2496 Sdlc Layer Arl Integration Suggestions 309764df

SDLC Layer Separation Architecture — ARL Integration Suggestions

Generated: 2026-02-23 Plan: ARL = Observe → Identify → Probe → Accumulate → Improve Source: Full read of all ARL items from sdlc-layer-candidates-master.md

Plan Context

• Observe: kaizen, session-historian — session recall, transcript analysis
• Identify: deviations/hallucinations — fact-check, hallucination-detector, doc-drift
• Probe: human for invisible knowledge — (explicit phase; no dedicated tool yet)
• Accumulate: local domain knowledge with staleness — research/, KB, context manifest
• Improve: SAM + connective tissues — optimize-claude-md, work-backlog-item close, skill/agent patches

1. agentskill-kaizen (plugin)

Path: plugins/agentskill-kaizen/

Integration

• Observe: transcript-analyst agent, transcript-analysis skill, kaizen-analysis MCP — session transcript mining
• Improve: kaizen-improvement skill, improvement-generator agent — hook generation, agent/skill patches

Amendment

Add agentskill-kaizen as the primary Observe→Improve bridge in the ARL flow. It spans Observe (analysis) and Improve (actionable outputs). The plan should explicitly reference it as the post-hoc implementation of ARL Layer 3 Observation (per plugin-creator/arl).

Nugget

• For Probe: transcript-analysis dimension 9 (Missing Hooks) identifies recurring manual corrections that could be automated — these are candidates for human-probing: "What invisible knowledge makes you correct this manually?"
• For Accumulate: kaizen-improvement outputs to .planning/kaizen/improvements/ — consider routing high-frequency findings into research/ or context manifest as institutional knowledge.

Extracted Content

• Commands: /agentskill-kaizen:analyze, explore, report, generate-hooks
• MCP: kaizen-analysis (process mining, clustering), kaizen-duckdb (persistent DB)
• 10 dimensions: Tool Misuse, Repeated Errors, User Frustration, Missing Tooling, Delegation Patterns, Shortest Path, Red Herrings, System Interruptions, Missing Hooks, DuckDB SQL
• Outputs: hooks, agent patches, skill patches, CLAUDE.md updates, script automation proposals
• Sentiment dashboard: real-time visualization of user message sentiment

2. transcript-analysis (skill)

Path: plugins/agentskill-kaizen/skills/transcript-analysis/SKILL.md

Integration

• Observe: Primary skill for session transcript analysis. Place at Observe phase entry point.

Amendment

Document the JSONL schema and DuckDB query patterns as the canonical Observe data model. Plan should specify that Observe phase outputs use .planning/kaizen/analysis-DATE.md format for downstream Identify/Improve consumption.

Nugget

• For Identify: Dimension 2 (Repeated Errors) — "Edit-before-Read", "String to replace not found" — these correlate with hallucination-detector's speculation-as-diagnosis; consider cross-referencing.
• For Probe: Dimension 3 (User Frustration) — "No,", "Don't", "Why did you" — signals where human knowledge was needed but not captured; feed into Probe design.
• For Accumulate: Output format includes "Recommendation type — hook, skill patch, agent prompt fix, CLAUDE.md update" — these map to Accumulate targets (research/, context manifest).

Extracted Content

• Data: ~/.claude/projects/{project-key}/ — JSONL transcripts, subagent transcripts, tool-results
• Record types: assistant, user, system, progress, file-history-snapshot, summary
• 10 dimensions with extraction methodology (Tool Misuse, Repeated Errors, User Frustration, etc.)
• Process mining: extract_tool_sequences, discover_process_model, check_conformance, find_frequent_patterns, detect_frustration_signals, cluster_sessions
• Output: .planning/kaizen/analysis-DATE.md with session ID, severity, evidence, frequency, recommendation type

3. kaizen-improvement (skill)

Path: plugins/agentskill-kaizen/skills/kaizen-improvement/SKILL.md

Integration

• Improve: Transform Observe findings into actionable improvements. Place at Improve phase, downstream of Observe.

Amendment

The plan's "Improve (SAM + connective tissues)" should explicitly include kaizen-improvement as the transcript-derived improvement pipeline. It produces delegation prompts for subagent-refactorer, skill-creator, and CLAUDE.md updates — all connective tissue targets.

Nugget

• For Probe: "Improvements are instruction sets for specialist agents, not direct edits" — the delegation protocol could include a Probe step: "Before implementing, ask: does the human have context this finding doesn't capture?"
• For Accumulate: Priority scoring includes "Blast radius — project-wide > single-agent > single-session" — project-wide improvements (CLAUDE.md) should update Accumulate artifacts (e.g., research/ patterns).

Extracted Content

• Prerequisite: Analysis findings in .planning/kaizen/ from transcript-analysis
• Five improvement types: Hook generation, Agent prompt refinement, Skill patches, CLAUDE.md updates, Script automation
• Delegation protocol: outcome-focused, never prescribe specific code
• Output: .planning/kaizen/improvements/ (draft) or direct install (hooks only, --install)
• Priority: frequency × impact, automation potential, blast radius, implementation cost

4. hallucination-detector (plugin)

Path: plugins/hallucination-detector/

Integration

• Identify: Primary tool for detecting speculation-as-diagnosis, invented causality, pseudo-quantification, completeness overclaims. Place at Identify phase, structural enforcement (Stop hook).

Amendment

The plan should state that Identify includes both (a) skill-based verification (fact-check) and (b) structural enforcement (hallucination-detector Stop hook). The hook blocks completion when triggers are present — architectural constraint, not behavioral instruction.

Nugget

• For Probe: When blocked, Claude must rewrite with evidence-first language. The rewrite often surfaces "I don't know yet" — a natural Probe trigger: "What would you need to know?"
• For Accumulate: Triggers (speculation, causality, pseudo-quantification, completeness) could be added to research/ as "hallucination pattern taxonomy" for skill documentation.
• For Improve: After 2 blocks in same response cycle, plugin allows completion to prevent infinite loops — document this as a known trade-off in Improve phase.

Extracted Content

• Stop hook: blocks completion when triggers found in last assistant message
• 5 triggers: Speculation ("I think", "probably"), Causality ("because" without evidence), Pseudo-quantification ("8/10", "70%"), Completeness ("all files checked"), Delegation Micromanagement (overly prescriptive edits)
• Required fix: evidence-first language, cited observations, "I don't know yet"
• Ignores: code blocks, blockquotes, questions
• Trade-off: 2 blocks → allow completion (infinite loop prevention)

5. plugin-creator/arl (skill)

Path: plugins/plugin-creator/skills/arl/SKILL.md

Integration

• Cross-cutting: ARL theory reference — informs all phases. Not a phase-specific tool; use when designing or evaluating ARL flows.

Amendment

The plan should explicitly reference the ARL skill as the theoretical foundation. Add a "Theory" subsection that cites: HOOTL, Three Layers (Research Body, Execution Model, Observation), 10 Gates (R1-R10), Scope-Feasibility Matrix, Decision Tree for gate replacement.

Nugget

• For Probe: Layer 2 Execution Model — "Asynchronous feedback queue", "AI user representatives", "Question-to-action-item conversion" — these are Probe design patterns. Plan should add Probe phase using these concepts.
• For Observe: "agentskill-kaizen is the current implementation of Layer 3 Observation in post-hoc mode" — plan should state Observe phase = agentskill-kaizen + session-historian.
• For Accumulate: Scope-Feasibility Matrix — "scope-classification step must precede any attempt at autonomous operation" — Accumulate should store scope classifications for work items.

Extracted Content

• HOOTL: human-in-the-loop outcome quality with human-out-of-the-loop execution
• Three Layers: Research Body (R1-R10, 7 principles), Execution Model (pre-discovery, async queue, AI reps, question-to-action), Observation (passive agents, agentskill-kaizen)
• 10 Gates: R1 Information Completeness, R2 Loop Detection, R3 Validity Filtering, R4 Plan Quality, R5 Purpose Anchor, R6 Content-Loss, R7 Convergence, R8 Proportionality, R9 Downstream Impact, R10 Split Justification
• Decision Tree: 4 conditions for replacing human gate (external truth, single dimension, distinguishable success/fail, bounded scope)
• Scope-Feasibility Matrix: High/Medium/Low scope clarity × goal measurability × data enumeration → eliminable?

6. session-historian (skill)

Path: .claude/skills/session-historian/SKILL.md

Integration

• Observe: Session recall, transcript search. Place at Observe phase for "I forgot what happened" and context reconstruction.

Amendment

The plan should distinguish session-historian (recall/search) from transcript-analysis (mining/patterns). session-historian = user-initiated recall; transcript-analysis = systematic analysis. Both consume ~/.claude/projects/ JSONL.

Nugget

• For Probe: Fidelity rules — "Distinguish absence: 'Not mentioned in transcript' not 'didn't happen'" — when Probe surfaces invisible knowledge, record it so future sessions don't assume absence = didn't happen.
• For Accumulate: Summaries cached at ~/.claude/kaizen/session-summaries/ — consider feeding high-value summaries into context manifest or research/ as "session-derived knowledge."
• For Identify: session_query.py search reads raw JSONL — fact-check and Identify phase can use same index for claim verification against prior session evidence.

Extracted Content

• Script: .claude/skills/session-historian/scripts/session_query.py — list, messages, search, show, index
• Index: ~/.claude/kaizen/session-index.duckdb (sessions + user_messages tables)
• Summaries: ~/.claude/kaizen/session-summaries/ — AI-generated, mark-summarized
• Fidelity rules: Read before summarizing, verbatim user messages, preserve counts, distinguish absence
• Workflow: list → messages (verbatim) → search (raw) → show + summary template

7. knowledge-explorer (skill)

Path: .claude/skills/knowledge-explorer/SKILL.md

Integration

• Accumulate: Manages research/ KB — local domain knowledge with staleness (verified, next_review). Place at Accumulate phase entry point.

Amendment

The plan should define Accumulate as: research/ KB (knowledge-explorer) + context manifest (context-refinement) + Integration Opportunities (research-context-agent). knowledge-explorer is the KB CRUD layer.

Nugget

• For Probe: Staleness (next_review, 6 months) — when KB entry is stale, Probe could ask: "Has anything changed in your experience with this tool?"
• For Improve: fetch-github, add, update-append — these operations feed Improve when topic-specialist or research-curator produce verified content.
• For Identify: fact-check can verify claims in research/ entries; knowledge-explorer's verified/next_review fields support Identify's evidence discipline.

Extracted Content

• Script: research/knowledge-explorer.py — list, show-template, fetch-github, add, update-append, migrate
• KB root: research/ with category subdirs
• Frontmatter: name, description, metadata (topic, category, source_url, verified, next_review, tags)
• Staleness: next_review < today or Last Verified > 6 months
• Valid categories: agent-frameworks, developer-tools, mcp-ecosystem, etc. (20+)

8. refresh-research (skill)

Path: .claude/skills/refresh-research/SKILL.md

Integration

• Accumulate: Bulk refresh of research entries. Place at Accumulate phase for staleness-driven updates.

Amendment

The plan should include refresh-research as the bulk Accumulate refresh workflow. It runs RT-ICA pre-flight, spawns research-curator agents in waves of 5, updates README, produces summary report.

Nugget

• For Probe: RT-ICA pre-flight checks "mcp__Ref and mcp__exa available" — Probe phase could add: "Human available for invisible-knowledge questions?"
• For Improve: Post-actions (lint, commit) — ensure Improve phase's work-backlog-item close and optimize-claude-md don't conflict with refresh-research commits.
• For Identify: Staleness detection (past review date, >6 months) — Identify could flag research entries with REFUTED claims from fact-check.

Extracted Content

• Scope: --all, --stale (default), --category, --dry-run
• Workflow: Inventory → Staleness → RT-ICA → Waves of 5 research-curator → README update → Summary report → Lint + commit
• RT-ICA conditions: mcp__Ref, mcp__exa, gh, network, ./research/ writable
• Outcome categories: Updated, Unchanged, Failed

9. research-curator (skill)

Path: .claude/skills/research-curator/SKILL.md

Integration

• Accumulate: Add/maintain research entries. Single URL, batch (--batch), rerun (--rerun), validate (--validate). Place at Accumulate phase as the content-creation layer.

Amendment

The plan should specify research-curator as the Accumulate content executor. It spawns @research-curator agents; refresh-research orchestrates bulk; knowledge-explorer provides CRUD/exploration.

Nugget

• For Probe: Batch mode waves of 5 — when research-curator fails, Probe could ask: "What context from your experience would help verify this?"
• For Improve: Post-actions (README, lint, commit, push) — same pattern as work-backlog-item; ensure consistent artifact conventions.
• For Identify: Validate mode runs validate_research.py — script detects, agent fixes. fact-check could feed REFUTED claims into --validate workflow.

Extracted Content

• Modes: Default (single URL), Batch (--batch, waves of 5), Rerun (--rerun), Validate (--validate)
• Agent: @research-curator for content work
• Post-actions: README update, lint, commit, push
• Batch: max 5 concurrent, duplicate detection, progress reporting

10. research-curator/references (entry-template, validation-rules, batch-mode)

Path: .claude/skills/research-curator/references/entry-template.md, validation-rules.md, batch-mode.md

Integration

• Accumulate: Define Accumulate artifact format and validation. Reference from plan's Accumulate schema.

Amendment

The plan should include the research entry schema (entry-template) and validation rules (error/warning/info severity) as Accumulate artifact conventions. batch-mode documents wave spawning (max 5) — reuse across ARL phases.

Nugget

• For Probe: Entry template "Relevance to Claude Code Development" — Probe could populate "Invisible knowledge from user experience" subsection.
• For Improve: Freshness Tracking (Last Verified, Next Review) — Improve phase (optimize-claude-md, topic-specialist) should update these when modifying research entries.
• For Identify: validation-rules — script vs agent responsibility (script detects, agent fixes content) — same pattern as fact-check's claim extraction vs verification.

Extracted Content

• Entry template: Category selection flowchart, Required Information (Identity, Substance, Relevance), Freshness Tracking
• Validation: section_completeness, header_fields, empty_sections (error); access_dates, freshness_tracking, statistics_currency (warning)
• Batch: waves of 5, sequential, duplicate detection, post-batch README/lint/commit

11. fact-check (skill)

Path: .claude/skills/fact-check/SKILL.md

Integration

• Identify: Verify claims against primary sources. Produces VERIFIED/REFUTED/INCONCLUSIVE. Place at Identify phase for backlog items, skill docs, plugin content.

Amendment

The plan should define Identify as: (a) hallucination-detector (structural, Stop hook), (b) fact-check (verification, primary sources), (c) doc-drift-auditor (code vs docs). fact-check is the verification workflow.

Nugget

• For Probe: INCONCLUSIVE verdict — "State what additional step would resolve it" — natural Probe trigger: ask human for invisible knowledge that could resolve.
• For Accumulate: VERIFIED/REFUTED claims update backlog; consider routing to research/ when claim is about a tool/library (update KB entry).
• For Improve: fact-check spawns @fact-checker in waves of 5 — same pattern as research-curator; topic-specialist consumes fact-check findings for skill updates.

Extracted Content

• Evidence: WebFetch, WebSearch, CLI output, repo source, MCP — NOT training data
• Claim extraction: backlog item, plugin path, --all-unverified
• Verification: waves of 5 @fact-checker, CoVe (2-3 falsification questions, independent check)
• Verdict: VERIFIED, REFUTED, INCONCLUSIVE with citation
• Post-actions: update backlog, lint, commit

12. optimize-claude-md (skill)

Path: .claude/skills/optimize-claude-md/SKILL.md

Integration

• Improve: Optimize AI-facing files (CLAUDE.md, SKILL.md, agents). Place at Improve phase as the "connective tissues" optimizer.

Amendment

The plan's "Improve (SAM + connective tissues)" should explicitly list optimize-claude-md as the AI-facing documentation optimizer. It runs RT-ICA pre-check, 8 optimization principles, CoVe post-check, independent verification.

Nugget

• For Probe: Phase 4 — "If agent signals BLOCKED: Present blocking reason, ask for resolution" — BLOCKED often means missing human context; add Probe step before re-delegation.
• For Accumulate: Optimized files are Accumulate targets; ensure optimize-claude-md doesn't remove content that was added from Accumulate (e.g., research-derived patterns).
• For Identify: Independent verification (Phase 5) — second agent checks for regressions; aligns with fact-check's CoVe and Identify's evidence discipline.

Extracted Content

• Phases: Validate → Baseline → Delegate @contextual-ai-documentation-optimizer → Handle BLOCKED/DONE → Independent verification → Measure → Report → Apply on approval
• 8 principles: Positive framing, Motivation, Concrete examples, Front-loaded priorities, Concise language, Explicit format control, Strategic XML tagging, Structural enforcement
• RT-ICA pre-check, CoVe post-check
• Iterative mode for >300 lines: Structural → Content → Polish

13. work-backlog-item close path (Step 9)

Path: .claude/skills/work-backlog-item/SKILL.md (Step 9)

Integration

• Improve: Verification agent for acceptance criteria; closes loop on completed items. Place at Improve phase as the completion verification step.

Amendment

The plan should include work-backlog-item close as the Improve phase completion gate. It verifies checklist 100%, spawns acceptance-criteria verification agent, writes closing record, closes GitHub issue.

Nugget

• For Probe: Step 9d verification agent prompt — "Does the implementation satisfy the stated goal?" — add: "Were any invisible requirements discovered during implementation?" (feeds Probe/Accumulate).
• For Accumulate: Closing record includes "verified by checklist + acceptance criteria check" — context-refinement's "Discovered During Implementation" could be invoked before close to capture Accumulate-worthy findings.
• For Identify: Verification agent checks git log, reads changed files — same evidence discipline as fact-check and doc-drift-auditor.

Extracted Content

• Trigger: $0 = close or resolve
• Close path: 9a Find item → 9c Checklist verification (100%) → 9d Spawn verification agent (PASS/FAIL) → 9e Write closing record
• Verification agent: Read plan, git log, key files; assess goal satisfaction; return PASS/FAIL + evidence
• Resolve path: No verification; reason required
• GitHub: Close issue with comment on success

14. doc-drift-auditor (agent)

Path: .claude/agents/doc-drift-auditor.md

Integration

• Identify: Identifies doc vs implementation drift. Place at Identify phase alongside fact-check and hallucination-detector.

Amendment

The plan should add doc-drift-auditor to Identify phase. It complements fact-check (claims) and hallucination-detector (speculation) with structural drift — implemented but undocumented, documented but unimplemented, outdated, mismatched details.

Nugget

• For Probe: "Documented but unimplemented" — Probe: "Was this intentionally deferred? What would need to change to implement it?"
• For Accumulate: DOCUMENTATION_DRIFT_AUDIT.md findings — feed into context manifest "Discovered During Implementation" or research/ when drift reveals tool/library behavior changes.
• For Improve: Drift findings are Improve inputs — update docs to match code, or vice versa; optimize-claude-md could consume drift report for targeted optimization.

Extracted Content

• Process: Repository discovery → Git timeline → Implementation analysis → Documentation claims → Drift detection → Report
• Categories: Implemented but undocumented, Documented but unimplemented, Outdated, Mismatched details
• Evidence: file:line, commit SHA, quoted claims, code reality
• Output: DOCUMENTATION_DRIFT_AUDIT.md
• Boundaries: Audit only; no modifications; no training-data reliance

15. code-review (agent)

Path: .claude/agents/code-review.md

Integration

• Identify: LLM-slop and hallucination detection in code. Place at Identify phase for code artifacts (overlaps with hallucination-detector for narrative, code-review for code).

Amendment

The plan should include code-review in Identify phase for code-level deviations: reimplemented scaffolding, junk patterns, placeholders, hallucinated defaults, duplicate env vars. Distinct from hallucination-detector (narrative) and fact-check (claims).

Nugget

• For Probe: "Creating defaults/fallbacks that are entirely hallucinated or imagined" — when code-review flags this, Probe: "What is the correct default for this context?"
• For Accumulate: code-review findings (Critical/Warning/Suggestion) — high-frequency patterns could become research/ entries or CLAUDE.md rules.
• For Improve: code-review runs during context compaction or pre-commit — ensure Improve phase (work-backlog-item close) invokes code-review before verification agent when code was changed.

Extracted Content

• Focus: "Some or all of the code was generated by an LLM"
• Patterns: Reimplemented scaffolding, junk patterns, placeholders, hallucinated defaults, duplicate env vars, indentation/JSON/YAML issues
• Severity: Critical (security, correctness), Warning (reliability, performance), Suggestion (alternatives, docs)
• Process: Get changes → Understand patterns → Focus areas → Review against standards
• Boundaries: Focus on what matters; respect existing choices; be specific

16. context-refinement (agent)

Path: .claude/agents/context-refinement.md

Integration

• Accumulate: Updates context manifest with discoveries from work session. Place at Accumulate phase as the institutional knowledge capture agent.

Amendment

The plan should define context-refinement as the Accumulate session-capture agent. It reads transcript, identifies drift/discoveries, appends "Discovered During Implementation" to context manifest. Reduces staleness of context for future sessions.

Nugget

• For Probe: "Wrong assumptions in original context" — when context-refinement finds this, Probe: "What assumption should have been in the original context? How would we capture it next time?"
• For Observe: context-refinement reads transcript from sessions/transcripts/context-refinement/ — ensure this aligns with session-historian and transcript-analysis data locations.
• For Improve: "Guardian of institutional knowledge" — Improve phase (work-backlog-item close) should invoke context-refinement before closing to capture last-session discoveries.

Extracted Content

• Trigger: End of work session
• Process: Read transcript → Analyze for drift/discoveries → Decision (no update vs update) → Append "Discovered During Implementation"
• Qualifies: Undocumented interactions, incorrect assumptions, hidden side effects, complex error cases
• Doesn't qualify: Minor typos, implied things, temporary workarounds
• Output: "No context updates needed" or "Context manifest updated with X discoveries"

17. logging (agent)

Path: .claude/agents/logging.md

Integration

• Observe: Consolidates work session output. Place at Observe phase as the task state maintainer for future session consumption.

Amendment

The plan should include logging in Observe phase. It ensures task file reflects present state for kaizen/session-historian consumption. "Maintains clean task state for future sessions" — enables Observe to read accurate work logs.

Nugget

• For Accumulate: logging's Work Log (Completed, Decisions, Discovered, Next Steps) — context-refinement could read this to avoid duplicating "Discovered" in context manifest.
• For Improve: logging runs during context compaction or task completion — coordinate with work-backlog-item close so logging runs before close verification.
• For Probe: "Decisions" and "Discovered" sections — when these are sparse, Probe: "Were there decisions or discoveries not captured?"

Extracted Content

• Trigger: Context compaction or task completion
• Responsibilities: Read file, read transcript, assess cleanup, remove irrelevant, update existing, add new, chronological order
• Format: Completed, Decisions, Discovered, Next Steps
• Rules: Cleanup first, chronological integrity, consolidation, clarity
• Boundaries: Only edit task file; never touch sessions/state/, current-task.json

18. topic-specialist (agent)

Path: .claude/agents/topic-specialist.md

Integration

• Improve: Researches primary sources, can update or create skills with verified findings. Place at Improve phase as the verified-knowledge-to-skill bridge.

Amendment

The plan should include topic-specialist in Improve phase. It feeds SAM + connective tissues with primary-source-verified content. OUTPUT: "answer + update skill" or "answer + create skill".

Nugget

• For Probe: "If a primary source is unavailable: state 'Unable to verify from primary source'" — Probe: "Do you have experience with this that could substitute?"
• For Accumulate: topic-specialist populates skills from verified findings — skills are Accumulate targets; ensure research/ entries are updated when topic-specialist adds tool/library knowledge.
• For Identify: topic-specialist uses fact-check, find-cause, research-curator skills — CoVe, evidence discipline; aligns with Identify's verification requirements.

Extracted Content

• Invocation: TOPIC, SKILLS, QUESTION, CONDITIONS, OUTPUT (answer only | answer + update skill | answer + create skill)
• Research: GitHub source, README, docs, issues — primary sources only
• CoVe: 2-3 falsification questions, cross-check
• Update skill: Append with citations, do not remove existing
• Create skill: Invoke skill-creator, add-doc-updater, populate with verified findings
• Boundaries: No training-data-only; no commit; no plugin/agent creation

19. research-context-agent (agent)

Path: .claude/agents/research-context-agent.md

Integration

• Accumulate: Cross-references research with skills, agents, hooks, commands. Discovers integration opportunities. Place at Accumulate phase as the connective tissue between research KB and capabilities.

Amendment

The plan should include research-context-agent in Accumulate phase. It appends "Integration Opportunities" to research files — Enhances Existing, New Skill Candidates, New MCP Candidates, Cross-References. This is the Accumulate→Improve bridge (identifies what to improve).

Nugget

• For Probe: "New skill candidate" — when research-context-agent proposes one, Probe: "Is there existing institutional knowledge that would inform this skill?"
• For Improve: Integration Opportunities table — direct input to Improve phase (topic-specialist, optimize-claude-md, kaizen-improvement).
• For Identify: research-context-agent validates claims against primary sources (WebSearch/WebFetch) — aligns with fact-check evidence discipline.

Extracted Content

• Process: Absorb (extract from research) → Search & Match (5 dimensions) → Append (Integration Opportunities)
• Dimensions: Enhance skills, enhance agents, enhance hooks, enhance commands, new skill candidate, new MCP candidate
• Output: Enhances Existing table, New Skill Candidates, New MCP Server Candidates, Cross-References
• Rules: Concrete over vague, skip empty sections, no false positives, preserve content, idempotent

Summary: ARL Phase Mapping

Recommended Plan Amendments

1. Add Probe phase design: Use ARL Layer 2 (async feedback queue, AI user representatives, question-to-action-item) and transcript-analysis dimension 3 (User Frustration) to design human-probing workflow.
2. Unify wave spawning: Document "max 5 concurrent" as ARL-wide pattern (fact-check, research-curator, refresh-research, groom-backlog-item).
3. Define Accumulate schema: research/ entry template + context manifest "Discovered During Implementation" format.
4. Define Improve completion sequence: logging → context-refinement → work-backlog-item close (with verification agent) → code-review (if code changed).
5. Reference plugin-creator/arl: Add "ARL Theory" subsection citing HOOTL, 10 Gates, Scope-Feasibility Matrix, Layer 3 Observation (agentskill-kaizen).