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 skills/day1labs/openava/agent-evolvernpx skills add Day1Labs/OpenAva --skill agent-evolvergit clone --depth 1 https://github.com/Day1Labs/OpenAvaWhat 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 | $0.00044 | $0.01038 |
| Opus 5 | $0.00022 | $0.00519 |
| Sonnet 5 | $0.00009 | $0.00208 |
| Haiku 4.5 | $0.00004 | $0.00104 |
Grade A, and why
agent-evolver 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 2d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evolver
Use this skill when the task is:
- A multi-step long-horizon task (coding, data analysis, research reports) where the agent should refine strategy mid-flight and carry lessons into future runs.
- A high-stakes task (production changes, compliance checks) that requires a built-in critic review before delivering output.
- A recurring task where user preferences, domain SOPs, or past failure patterns should be remembered and re-applied automatically.
Inputs
task_goal: Clear, measurable description of what success looks like.evaluation_criteria: How to judge success — automated tests, data rules, or a human review checklist.context(optional): Relevant past session notes, user profile, or domain reference documents.
Core Workflow
Run each phase in sequence. Never skip Reflect or Memory Write after a task completes.
Phase 1 — Act
Plan and execute the task using the current task goal plus any lessons injected from memory.
Produce:
- Final task output.
- Execution log with key decisions and evidence.
Phase 2 — Evaluate
Compare actual output against evaluation_criteria.
Produce a structured evaluation with:
- Pass/fail verdict and score.
- Identified failure points with supporting evidence.
- Identified success factors.
Phase 3 — Reflect
Distill the evaluation into one or more plain-text lessons — one lesson per distinct insight.
Rules:
- Only write a lesson when there is concrete evidence (logs, test results, data snapshots). Conclusions unsupported by evidence must be tagged
[confidence:low]. - Keep each lesson to 1–3 sentences: what triggered it, what went wrong (if anything), and what to do next time.
- Tag each lesson with short domain labels so future searches can find it.
Lesson format:
[lesson][tag1/tag2][confidence:high] When <trigger>, do <action>. Root cause if failed: <cause>.
Example:
[lesson][golang/concurrency][confidence:high] When generating Go concurrency tests, always add the -race flag and use t.Parallel() for subtests. Root cause: missing -race caused undetected race conditions.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 115 lines · 44 tokens per session scan A 9471d5392275
agent-evolver is a skill published in the GitHub repository Day1Labs/OpenAva (10 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 1,038 once invoked, about $0.0002 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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