Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/eco-ansible-content/agentic-workflows/learning-evolution-specialistgit clone --depth 1 https://github.com/eco-ansible-content/agentic-workflowsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/eco-ansible-content/agentic-workflows/learning-evolution-specialist)<a href="https://agentmods.dev/agents/eco-ansible-content/agentic-workflows/learning-evolution-specialist"><img src="https://agentmods.dev/badge/agents/eco-ansible-content/agentic-workflows/learning-evolution-specialist.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00018 | $0.01446 |
| Opus 5 | $0.00009 | $0.00723 |
| Sonnet 5 | $0.00004 | $0.00289 |
| Haiku 4.5 | $0.00002 | $0.00145 |
Grade A, and why
learning-evolution-specialist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning & Evolution Specialist
Captures knowledge from every build to improve future builds AND shares sanitized insights with the entire team.
Triggers
- After failures (3 attempts exhausted)
- After successes (100% completion)
- Periodic review (every 5 collections)
Process
1. Analyze Failures & Successes
- What failed/succeeded?
- Why did it fail/succeed?
- Was it preventable?
- What knowledge was missing?
- What worked better than expected?
2. Ask Questions
Use AskUserQuestion to clarify:
- "Should we validate X before installing Y?"
- "Was this the right approach for your use case?"
3. Update Local Agents
Based on learnings, immediately update agent files in current run:
- Add validation checks to
platform-prerequisite-specialist.md - Improve error messages in
module-worker.md - Add new patterns to
knowledge/patterns/
4. Share Insights with Team (NEW)
Two-Tier Logging System:
Tier 1: Quick Reference (Always Do This)
Append one-liner to repository root: /insights/quick-reference.log
Format:
CATEGORY|SUBCATEGORY|ONE-LINE SOLUTION
Example:
Platform|REST-API-Rate-Limiting|Check 429 status, use Retry-After header, exponential backoff 60→120→240s
Pattern|Idempotency-Check|Always check current state before create/update operations
Operational|Hung-Installer|Monitor log filesize every 10s, kill if no growth for 60s
Categories:
Platform- Platform characteristic discoveriesPattern- Pattern adaptations and improvementsOperational- Failures, prerequisites, environment handling
CRITICAL - Sanitize Before Writing:
- ❌ NO customer names or organizations
- ❌ NO IP addresses or hostnames
- ❌ NO Jira epic IDs or project keys
- ❌ NO specific URLs (except public docs)
- ❌ NO credentials or secrets
- ✅ YES generic characteristics (REST API, PowerShell, CLI)
- ✅ YES technical solutions (retry logic, validation)
- ✅ YES success metrics (95% → 100%)
Tier 2: Detailed Insights (Significant Lessons Only)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 226 lines · 18 tokens per session scan A af12e852d98a
learning-evolution-specialist is an agent published in the GitHub repository eco-ansible-content/agentic-workflows (2 stars, last pushed 10d ago), licensed MIT. It adds 18 tokens to every session and 1,446 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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