deep-assumptions

name	deep-assumptions
description	Use when the user wants to inspect deep-work's assumption health report — Wilson Score-based per-assumption confidence, verdict (justified / loosen / drop), model-aware split, and decay history. Triggers on `/deep-assumptions`, `/deep-status --assumptions`, "assumption health", "rule justification", "loosen enforcement", "어썸션 헬스", "규칙 정당화", "가정 검증". Reads session evidence to decide whether deep-work's hook denylist + receipt validation rules are still justified. Sub-page of the deep-status hub.
user-invocable	true

Invocation

이 스킬은 두 가지 경로로 호출됩니다 — 어느 쪽이든 본 SKILL 본문의 절차를 그대로 실행합니다:

1. Claude Code 슬래시 — 사용자가 /deep-assumptions [args...] 입력 (skill 의 user-invocable: true 가 슬래시 진입을 허용).
2. 타 에이전트 / Codex / Copilot CLI / Gemini CLI / SDK — Skill({ skill: "deep-work:deep-assumptions", args: "..." }) 형태로 명시 invoke (cross-platform 표준 경로).

두 경로 모두 args 는 동일한 토큰 문자열로 전달되며, 본문 ($ARGUMENTS 자리) 의 파서가 동일하게 처리합니다.

Inputs (skill args)

인자	의미
(없음) / report	Default — per-assumption confidence + Wilson Score + verdict + model-aware split
report --verbose	Per-signal per-session breakdown
history	ASCII confidence evolution timeline (requires 5+ sessions)
export --format=badge	shields.io 호환 JSON badge 출력
--rebuild	Receipt 파일에서 JSONL 재생성 후 report 표시

빈 args / 매칭되지 않는 토큰 → 본문의 default 분기로 진입.

Prerequisites

이 entry skill 은 deep-work-orchestrator (Phase dispatch) 및 deep-work-workflow (reference skill — Phase 규약/Exit Gate/M3 envelope) 와 함께 동작합니다. 활성 deep-work 세션이 있을 때는 세션 state file (.claude/deep-work.<SESSION_ID>.md) 의 변수 (work_dir, current_phase, active_slice 등) 를 읽어 동작하며, 세션 외부에서도 standalone 실행이 가능한 경우 본문의 분기를 따릅니다.

Hub-spoke 관계: 본 skill 은 deep-status hub 의 sub-page 입니다 — /deep-status --<flag> 가 본문 로직을 inline Read 하여 실행하는 것이 주 경로이며, 직접 호출도 동일하게 지원됩니다.

Cross-platform self-containment: Claude Code 에서는 sibling skill 이 description 매칭으로 자동 로드됩니다. Codex / Copilot CLI / Gemini CLI / Agent SDK 에서 Skill() 로 호출 시 sibling auto-load 보장이 약할 수 있으므로, 본문은 self-contained 으로 보존되어 있습니다 — state file 해석, $ARGUMENTS 파싱, AskUserQuestion 분기, 출력 포맷이 인라인.

Internal (v6.3.0) — /deep-status --assumptions가 이 파일의 로직을 Read하여 실행합니다. 자동 호출이 주 경로이며, 직접 호출도 지원됩니다. 참조처: skills/deep-status/SKILL.md §9 (Read skills/deep-assumptions/SKILL.md and follow its logic).

Assumption Health Report (v5.0)

Analyze deep-work's enforcement assumptions against session history to determine which rules are justified by evidence and which should be loosened.

Language

Detect the user's language from their messages or the Claude Code language setting. Output ALL user-facing messages in the detected language. The display templates below use Korean as the reference format — translate naturally to the user's language while preserving emoji, formatting, and structure.

Usage

• /deep-assumptions — Show assumption health report (default: report)
• /deep-assumptions report — Per-assumption confidence with Wilson Score, verdict, model-aware split
• /deep-assumptions report --verbose — Per-signal per-session breakdown
• /deep-assumptions history — ASCII confidence evolution timeline (requires 5+ sessions)
• /deep-assumptions export --format=badge — shields.io JSON badge output
• /deep-assumptions --rebuild — Regenerate JSONL from receipts, then show report

Runtime Setup

1. Read state and paths

Resolve the current session's state file:

1. If DEEP_WORK_SESSION_ID env var is set → .claude/deep-work.${DEEP_WORK_SESSION_ID}.md
2. If .claude/deep-work-current-session pointer file exists → read session ID → .claude/deep-work.${SESSION_ID}.md
3. Legacy fallback → .claude/deep-work.local.md

Set $STATE_FILE to the resolved path.

Read $STATE_FILE and extract work_dir. If the state file doesn't exist, default work_dir to deep-work.

Set paths:

PLUGIN_DIR = <directory containing this command file>/../hooks/scripts
REGISTRY_PATH = <directory containing this command file>/../assumptions.json
WORK_DIR = $PROJECT_ROOT/<work_dir from state or "deep-work">
HISTORY_PATH = $WORK_DIR/harness-history/harness-sessions.jsonl

2. Parse subcommand

From $ARGUMENTS, determine the subcommand:

Input	Action
(empty), report	Report (default)
report --verbose	Verbose report
history	Timeline
export --format=badge	Badge export
--rebuild	Rebuild JSONL, then report

Subcommand: --rebuild

Regenerate harness-sessions.jsonl from receipt files. This repairs corrupted or missing history.

Run via Bash:

echo '{"action":"rebuild","workDir":"<WORK_DIR>"}' | node "<PLUGIN_DIR>/assumption-engine.js"

Parse the JSON result. If sessions array is non-empty, write each session as a line to $HISTORY_PATH (creating harness-history/ directory first with mkdir -p).

Display:

JSONL 재생성 완료
   소스: $WORK_DIR/receipts/
   세션 수: [N] entries rebuilt
   파일: $HISTORY_PATH

Then proceed to the default report subcommand.

Subcommand: report (default)

Step 1: Cold start / new model detection

Read model_primary from state file (or "unknown").

Run via Bash:

echo '{"action":"detect-model","historyPath":"<HISTORY_PATH>","model":"<model_primary>"}' | node "<PLUGIN_DIR>/assumption-engine.js"

Parse JSON result. If isNew is true and totalSessions > 0:

⚠️ 새 모델 감지: <model_primary>
   이 모델의 세션 이력이 없습니다. 이전 모델(<totalSessions> sessions)의 데이터를 기반으로 보고합니다.
   해석 시 모델 차이를 고려하세요.

If totalSessions is 0:

ℹ️ 세션 이력이 없습니다.
   deep-work 세션을 실행하면 assumption 검증 데이터가 수집됩니다.
   최소 5회 세션 후 유의미한 분석이 가능합니다.

Stop here.

Step 2: Generate report

Determine options based on arguments:

• Default: {"splitByModel": true}
• With --verbose: {"splitByModel": true, "verbose": true}

Run via Bash:

echo '{"action":"report","registryPath":"<REGISTRY_PATH>","historyPath":"<HISTORY_PATH>","options":{"splitByModel":true}}' | node "<PLUGIN_DIR>/assumption-engine.js"

Parse JSON result containing text, data, and warnings.

Step 3: Display report

Display the text field from the engine result. This contains the formatted report:

ASSUMPTION HEALTH REPORT ([N] sessions analyzed)
========================================================

1. phase_guard_blocks_edits
   Hypothesis: Blocking edits during non-implement phases improves quality
   Evidence:   8 supporting / 1 weakening / 3 neutral
   Confidence: HIGH (0.82)
   Verdict:    KEEP at current level (block)

2. tdd_required_before_implement
   Hypothesis: RED-GREEN-REFACTOR produces fewer bugs
   Evidence:   5 supporting / 4 weakening / 3 neutral
   Confidence: MEDIUM (0.56)
   Verdict:    CONSIDER loosening to "coaching"
   [claude-opus-4-6]: HIGH (0.78) — 5S/1W/8 sessions
   [claude-sonnet-4-6]: LOW (0.32) — 0S/3W/4 sessions

3. research_required_before_plan
   ...

PROPOSED CONFIG CHANGES (for manual application):
  tdd_required_before_implement: strict -> coaching
  (no other changes recommended at this time)

Auto-adjustment is active (v5.1). Adjustments are applied at session start.
To override: /deep-work --tdd=strict [task]

After displaying the engine report text, check the state file for assumption_adjustments:

If assumption_adjustments is non-empty, display:

ACTIVE AUTO-ADJUSTMENTS (this session):
  - tdd_mode: strict → coaching (score 0.42)
  - receipt_depth: full → minimal (score 0.28)

These adjustments were applied at session start based on accumulated evidence.
User override (--tdd=X) takes precedence over auto-adjustments.

Step 4: Verbose details (if --verbose)

If --verbose was specified, additionally display per-signal per-session breakdown from the data array.

For each assumption in data:

─── [assumption.id] 상세 분석 ───
Signal                                    | Type       | Sessions Triggered
────────────────────────────────────────────────────────────────────────────
test_pass_rate > threshold                | supporting | 2026-03-28, 2026-03-25
high_override_rate                        | weakening  | 2026-03-30
zero_rework_after_override                | weakening  | (none)
model_passes_all_tests_first_try          | supporting | 2026-03-28

To produce this, re-evaluate each assumption against each session:

• For each assumption in data, iterate over its signals from assumptions.json
• For each session, check if the signal was triggered (from the per-session evaluated details)
• Display the session_id (date portion) for triggered sessions, or "(none)"

Step 5: Display warnings

If warnings array is non-empty, display at the bottom:

⚠️ Warnings:
   - [warning message 1]
   - [warning message 2]

Step 6: Staleness tags

For any assumption with a "STALE" verdict in the report, append a staleness tag:

🕐 Stale assumptions detected. Run more sessions with varied configurations to refresh evidence.

Subcommand: history

Step 1: Check minimum sessions

Run the report action first to get session count. If fewer than 5 sessions:

ℹ️ 타임라인을 표시하려면 최소 5회 세션이 필요합니다.
   현재: [N]회

Stop here.

Step 2: Generate timelines

Run via Bash:

echo '{"action":"timeline","registryPath":"<REGISTRY_PATH>","historyPath":"<HISTORY_PATH>","options":{"windowSize":3,"width":40,"height":10}}' | node "<PLUGIN_DIR>/assumption-engine.js"

Parse JSON result containing timelines object (keyed by assumption ID).

Step 3: Display timelines

For each assumption, display its ASCII timeline chart:

Assumption Confidence Timeline
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[phase_guard_blocks_edits] Confidence Timeline
──────────────────────────────────────────────
1.0 |
0.7 |████████-----
    |████████████
0.4 |████████████-----
    |████████████████
    |████████████████
0.0 |████████████████
    +────────────────
     oldest    newest

[tdd_required_before_implement] Confidence Timeline
──────────────────────────────────────────────
1.0 |
0.7 |████----------
    |████████
0.4 |████████------
    |████████████
    |████████████
0.0 |████████████████
    +────────────────
     oldest    newest

─── Legend ───
█ = confidence at session window
- = threshold marker (HIGH=0.7, MEDIUM=0.4)
Each column = window of 3 sessions

Subcommand: export --format=badge

Step 1: Generate badge

Run via Bash:

echo '{"action":"badge","registryPath":"<REGISTRY_PATH>","historyPath":"<HISTORY_PATH>"}' | node "<PLUGIN_DIR>/assumption-engine.js"

Step 2: Display and save

Parse JSON result (shields.io format). Display:

Harness Health Badge (shields.io endpoint format):

{
  "schemaVersion": 1,
  "label": "harness health",
  "message": "78%",
  "color": "brightgreen"
}

README.md에 추가:
  ![Harness Health](https://img.shields.io/badge/harness_health-78%25-brightgreen)

또는 endpoint badge로 사용하려면 위 JSON을 파일로 저장 후 shields.io endpoint URL을 설정하세요.

Write the badge JSON to $WORK_DIR/harness-history/badge.json.

Error Handling

• If assumptions.json is missing: show "Registry not found. Ensure deep-work v5.0 is installed."
• If harness-sessions.jsonl is missing or empty: show the cold start message from Step 1.
• If node is not available: show "Node.js is required to run the assumption engine."
• If the engine returns an error field: display it as a warning and stop.