SkillAtlasSkill 详情

assessment

Essays and writing behind this toolkit live at vexjoy.com.

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项目 README

来源文件:README.md

抓取于 2026年9月19日

VexJoy Agent

VexJoy Agent

Essays and writing behind this toolkit live at vexjoy.com.

VexJoy Agent connects plain-English requests to specialist agents, skills, and workflows. /do selects the knowledge and tools needed for your task. Hooks enforce specific checks, and scripts handle repeatable work.

The aim is to give capable models useful domain knowledge without making you learn the toolkit's catalog.

43 domain agents, 59 workflow skills, 78 hooks, 153 scripts. Agents carry knowledge, skills enforce methodology, hooks block incomplete work, scripts handle determinism.

Works across Claude Code (/do), Codex ($do), Factory (/do), Reasonix (/do).

What It Looks Like

$ claude

> /do debug this Go test

  Routing: go-engineer + systematic-debugging
  Phase 1/4: Reproduce: running test, capturing failure...
  Phase 2/4: Hypothesize: 3 candidates from stack trace...
  Phase 3/4: Verify: isolated root cause in connection pool timeout
  Phase 4/4: Fix: patch applied, test passing, PR opened

  ✓ Delivered: PR #847, fix connection pool timeout in health check

The router pairs a Go agent with a debugging skill, then follows the task through verification and delivery.

The Pipeline

  ROUTE        PLAN         EXECUTE      VERIFY       DELIVER      RECORD
 ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐
 │ /do  │───▶│ Task │───▶│Agent │───▶│Tests │───▶│  PR  │───▶│Route │
 │Router│    │ Plan │    │+Skill│    │Gates │    │Branch│    │Result│
 └──────┘    └──────┘    └──────┘    └──────┘    └──────┘    └──────┘

/d — Jev-Powered Router

/d routes requests through TypeSafe's Jev classifier. One API call picks the agent, skill, and pipeline — no manifest read into context. Requires Jev; use /do if TypeSafe is not configured.

Setup: install the typesafe MCP plugin and set TYPESAFE_API_KEY in your environment.

> /d fix the flaky test in the payments module

  ROUTING (/d): testing-automation-engineer + testing-preferred-patterns
  Source: jev (confidence: medium)
  Invoking...

Anti-Rationalization

Checks require evidence rather than confidence.

Agent SaysWhat Happens
"Code looks correct, skip tests"Exit gate requires test output. Blocked.
"Trivial change, no verification"Hook blocks completion without evidence.
"Similar to before"Skill demands case-specific proof.
"User is in a hurry"Protocol overrides time pressure.
"I'm confident"Gate demands exit code, not assertion.

Hooks run at configured events. Skills state what to verify; blocking78 hooks enforce the checks they cover. Coverage depends on the runtime and tool path.

Knowledge Work Is First-Class

The content engine researches, drafts in a calibrated voice, checks 397 writing patterns, and adapts finished pieces for each platform. /html produces a self-contained report, slide deck, prototype, chart, or diagram. It needs no coding or setup beyond installation.

It Proves Its Own Changes

Toolkit changes use direct review and relevant checks. Model comparisons can settle specific uncertainties; they are not required for every edit. PHILOSOPHY.md explains the validation policy. what-didnt-work.md records failed experiments, routing reversals, unvalidated A/B citations, disabled lint rules, and program refutations.

The automated nightly evolution loop (/evolve, writes to evolution-reports/) ran regularly through mid-May 2026. It is currently dormant; recent evidence has come from manual PRs instead.

Installation

git clone https://github.com/notque/vexjoy-agent.git ~/vexjoy-agent
cd ~/vexjoy-agent
./install.sh

Installs into ~/.claude/ and mirrors into ~/.codex/, ~/.factory/, and ~/.reasonix/ when the runtime command is on PATH or its home directory exists. Choose symlinks for live updates through git pull, or copies for a stable snapshot.

Want only part of the toolkit? Run ./install.sh --configure to pick which skills, agents, and78 hooks install, or copy .local.example/profile.yaml to .local/profile.yaml and edit. No profile file = full install, unchanged behavior. Credit: @thomasvan. Details: .local.example/README.md.

CLIEntry Point
Claude Code/do
Codex$do
Factory/do
Reasonix/do

Jev Auto-Compact plugin (optional, requires TYPESAFE_API_KEY):

claude plugin marketplace add ./plugins/jev-auto-compact
claude plugin install jev-auto-compact@jev-auto-compact -y

Replaces LLM-generated compaction summaries with Jev-judged verbatim pruning. Once context reaches 60%, Jev evaluates each old tool call (keep, truncate result, or drop) and returns the pruned transcript with zero rewriting, in about a second instead of one to three minutes. The threshold matters: every compaction is a cold KV-cache rewrite of the prefix, so compacting every turn multiplies cost. Evidence lives in learning.db (python153 scripts/jev-compact-evidence.py).

Proof it works: python153 scripts/jev-compact-evidence.py prints every compaction from two sources side by side — the plugin's claim and the engine's own compact_boundary record (tokens before/after, duration). A Jev compaction shows as a sub-second engine record next to a matching plugin claim; a built-in LLM compaction shows as a 30–150s record. Rows live in ~/.claude/learning/learning.db (compaction_events, session_usage).

Full setup: docs/start-here.md

Codex CLI Parity

Mirrors agents, skills, and supported78 hooks into ~/.codex/. The original six-hook allowlist was correct for Codex v0.114, when tool hooks only intercepted Bash. Current support requires Codex v0.144.1+ and classifies the 62 Claude hook registrations as 26 native, 27 adapter-backed, and 9 unsupported (53 supported). These are registration counts, not unique hook files. The installer also preserves explicit per-subagent model routing for GPT-5.6 Sol by setting the MultiAgent V2 compatibility keys documented in openai/codex#31814.

Codex now exposes apply_patch to tool78 hooks. VexJoy's adapter converts each patch operation into the Write/Edit payload expected by existing guards, but it cannot intercept writes performed through unified_exec, unmatched MCP tools, WebSearch, or other unsupported tool paths. PreCompact and Stop adapters also receive less telemetry than Claude Code: Codex does not provide Claude's conversation_history or session_data. This is expanded compatibility, not full Claude parity.

After install or any hook-definition change, run /hooks in Codex and review the new definitions before trusting them. Codex hash-trusts hook commands and skips changed, unreviewed definitions.

Gemini CLI / Antigravity CLI Support (removed)

Gemini CLI support removed (deprecated upstream, transitioned to Antigravity CLI); Antigravity support pending CLI maturity. Per Google's transition announcement, Gemini CLI stops serving requests on 2026-06-18 for Google AI Pro / Ultra and free Gemini Code Assist for individuals. Gemini API integrations (image-gen backends, sprite pipeline, GEMINI_API_KEY) are unaffected and stay in the toolkit.

If a prior install mirrored into ~/.gemini/, remove the stale mirrors with:

rm -rf ~/.gemini/skills ~/.gemini/agents ~/.gemini/hooks ~/.gemini/scripts ~/.gemini/antigravity/plugins/vexjoy-agent
Factory CLI Support

Mirrors agents (as "droids"), skills, and all78 hooks into ~/.factory/. Hook config merges into ~/.factory/settings.json with paths rewritten.

Reasonix Support

Mirrors skills, 153 scripts, and the allowlisted 78 hooks (scripts/reasonix-hooks-allowlist.txt) into ~/.reasonix/ (no agent or custom-command surface, so neither is installed; the /do router rides in as a skill). Reasonix fires only 4 events (PreToolUse, PostToolUse, UserPromptSubmit, Stop), so only hooks for those events are allowlisted. Hook config is written to the hooks key of ~/.reasonix/settings.json in Reasonix's native flat shape (one entry per hook, match regex over the tool name); the generator builds absolute python3 commands, so no path rewrite is applied. MCP/model/permissions in ~/.reasonix/config.json are user-owned and left untouched.

Token-saving mode

The toolkit supplies its own routing, domain knowledge, methodology, and enforcement. The default system prompt duplicates most of that.

claude --system-prompt "."

Strips built-in tool-use instructions. The toolkit's agents, skills,78 hooks, and CLAUDE.md provide equivalent coverage.

Four Layers

LayerCountDoes
Agents43Domain knowledge: idiom tables, failure mode catalogs, error-to-fix mappings
Skills59Phased methodology with gates. Can't skip steps. Each phase has exit criteria requiring evidence.
Hooks78Fire on lifecycle events. Block incomplete work. Zero LLM cost.
Scripts153Determinism: test runners, linters, validators. No LLM judgment.

Full skill catalog: docs/skills.md.

┌─────────────────────────────────────────────────┐
│  SKILL.md                                       │
│  ┌─ Frontmatter ─────────────────────────────┐  │
│  │ triggers, pairs_with, success-criteria     │  │
│  └────────────────────────────────────────────┘  │
│  Reference Loading Table (conditional imports)   │
│  Phased Instructions (numbered, with gates)      │
│  Verification (evidence requirements)            │
└─────────────────────────────────────────────────┘

Built with the Toolkit

A game built entirely by Claude Code using these agents, skills, and pipelines:

Choose Your Path

I just want to use it Install, learn /do, done.

I do knowledge work Writing, research, data analysis, moderation, HTML artifacts. No code.

I'm a developer Architecture, extension points, adding agents and skills.

I'm an AI power user Routing tables, pipelines,78 hooks, telemetry DB.

I'm an AI agent Machine-dense inventory. Tables, paths, schemas.

I'm on LinkedIn 🚀 Thought leadership. Agree? 👇

Philosophy

  • Zero-expertise operation. Say what you want. The system classifies, dispatches, enforces, delivers.
  • LLMs orchestrate, programs execute. Deterministic work belongs to153 scripts. LLM judgment handles design decisions, diagnosis, review.
  • Density. Every word carries instruction, rule, or decision. Cut everything else.
  • Breadth over depth. Right context ensures correctness. Unfocused context adds cost.
  • Structural enforcement. Exit codes enforce what instructions can't. Quality gates are automated, not advisory.
  • Everything pipelines. Complex work decomposes into phases. Phases have gates. Gates prevent cascading failures.

Full design philosophy: PHILOSOPHY.md

Maintenance

One report-only script surfaces upkeep work; it prints a digest and never edits, deletes, or blocks.

  • python153 scripts/stale-skill-scan.py --top 20 ranks stale skills and agents as pruning candidates. Run it quarterly; see docs/deprecation-template.md.

Scheduled work follows the same boundary as everything else: judgment uses agents; repeatable plumbing uses153 scripts.

NeedUse
Run a deterministic command on a schedulescripts/agent-scheduler.py with runner: "command"
Run an agent judgment on a schedule, webhook, or file changescripts/agent-scheduler.py with the default runner: "claude"
Install or remove a user crontab entry safelyscripts/crontab-manager.py
Audit shell cron reliabilitycron-automation
Keep one interactive objective moving until criteria verifyobjective-loop

Contributing

See CONTRIBUTING.md.

License

MIT. See LICENSE.

数据与 AI

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:2 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/analysis/assessment" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/analysis/assessment" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/analysis/assessment" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/analysis/assessment" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/notque/vexjoy-agent.git
  3. 将 "skills/analysis/assessment" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: assessment
description: "Assessment: read-only inspection, codebase overview, value analysis, health checks, ADR consultation, decision analysis, multi-perspective critique."
user-invocable: true
allowed-tools:
  - Read
  - Write
  - Bash
  - Grep
  - Glob
  - Edit
  - Task
  - Skill
  - Agent
routing:
  not_for: "code review with findings (use review), building or fixing (use workflow)"
  triggers:
    - "inspect without changing"
    - "read-only"
    - "audit current state"
    - "onboard to codebase"
    - "codebase structure"
    - "give me an overview"
    - "summarize this repo"
    - "repo value analysis"
    - "what can we learn from"
    - "service status"
    - "check health"
    - "is service running"
    - "validate endpoints"
    - "consult on ADR"
    - "architecture consultation"
    - "adr consultation"
    - "help me decide"
    - "decision matrix"
    - "pros and cons"
    - "trade-offs"
    - "critique these ideas"
    - "devil's advocate"
    - "stress test proposals"
    - "roast this"
    - "poke holes in this"
  category: analysis
  pairs_with:
    - review
    - workflow
    - security

Assessment Skill

Seven modes for read-only analysis and decision support. Match the request to a mode, then follow that mode's phases.

Mode Selection

Request patternMode
Inspect, browse, explore without changingRead-Only Inspection
Onboard, overview, summarize repo, codebase structureCodebase Overview
Repo value analysis, compare repos, what can we learnRepo Value Analysis
Service status, health check, uptime, validate endpointsService Health Check
Consult on ADR, challenge design, architecture consultationADR Consultation
Help me decide, decision matrix, pros/cons, trade-offsDecision Scoring
Critique ideas, devil's advocate, stress test, roastMulti-Persona Critique

Read-Only Inspection

Safe exploration without modifying files or system state.

Phase 1: SCOPE

Parse the request. Determine target scope (file, directory, service, system-wide). Clarify before proceeding if scope could match dozens of results.

Phase 2: GATHER

Use read-only tools only.

Allowed: ls, find, wc, du, df, file, stat, ps, top -bn1, uptime, free, pgrep, git status/log/diff/show/branch, sqlite3 "SELECT ...", curl -s (GET only), date, env.

Forbidden: mkdir, rm, mv, cp, touch, chmod, chown, git add/commit/push, file writes, INSERT/UPDATE/DELETE/DROP, npm/pip/apt install, kill, systemctl restart.

Phase 3: REPORT

Lead with the answer. Show supporting evidence. List files examined. All claims must cite evidence.


Codebase Overview

4-phase exploration producing an evidence-backed onboarding report. Read-only.

Read any .claude/CLAUDE.md or CLAUDE.md in the repo root first. Skip sensitive files (.env, *.pem, *.key, credentials) silently.

Phase 1: DETECT

Examine root directory. Identify project type from config files (package.json, go.mod, pyproject.toml, pom.xml, Cargo.toml). Document: language, framework, build system, dependencies. Load references/codebase-overview/exploration-strategies.md for language-specific discovery commands.

Gate: Project type identified. Tech stack documented.

Phase 2: EXPLORE

Discover entry points, core modules, data models, API surfaces, configuration, tests. Limit 20 files per category. Map directory structure (exclude node_modules/, venv/, vendor/, dist/, build/, __pycache__/).

Gate: Entry points, core modules, data layer, API surface, config, tests documented.

Phase 3: MAP

Identify design patterns with file evidence. Map 5-10 key abstractions. Trace a typical request through the full stack. Analyze last 10 commits. All paths absolute. All claims cite source files.

Gate: Patterns identified, abstractions mapped, data flow documented.

Phase 4: SUMMARIZE

Generate report using references/codebase-overview/report-template.md. Include "Where to Add New Code" section. Run post-exploration secret scan.

For deep-dive mode ("full picture"), launch 4 parallel domain agents via Task. See references/codebase-overview/examples-and-errors.md for dispatch template.

Scripts: scripts/cartographer.py (quick), scripts/cartographer_omni.py (100-metric), scripts/cartographer_ultimate.py (focused performance).


Repo Value Analysis

6-phase pipeline analyzing external repositories for adoptable ideas.

Phase 1: CLONE

Parse input (GitHub URL, local path, org/repo). git clone --depth 1. Categorize files into zones (skills, agents, hooks, docs, tests, config, code, other). Cap zones at ~100 files.

Phase 2: DEEP-READ (parallel)

Dispatch 1 Agent per zone (up to 8). Each reads EVERY file and produces: component inventory, key techniques, notable patterns, gaps. Output to /tmp/[REPO]-zone-[zone].md.

Gate: 75%+ agents returned.

Phase 3: INVENTORY (parallel with Phase 2)

Dispatch 1 Agent to catalog vexjoy-agent repo: agents, skills, hooks, scripts with counts. Output to /tmp/self-inventory.md.

Phase 4: SYNTHESIZE

Read all zone findings and inventory. Build comparison table. Rate gaps: HIGH/MEDIUM/LOW. Save draft to research-[REPO]-comparison.md.

Phase 5: AUDIT (parallel)

For each HIGH/MEDIUM recommendation, dispatch 1 audit Agent to verify: ALREADY EXISTS, PARTIAL, or MISSING. Skip with --quick.

Phase 6: REPORT

Adjust recommendations from audit. Write final report: executive summary, comparison table, already-covered, recommendations, verdict, next steps. Clean /tmp/ files. Load references/repo-value-analysis/phase7-implement-template.md for implementation dispatch.


Service Health Check

Deterministic service monitoring: Discover-Check-Report. Never report healthy without verifying process status independently.

Phase 1: DISCOVER

Locate service definitions: services.json, docker-compose, systemd units, or user input. Build manifest: process pattern, health file, port, stale threshold per service.

Phase 2: CHECK

Per service: (1) pgrep -f "<pattern>" for process status, (2) parse health file JSON for staleness/status/connections, (3) ss -tlnp "sport = :<port>" for port.

ConditionStatus
Process not runningDOWN
Running + health file missing/staleWARNING
Running + status=errorERROR
Running + disconnected >30minWARNING
Running + port not listeningERROR
Running + healthyHEALTHY

Gate: All services evaluated with evidence.

Phase 3: REPORT

Output summary (X/N healthy), highlight services needing action, provide copy-pasteable remediation. Never auto-restart without explicit flag.

For endpoint validation, load references/service-health-check/endpoint-validator.md. For CVE source auditing, load references/service-health-check/cve-source-check.md.


ADR Consultation

3-agent parallel architecture consultation producing PROCEED or BLOCKED.

Phase 1: DISCOVER

Locate ADR (user path, .adr-session.json, or ask). Validate via adr-query.py. Read full ADR. Create adr/{adr-name}/ directory.

Gate: ADR read, path validated, consultation directory created.

Phase 2: DISPATCH (parallel)

Launch all 3 agents in ONE message. Load references/adr-consultation/agent-prompts.md for prompt templates:

  1. Contrarian (reviewer-perspectives): challenge assumptions, simpler alternatives
  2. User advocate (reviewer-perspectives): user impact, cognitive load
  3. Meta-process (reviewer-perspectives): system health, coupling, SPOF

For complex decisions, add 2 more agents (see agent-prompts.md).

Gate: All agents returned and wrote to adr/{adr-name}/.

Phase 3: SYNTHESIZE

Read agent files from disk. Extract concerns to adr/{adr-name}/concerns.md. Determine verdict: all PROCEED = strong consensus, any BLOCK = hard block, mixed = significant concerns. Write adr/{adr-name}/synthesis.md. Issue verdict per references/adr-consultation/consultation-patterns.md.


Decision Scoring

Weighted scoring for 2-4 options. Runs inline (no fork).

Step 1: Frame

State decision in one sentence. List 2-4 options. Eliminate non-starters first.

Step 2: Criteria

Default weights (adjust per domain -- load references/decision-helper/decision-archetypes.md for build-vs-buy, database, cloud, framework, API, or operational tooling):

CriterionWeightMeasures
Correctness5Solves the actual problem
Complexity3Added complexity (lower = better)
Maintainability3Ease of change/debug
Risk3Failure mode severity
Effort2Implementation time
Familiarity2Team comfort
Ecosystem1Library/community support

Lock weights before scoring. Do not adjust after seeing results.

Step 3: Score

Rate each option 1-10 per criterion with one-sentence justification. Calculate sum(score * weight) / sum(weights).

Step 4: Analyze

All scores <6.0: no good option -- explore alternatives. Top two within 0.5: close call -- identify deciding criteria. Top leads by >0.5: recommend winner. If matrix contradicts intuition, ask which criterion is missing.

Step 5: Persist

Append to active ADR session (.adr-session.json) or task plan.


Multi-Persona Critique

5-persona parallel critique with consensus synthesis.

Phase 1: UNDERSTAND

Extract or generate numbered proposals. Each: what it does, why it matters, how it differs from status quo (2-4 sentences). Research domain first if generating.

Phase 2: BRIEF

Load references/multi-persona-critique/personas.md. Build prompts for 5 personas, each receiving ALL proposals:

  1. The Logician: coherence, assumptions, falsifiability
  2. The Pragmatic Builder: build cost, maintenance, simpler alternatives
  3. The Systems Purist: accidental complexity, separation of concerns
  4. The End User Advocate: friction, delight, solved-problem test
  5. The Skeptical Philosopher: human agency, dependency risk

Each produces: STRONG/PROMISING/WEAK/REJECT per proposal, ranked list, cross-cutting observations.

Phase 3: DISPATCH (parallel)

Launch all 5 via Agent. Wait for ALL to complete.

Phase 4: SYNTHESIZE

Build consensus matrix (proposals x personas x ratings). Classify: CONSENSUS (4+ agree), CONTESTED (2-3 split), OUTLIER (1 vs 4). Score: STRONG=3, PROMISING=2, WEAK=1, REJECT=0. Sum per proposal (0-15).

Phase 5: PRESENT

Generate report using references/multi-persona-critique/synthesis-template.md: consensus matrix, features to build, worth investigating, disagreements, shelve, cross-cutting insights.

For roast-style code critique with HN personas and file:line validation, load references/multi-persona-critique/roast.md.


Deep References

Load on demand when the corresponding phase needs detailed lookup data.

ContextReferenceContent
Codebase overview: language commandsreferences/codebase-overview/exploration-strategies.mdPer-language discovery commands
Codebase overview: report formatreferences/codebase-overview/report-template.md12-section report template
Codebase overview: deep-dive dispatchreferences/codebase-overview/examples-and-errors.mdParallel agent template, worked examples
Codebase overview: statistical lensesreferences/codebase-overview/statistical-three-lenses.mdThree-lens statistical analysis
Codebase overview: metrics catalogreferences/codebase-overview/statistical-metrics-catalog.md100-metric catalog
Codebase overview: statistical phasesreferences/codebase-overview/statistical-phase-details.mdPhase banners and workflows
Codebase overview: statistical examplesreferences/codebase-overview/statistical-analysis-examples.mdReal-world statistical workflows
Value analysis: implementationreferences/repo-value-analysis/phase7-implement-template.mdAgent dispatch template
Health check: endpoint validationreferences/service-health-check/endpoint-validator.mdFull endpoint validation methodology
Health check: security headersreferences/service-health-check/security-headers.mdHSTS, CSP reference
Health check: endpoint configreferences/service-health-check/endpoint-config-preferred-patterns.mdConfig patterns
Health check: auth endpointsreferences/service-health-check/auth-endpoint-patterns.mdAuth endpoint patterns
Health check: CVE sourcesreferences/service-health-check/cve-source-check.mdCVE source check methodology
ADR: agent promptsreferences/adr-consultation/agent-prompts.md3-agent prompt templates
ADR: artifact patternsreferences/adr-consultation/consultation-patterns.mdVerdict display, artifact templates
ADR: failure modesreferences/adr-consultation/consultation-preferred-patterns.mdDispatch and verdict fixes
ADR: error recoveryreferences/adr-consultation/error-handling.mdError recovery by phase
Decision: archetypesreferences/decision-helper/decision-archetypes.mdArchetype-specific criteria weights
Decision: failure modesreferences/decision-helper/decision-preferred-patterns.mdScoring discipline patterns
Critique: personasreferences/multi-persona-critique/personas.md5 persona specifications
Critique: synthesisreferences/multi-persona-critique/synthesis-template.mdConsensus matrix and report
Critique: examplesreferences/multi-persona-critique/examples-and-errors.mdWorked examples, failure modes
Critique: roast modereferences/multi-persona-critique/roast.mdHN persona evidence-based critique

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