunner(), memory)
handler := NewHandler(cfg, service, memory)
progress := progression.NewService(memory)
handler := NewHandler(cfg, service, memory, progress)
req := httptest.NewRequest(http.MethodGet, "/healthz", nil)
rec := httptest.NewRecorder()

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@@ -0,0 +1,46 @@
package httpapi
import (
"errors"
"net/http"
"tutor/internal/learnermemory"
)
func (h Handler) getReadinessMap(w http.ResponseWriter, r *http.Request) {
if h.progress == nil {
writeError(w, http.StatusNotFound, "progression not configured")
return
}
readiness, err := h.progress.ReadinessMap(r.PathValue("userID"))
if errors.Is(err, learnermemory.ErrProfileNotFound) {
writeError(w, http.StatusNotFound, "learner memory not found")
return
}
if err != nil {
writeError(w, http.StatusBadRequest, err.Error())
return
}
writeJSON(w, http.StatusOK, readiness)
}
func (h Handler) getNextChallenge(w http.ResponseWriter, r *http.Request) {
if h.progress == nil {
writeError(w, http.StatusNotFound, "progression not configured")
return
}
challenge, err := h.progress.NextChallenge(r.PathValue("userID"))
if errors.Is(err, learnermemory.ErrProfileNotFound) {
writeError(w, http.StatusNotFound, "learner memory not found")
return
}
if err != nil {
writeError(w, http.StatusBadRequest, err.Error())
return
}
writeJSON(w, http.StatusOK, challenge)
}

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@@ -0,0 +1,207 @@
package progression
import (
"errors"
"sort"
"strings"
"tutor/internal/learnermemory"
"tutor/internal/workflows"
)
type Service struct {
memory *learnermemory.Service
}
func NewService(memory *learnermemory.Service) *Service {
return &Service{memory: memory}
}
func (s *Service) ReadinessMap(userID string) (ReadinessMap, error) {
snapshot, err := s.snapshot(userID)
if err != nil {
return ReadinessMap{}, err
}
concepts := make([]ConceptProgress, 0, len(snapshot.Mastery))
for _, mastery := range snapshot.Mastery {
concepts = append(concepts, ConceptProgress{
Concept: mastery.Concept,
State: mastery.State,
LadderLevel: ladderForState(mastery.State),
NextAction: actionForState(mastery.State),
Evidence: append([]workflows.EvidenceRef(nil), mastery.Evidence...),
})
}
sort.Slice(concepts, func(i, j int) bool {
return concepts[i].Concept.ID < concepts[j].Concept.ID
})
readiness := ReadinessMap{
UserID: snapshot.Profile.UserID,
Track: trackFromConcepts(concepts),
ReadinessPercentage: readinessPercentage(concepts),
Concepts: concepts,
Rewards: rewardsFromConcepts(concepts),
Unlocks: unlocksFromConcepts(concepts),
}
return readiness, nil
}
func (s *Service) NextChallenge(userID string) (workflows.NextChallenge, error) {
readiness, err := s.ReadinessMap(userID)
if err != nil {
return workflows.NextChallenge{}, err
}
if len(readiness.Concepts) == 0 {
return workflows.NextChallenge{}, errors.New("no learner memory concepts available")
}
target := readiness.Concepts[0]
for _, concept := range readiness.Concepts[1:] {
if readinessScore(concept.State) < readinessScore(target.State) {
target = concept
}
}
return workflows.NextChallenge{
UserID: readiness.UserID,
Track: readiness.Track,
Concept: target.Concept,
LadderLevel: target.LadderLevel,
Question: challengeQuestion(target),
Rationale: "Selected from the weakest evidenced learner-memory concept.",
DifficultyAction: workflowDifficulty(target.NextAction),
Evidence: append([]workflows.EvidenceRef(nil), target.Evidence...),
}, nil
}
func (s *Service) snapshot(userID string) (learnermemory.Snapshot, error) {
if s.memory == nil {
return learnermemory.Snapshot{}, errors.New("learner memory not configured")
}
if strings.TrimSpace(userID) == "" {
return learnermemory.Snapshot{}, errors.New("user_id is required")
}
return s.memory.Snapshot(userID)
}
func ladderForState(state workflows.ReadinessState) workflows.LadderLevel {
switch state {
case workflows.ReadinessFragile:
return workflows.LadderDefine
case workflows.ReadinessImproving:
return workflows.LadderTradeoffs
case workflows.ReadinessInterviewReady:
return workflows.LadderDesignConstraints
case workflows.ReadinessStrongSignal:
return workflows.LadderInterviewPressure
default:
return workflows.LadderDefine
}
}
func actionForState(state workflows.ReadinessState) DifficultyAction {
switch state {
case workflows.ReadinessFragile:
return ActionRecover
case workflows.ReadinessImproving:
return ActionHold
case workflows.ReadinessInterviewReady, workflows.ReadinessStrongSignal:
return ActionRaise
default:
return ActionLower
}
}
func workflowDifficulty(action DifficultyAction) workflows.DifficultyAction {
switch action {
case ActionRecover:
return workflows.DifficultyRecover
case ActionLower:
return workflows.DifficultyLower
case ActionRaise:
return workflows.DifficultyRaise
default:
return workflows.DifficultyHold
}
}
func readinessPercentage(concepts []ConceptProgress) int {
if len(concepts) == 0 {
return 0
}
total := 0
for _, concept := range concepts {
total += readinessScore(concept.State)
}
return total * 100 / (len(concepts) * 4)
}
func readinessScore(state workflows.ReadinessState) int {
switch state {
case workflows.ReadinessFragile:
return 1
case workflows.ReadinessImproving:
return 2
case workflows.ReadinessInterviewReady:
return 3
case workflows.ReadinessStrongSignal:
return 4
default:
return 0
}
}
func rewardsFromConcepts(concepts []ConceptProgress) []Reward {
rewards := []Reward{}
for _, concept := range concepts {
if len(concept.Evidence) == 0 || readinessScore(concept.State) < 2 {
continue
}
rewards = append(rewards, Reward{
Kind: RewardConceptProgress,
Label: "Evidence-backed progress on " + concept.Concept.Label,
Evidence: append([]workflows.EvidenceRef(nil), concept.Evidence...),
})
}
return rewards
}
func unlocksFromConcepts(concepts []ConceptProgress) []Unlock {
stable := []workflows.EvidenceRef{}
for _, concept := range concepts {
if readinessScore(concept.State) < 3 || len(concept.Evidence) == 0 {
continue
}
stable = append(stable, concept.Evidence...)
}
if len(stable) < 2 {
return []Unlock{}
}
return []Unlock{{
Kind: UnlockBossQuestion,
Label: "Integrated backend interview boss question",
Evidence: append([]workflows.EvidenceRef(nil), stable...),
}}
}
func trackFromConcepts(concepts []ConceptProgress) string {
if len(concepts) == 0 {
return ""
}
return concepts[0].Concept.Track
}
func challengeQuestion(concept ConceptProgress) string {
switch concept.LadderLevel {
case workflows.LadderTradeoffs:
return "Explain a production tradeoff for " + concept.Concept.Label + "."
case workflows.LadderDesignConstraints:
return "Design a constrained backend scenario that uses " + concept.Concept.Label + "."
case workflows.LadderInterviewPressure:
return "Answer a timed interview follow-up about " + concept.Concept.Label + "."
default:
return "Define " + concept.Concept.Label + " and give one concrete backend example."
}
}

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@@ -0,0 +1,97 @@
package progression
import (
"testing"
"tutor/internal/learnermemory"
"tutor/internal/workflows"
)
func TestReadinessMapUsesEvidenceBackedMemory(t *testing.T) {
service := seededService(t, workflows.ReadinessImproving)
readiness, err := service.ReadinessMap("user-1")
if err != nil {
t.Fatalf("ReadinessMap error: %v", err)
}
if readiness.ReadinessPercentage != 50 {
t.Fatalf("readiness = %d, want 50", readiness.ReadinessPercentage)
}
if len(readiness.Concepts) != 1 {
t.Fatalf("concepts = %d, want 1", len(readiness.Concepts))
}
if readiness.Concepts[0].LadderLevel != workflows.LadderTradeoffs {
t.Fatalf("ladder = %q", readiness.Concepts[0].LadderLevel)
}
if len(readiness.Rewards) != 1 {
t.Fatalf("rewards = %d, want 1", len(readiness.Rewards))
}
}
func TestNextChallengeTargetsWeakestConcept(t *testing.T) {
memory := learnermemory.NewService(learnermemory.NewMemoryStore())
if _, err := memory.EnsureProfile(learnermemory.ProfileInput{
UserID: "user-1",
TargetRole: "backend developer",
Stack: []string{"go"},
}); err != nil {
t.Fatalf("EnsureProfile error: %v", err)
}
evidence := []workflows.EvidenceRef{{Kind: workflows.EvidenceAnswer, ID: "a-1", Confidence: 1}}
if err := memory.ApplyCandidate(workflows.MemoryUpdateCandidate{
UserID: "user-1",
Updates: []workflows.MemoryUpdate{
{
Kind: workflows.MemoryConceptMastery,
Concept: workflows.ConceptRef{ID: "cache", Label: "Cache invalidation", Track: "backend-developer"},
ProposedState: workflows.ReadinessInterviewReady,
Evidence: evidence,
},
{
Kind: workflows.MemoryConceptMastery,
Concept: workflows.ConceptRef{ID: "indexes", Label: "Database indexes", Track: "backend-developer"},
ProposedState: workflows.ReadinessFragile,
Evidence: evidence,
},
},
}); err != nil {
t.Fatalf("ApplyCandidate error: %v", err)
}
challenge, err := NewService(memory).NextChallenge("user-1")
if err != nil {
t.Fatalf("NextChallenge error: %v", err)
}
if challenge.Concept.ID != "indexes" {
t.Fatalf("challenge concept = %q", challenge.Concept.ID)
}
if challenge.DifficultyAction != workflows.DifficultyRecover {
t.Fatalf("difficulty = %q", challenge.DifficultyAction)
}
}
func seededService(t *testing.T, state workflows.ReadinessState) *Service {
t.Helper()
memory := learnermemory.NewService(learnermemory.NewMemoryStore())
if _, err := memory.EnsureProfile(learnermemory.ProfileInput{
UserID: "user-1",
TargetRole: "backend developer",
Stack: []string{"go"},
}); err != nil {
t.Fatalf("EnsureProfile error: %v", err)
}
if err := memory.ApplyCandidate(workflows.MemoryUpdateCandidate{
UserID: "user-1",
Updates: []workflows.MemoryUpdate{
{
Kind: workflows.MemoryConceptMastery,
Concept: workflows.ConceptRef{ID: "idempotency", Label: "HTTP idempotency", Track: "backend-developer"},
ProposedState: state,
Evidence: []workflows.EvidenceRef{{Kind: workflows.EvidenceAnswer, ID: "a-1", Confidence: 1}},
},
},
}); err != nil {
t.Fatalf("ApplyCandidate error: %v", err)
}
return NewService(memory)
}

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@@ -0,0 +1,54 @@
package progression
import "tutor/internal/workflows"
type ReadinessMap struct {
UserID string `json:"user_id"`
Track string `json:"track"`
ReadinessPercentage int `json:"readiness_percentage"`
Concepts []ConceptProgress `json:"concepts"`
Rewards []Reward `json:"rewards"`
Unlocks []Unlock `json:"unlocks"`
}
type ConceptProgress struct {
Concept workflows.ConceptRef `json:"concept"`
State workflows.ReadinessState `json:"state"`
LadderLevel workflows.LadderLevel `json:"ladder_level"`
NextAction DifficultyAction `json:"next_action"`
Evidence []workflows.EvidenceRef `json:"evidence"`
}
type DifficultyAction string
const (
ActionRecover DifficultyAction = "recover"
ActionLower DifficultyAction = "lower"
ActionHold DifficultyAction = "hold"
ActionRaise DifficultyAction = "raise"
)
type Reward struct {
Kind RewardKind `json:"kind"`
Label string `json:"label"`
Evidence []workflows.EvidenceRef `json:"evidence"`
}
type RewardKind string
const (
RewardConceptProgress RewardKind = "concept_progress"
RewardReadiness RewardKind = "readiness"
)
type Unlock struct {
Kind UnlockKind `json:"kind"`
Label string `json:"label"`
Evidence []workflows.EvidenceRef `json:"evidence"`
}
type UnlockKind string
const (
UnlockBossQuestion UnlockKind = "boss_question"
)

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@@ -14,3 +14,4 @@
- [ ] 10. Draft the first `agent-farm-go` YAML workflow package.
- [x] 11. Validate the OpenSpec change.
- [x] 12. Implement evidence-backed learner memory ingestion and readback.
- [x] 13. Implement evidence-backed readiness map and next challenge APIs.