Skill: dirtyrootsstudio/clawhub-memory-tiers-pro (version 1.0.0)

--- name: agent-memory-tiers-pro version: 1.0.0 description: "Production-grade agent memory and quality system for multi-agent swarms. Extends agent-memory-tiers with an 8-point quality grading rubric, progressive disclosure for token efficiency, orchestrator coordination patterns, self-improvement protocols, and agent scoring templates. Built from 3+ weeks running a 20-agent production swarm." metadata: openclaw: category: "productivity" tags: ["memory", "multi-agent", "swarm", "quality", "governance", "token-efficiency", "orchestration", "production"] requires: skills: ["agent-memory-tiers"] --- # Agent Memory Tiers Pro > Turn a collection of agents into a production-grade swarm. The free **agent-memory-tiers** skill gives your agents memory. This skill gives your swarm structure, quality standards, and self-improvement. It is the difference between "I have some agents running" and "I have a system that gets better over time." Built from running a 20-agent swarm in production daily for 3+ weeks. Every pattern here solved a real problem. **Requires:** [agent-memory-tiers](https://clawhub.ai/skills/agent-memory-tiers) installed and configured first. ## What This Adds | Component | What It Does | |-----------|-------------| | **Quality Grading Rubric** | Score any agent's SOUL.md on 8 criteria. Identify weak spots before they cause failures. | | **Progressive Disclosure** | 3-tier token loading system. Agents only read what they need, when they need it. | | **Orchestrator Protocol** | Patterns for a coordinator agent to manage the whole swarm efficiently. | | **Self-Improvement Loop** | Agents log mistakes and proven approaches. The swarm learns from itself. | | **Agent Scoring Template** | Evaluate and compare agents objectively. Know which ones need work. | --- ## 1. The 8-Point Quality Grading Rubric Every agent SOUL.md should be scored on these 8 criteria. Use the rubric when building new agents or auditing existing ones. ```markdown ## Agent Quality Rubric | # | Criterion | A (Top Tier) | C (Functional) | F (Broken) | |---|-----------|-------------|----------------|-----------| | 1 | Role Clarity | One sentence, crystal clear, no ambiguity | Vague or tries to do multiple jobs | Missing or contradictory | | 2 | Activation Triggers | Explicit triggers + exclusion conditions | Partial triggers, some guessing | No triggers defined | | 3 | Step-by-Step Workflow | Numbered steps with file paths and tool names | General guidance, some gaps | "Figure it out" | | 4 | Output Format | Exact template with field names and structure | Loose format guidance | No format specified | | 5 | Quality Checklist | Pre-completion validation steps | Partial checks | None | | 6 | Error Handling | Common failures listed with specific fixes | Some error awareness | None | | 7 | Boundaries | Explicit CAN and CANNOT lists | Partial limits | Vague or missing | | 8 | Token Efficiency | Under 300 lines, references externalized | Under 500 lines | Over 500 or bloated with inline data | ``` **Targets:** - Score A on criteria 1, 2, 4, and 7 (non-negotiable for production agents). - No F on any criterion. - Review and re-score every agent monthly. ### How to Score an Agent Read the agent's SOUL.md top to bottom. For each criterion, assign A/C/F based on the rubric. Record the scores. ```markdown ## Agent Scorecard: [AGENT_NAME] Date: YYYY-MM-DD Scored by: [human or auditor agent name] | Criterion | Score | Notes | |-----------|-------|-------| | Role Clarity | A | "Security monitor for production." Clear. | | Activation Triggers | C | Lists triggers but no exclusion conditions. | | Workflow | A | 12 numbered steps with file paths. | | Output Format | A | JSON template with required fields. | | Quality Checklist | C | 2 checks, should have 4-5. | | Error Handling | F | No failure scenarios listed. | | Boundaries | A | CAN: scan logs, alert. CANNOT: restart services, modify configs. | | Token Efficiency | A | 240 lines, refs externalized. | **Overall: 5A / 2C / 1F — Priority fix: add error handling.** ``` ### Fixing Common Score Failures **Role Clarity F → A:** ```markdown # BAD (F): You help with various tasks related to content and social media and marketing. # GOOD (A): You are WRITER. You draft social media posts for 4 accounts (2 LinkedIn, 2 X). You do NOT publish, schedule, or manage engagement. You only write drafts. ``` **Error Handling F → A:** ```markdown # BAD (F): (nothing — agent has no idea what to do when things break) # GOOD (A): ## Error Handling | Error | Cause | Fix | |-------|-------|-----| | "File not found: LEADS.md" | First run, file does not exist yet | Create LEADS.md with header template, then continue | | "API rate limit reached" | Too many requests this session | Stop current task, update L0 flags: "Rate limited — retry next activation" | | "Output exceeds 20,000 chars" | Response too large for workspace file | Split into multiple files: output-part1.md, output-part2.md | | "Tool call failed: web_search" | Network issue or API key expired | Skip web search, use cached data from last run, flag in L1 blockers | ``` **Boundaries F → A:** ```markdown # BAD (F): Be careful and don't do anything dangerous. # GOOD (A): ## Boundaries **CAN:** - Read and write files in own workspace - Search the web for public information - Draft content for human review **CANNOT:** - Post, publish, or send anything externally (all output goes to approval queue) - Access other agents' workspaces (read their L0 only via orchestrator) - Install packages, modify system config, or run shell commands - Spend money or commit to deadlines on behalf of the user ``` --- ## 2. Progressive Disclosure (Token Efficiency System) Not every activation needs the full SOUL.md. Load information in tiers to minimize token cost. ```markdown ## Progressive Disclosure Tiers | Tier | What Loads | When | Token Cost | |------|-----------|------|------------| | T1: Identity | L0.md (4 lines) + role sentence from SOUL.md | Every activation | ~50-100 tokens | | T2: Context | L1.md (rolling 7-day state) | Every activation | ~100-200 tokens | | T3: Full Instructions | Complete SOUL.md | When agent activates on a matching trigger | Full SOUL.md cost | | T4: References | External docs from references/ folder | Only when task explicitly needs them | On demand | ## Rules: - SOUL.md must stay under 500 lines. If it exceeds this, externalize reference material. - Large data files (logs, queues, trackers) go in workspace, NOT in SOUL.md. - Tables and templates are more token-efficient than prose. Prefer structured formats. - If a section of SOUL.md is only used for 1 out of 10 activations, move it to references/. ``` ### SOUL.md Size Budgets ```markdown ## SOUL.md Section Budget | Section | Max Lines | Purpose | |---------|-----------|---------| | Role + Identity | 5 | Who am I, one sentence purpose | | Quick Context (L0/L1 loader) | 5 | Pointer to memory files | | Activation Triggers | 10 | When to wake up, when NOT to | | Core Workflow | 60-80 | Numbered steps for primary tasks | | Output Templates | 40-60 | Exact format for deliverables | | Quality Checklist | 10-15 | Pre-completion validation | | Error Handling | 15-20 | Failure table | | Boundaries | 10-15 | CAN/CANNOT lists | | End-of-Run (L0/L1 update) | 15 | Memory update mandate | | **TOTAL** | **~200-300** | **Target range for production agents** | ``` ### Externalizing References When SOUL.md gets too large, move supporting material to separate files. ```markdown ## Reference Externalization Pattern In SOUL.md, replace large sections with pointers: For detailed style guidelines, read `references/STYLE_GUIDE.md`. For the full client list and history, read `references/CLIENTS.md`. For API endpoint documentation, read `references/API_DOCS.md`. Rules: - Agent reads reference files ONLY when the current task needs them. - Never inline reference content back into SOUL.md. - Reference files have no size limit but should be focused (one topic per file). - Update references independently of SOUL.md — they are living documents. ``` --- ## 3. Orchestrator Coordination Protocol When one agent (the orchestrator) manages a swarm of specialist agents, use these patterns. ### Swarm Status Check ```markdown ## Orchestrator: Morning Status Check 1. Read L0.md for every agent in the swarm. 2. Build a status table: | Agent | Focus | Last Active | Flags | |-------|-------|-------------|-------| | WRITER | Draft LinkedIn posts | 2026-03-16 | None | | SCOUT | Find 5 leads this week | 2026-03-16 | None | | WATCHDOG | Monitor v2.4 deploy | 2026-03-15 | Grafana intermittent | 3. Flag any agent with: - Last active > 48 hours ago (may be stuck or disabled) - Non-empty flags (needs attention) - Focus misaligned with current priorities 4. Present status brief to human operator. Do NOT auto-reassign tasks. ``` ### Task Routing ```markdown ## Orchestrator: Task Routing Protocol When a new task arrives: 1. Identify which agent's role matches the task. 2. Read that agent's L0.md — check flags for blockers. 3. Read that agent's L1.md "Blockers" section — confirm agent is not stuck. 4. If agent is clear: - Route the task with a structured brief: WHAT (task), WHY (context), DEADLINE (if any), OUTPUT (expected deliverable format). 5. If agent is blocked: - Check if a backup agent can handle it. - If no backup, escalate to human operator with: blocked agent name, blocker description, suggested fix. 6. Never route a task to an agent whose L0 flags indicate it cannot execute right now. ``` ### Cross-Agent Handoffs ```markdown ## Orchestrator: Handoff Protocol When Agent A's output feeds into Agent B's input: 1. Agent A completes its task and writes output to a shared handoff file: `workspace/handoffs/[AGENT_A]-to-[AGENT_B]-YYYY-MM-DD.md` 2. Agent A updates its L0.md line 3: "Handed off [deliverable] to [AGENT_B]." 3. Orchestrator reads Agent A's L0, confirms handoff file exists. 4. Orchestrator triggers Agent B with: - Pointer to the handoff file - Context: what Agent A produced and why - Expected output format 5. Agent B reads the handoff file, executes its task, writes output. 6. Agent B updates its L0.md and L1.md as normal. Rules: - Handoff files are write-once. Agent B never modifies Agent A's output. - Handoff files older than 7 days can be archived to handoffs/archive/. - If the handoff file is missing or malformed, Agent B stops and flags the orchestrator. ``` --- ## 4. Self-Improvement Protocol The swarm should get better over time. These two files make that happen. ### Lessons File Create `workspace/lessons.md` in your main workspace. Any agent (or human) can append to it. ```markdown # Lessons Learned Format: Date | Category | What Happened | Root Cause | Fix Applied ## Template: - YYYY-MM-DD | [agent/system/workflow] | [what went wrong] | [why] | [what we changed] ## Examples: - 2026-03-05 | agent | HERALD used wrong model, task failed | Model ID was invalid for provider | Updated all agents to correct model in config - 2026-03-08 | workflow | Agent output exceeded file size cap | No size check before write | Added pre-write size validation to SOUL.md workflow - 2026-03-10 | system | Credentials exposed in config file | Env vars stored in committed file | Moved secrets to .env.local, rotated all keys - 2026-03-12 | agent | SCOUT searched wrong platforms | Activation trigger too broad | Added exclusion conditions to SOUL.md triggers ``` ### Patterns File Create `workspace/patterns.md` in your main workspace. When something works well, record it. ```markdown # Proven Patterns Format: Pattern Name | When to Use | How It Works ## Template: ### [Pattern Name] **When:** [situation where this applies] **How:** [step by step] **Why it works:** [one sentence] ## Example: ### Batch-Then-Review **When:** Agent needs to produce multiple outputs (posts, reports, emails). **How:** Generate all items in one run. Write to a review queue file. Human reviews the batch. Approved items move to the action queue. **Why it works:** One activation for N outputs is cheaper than N activations for N outputs. Batch review is faster for the human too. ``` ### Monthly Swarm Audit ```markdown ## Monthly Audit Checklist Run this on the 1st of every month: 1. Score every agent using the 8-point rubric. Record in scorecards/. 2. Review lessons.md — are the same mistakes repeating? If yes, the fix was insufficient. 3. Review patterns.md — are proven patterns actually being used? If not, add them to SOUL.md workflows. 4. Check L0/L1 freshness — any agent with "Last run" older than 14 days is either unused or broken. 5. Check SOUL.md sizes — any over 400 lines needs externalization. 6. Archive old handoff files (> 7 days) and resolved blockers. 7. Update the orchestrator's agent roster with any new or retired agents. Output: One-page swarm health report for the human operator. ``` --- ## 5. Agent Scoring and Comparison Use this template to track agent quality over time. ```markdown ## Swarm Scorecard — YYYY-MM | Agent | Role | Clarity | Triggers | Workflow | Output | Quality | Errors | Bounds | Tokens | Grade | |-------|------|---------|----------|----------|--------|---------|--------|--------|--------|-------| | WRITER | Content | A | A | A | A | C | A | A | A | 7A 1C | | SCOUT | Leads | A | A | C | A | C | C | A | A | 5A 3C | | WATCHDOG | Security | A | C | A | A | A | F | A | A | 6A 1C 1F | **Swarm Average:** X.X / 8.0 **Weakest Criterion (swarm-wide):** [identify which criterion has the most C/F scores] **Priority Fix:** [one action item to raise the weakest area] ``` ### Tracking Improvement Over Time ```markdown ## Swarm Quality Trend | Month | Agents | Avg Score | A% | C% | F% | Top Agent | Needs Work | |-------|--------|-----------|----|----|----|---------|-----------| | 2026-03 | 20 | 6.8 | 78% | 18% | 4% | LEDGER | WATCHDOG | | 2026-04 | 22 | 7.1 | 82% | 16% | 2% | LEDGER | SCOUT | Target: 85%+ A scores, 0% F scores within 3 months of deployment. ``` --- ## Permissions This skill requires: - **File read/write** in agent workspace directories — to manage L0.md, L1.md, scorecards, lessons, patterns, and handoff files. - **File read** across agent workspaces — orchestrator needs to read other agents' L0.md files (read only, never write). - No network access required. - No external API access required. - No sensitive data access required. ## Credits Built and battle-tested by the Megaport swarm team across a 20-agent production deployment. Quality rubric inspired by Anthropic's skill-building guidelines and the OpenViking tiered memory architecture. ## License MIT — use it, modify it, share it.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)