القائمة
Oral-reading-fluency assessment (EGRA/ASER-style) — the passage→record→transcribe→score pipeline, WCPM benchmarks, multilingual rules, and debugging.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Classroom-observation coaching pipeline — pluggable scoring frameworks (OECD/HOTS/TEACH/FICO), the async queue worker, lesson-plan integration, and session debugging.
Trace any feature end to end — webhook → handler → service → queue → worker → DB → storage → delivery — to find where a user's request actually broke. The map that points you at the right service, worker, table, and skill for each feature.
How the bot logs — structured single-line JSON to the console, correlation IDs that thread a request across handler/queue/worker, semantic events, and an optional external log backend. Use when adding logging or wiring observability.
Pre-merge defensive checks for recurring, predictable bug classes — run before merging code that (a) emits new interactive button/list/Flow IDs, (b) edits or sends a Flow, (c) writes new enum/status values, (d) chains Supabase update+filter+select, (e) sends to a dormant user, or (f) tracks files toxic to a fresh clone. Each class has a concrete grep/SQL pre-flight and the failure mode it prevents.
A/B testing via a Thompson-sampling multi-armed bandit. DB-driven tests (ab_tests / ab_test_variants / ab_test_events) that learn the best-performing variant online. Use when adding or measuring a message/content experiment.
Query the bot's Postgres database with a read-only role. Use when analysing user metrics, debugging DB state, or exploring the schema. Column names: phone_number (not phone), first_name (not name) in the users table.
| name | reading-assessment |
| description | Oral-reading-fluency assessment (EGRA/ASER-style) — the passage→record→transcribe→score pipeline, WCPM benchmarks, multilingual rules, and debugging. |
Up: .claude/CLAUDE.md (config & skills router) · See also: coaching, debugging
A teacher runs a student through a short oral-reading assessment: the bot generates a passage, the student reads it aloud, and the bot transcribes the audio, scores fluency against grade benchmarks, and returns a report with voice feedback.
passage-generation, transcription, pronunciation, fluency, analysis, comprehension, report, voice-feedback./reading command routed through bot/shared/handlers/text-message.handler.js.EGRA/ASER-style Oral Reading Fluency (ORF) assessment. Primary metric is WCPM (Words Correct Per Minute); alphabet assessments use LCPM (Letters Correct Per Minute). Passage types scale by difficulty: letters → words → sentences → paragraphs → story.
compareToBenchmarks → the check_benchmark_status RPC) → report + voice feedback.grade_levelgrade_level is the difficulty proxy (0 = letters … up to story) and drives both passage generation and the
benchmark lookup. The benchmark RPC check_benchmark_status(p_wcpm, p_grade, p_language, p_is_l2) returns
(benchmark_min, benchmark_max, on_track, percentile_rank), reading from the wcpm_percentiles table
(25th percentile = min, 75th = max; season auto-derived from the current month).
Once the teacher picks the assessment language, every bot-generated string for the rest of that
assessment must be in that language — not the user's stored preferred_language. The welcome message
before the pick still uses the user's language. Pin each prompt with an explicit
MUST be written entirely in language code "${lang}", because the model otherwise drifts back to the
default. (Comprehension answers are preserved verbatim for non-English; the report renderer handles RTL +
Nastaliq natively.)
| Language | Pronunciation scoring | Benchmarks |
|---|---|---|
| English | Full (provider-based) | Hasbrouck-Tindal ORF norms |
| Urdu | LLM fallback (no provider scoring) | L2-adjusted (~70% of EN; recalibrate after enough production data) |
| Arabic | LLM fallback, RTL | L2-adjusted |
| Others | LLM fallback | extrapolated |
RTL languages (Urdu/Arabic) render grids right-to-left with reversed letter order and direction indicators.
| Comprehension score | Label |
|---|---|
| ≥ 80% | On Track |
| 60–79% | Approaching Benchmark |
| < 60% | Needs Support |
Performance tiers off the fluency percentile: ≥75 Excellent · on-track & ≥50 Proficient · on-track Developing · else Emerging — applied identically in the HTML template and the legacy renderer so both look consistent.
| Symptom | Cause | Fix |
|---|---|---|
| Voice feedback has nonsensical corrections | Transcript not cleaned (diarization labels left in) | Clean the transcript before word alignment. |
| Only one comprehension question asked | Voice routing priority wrong (reading handler before comprehension) | Check routing order in voice-message.handler.js. |
| Repetitive passages | No diversity controls in the prompt | Use the theme/name/avoid arrays in passage generation. |
| Wrong benchmark | Grade/language/L2 args mismatched | Confirm the check_benchmark_status args; trace the correlation id — see debugging. |
const correctWords = passageWords.filter((w, i) =>
transcriptWords[i]?.toLowerCase() === w.toLowerCase()).length;
const wcpm = (correctWords / (durationSeconds / 60)).toFixed(1);