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 skills add AnthonyAlcaraz/agentic-graph-rag-skills --skill graduated-validation-protocolgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skillsWrote 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/skills/anthonyalcaraz/agentic-graph-rag-skills/graduated-validation-protocol)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/graduated-validation-protocol"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/graduated-validation-protocol/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/graduated-validation-protocol"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/graduated-validation-protocol.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00216 | $0.03426 |
| Opus 5 | $0.00108 | $0.01713 |
| Sonnet 5 | $0.00043 | $0.00685 |
| Haiku 4.5 | $0.00022 | $0.00343 |
Grade A, and why
graduated-validation-protocol 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 10d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graduated Validation Protocol
Overview
The evolutionary loop generates candidate improvements continuously. Not all of them should reach production. Any system that modifies itself in production needs a safety envelope. Ch7 provides it through the RPO spine and the Graduated Validation Protocol (GVP).
The RPO spine is three interlocking properties:
- Recursion — the improvement mechanisms are themselves part of the codebase the agent can analyze and improve. The self-improvement system applies to itself.
- Provenance — every change is cryptographically signed and committed
to an immutable ledger. This is source control for thought: complete
auditability and instant rollback for every model, prompt, or
architectural change.
lib.provenance_signatureis the dev-time stand-in. - Optimization — the GVP itself. Rather than a single approval gate that must be either permissive or strict, it routes each candidate to a risk tier and applies matching scrutiny.
The three tiers:
| Tier | Risk class | Gate | Outcome |
|---|---|---|---|
| 1 canary release | low: minor prompt updates, hyperparameter adjustments | ~1% live traffic; statistically significant target-metric lift with no core-KPI degradation | pass or automatic rollback |
| 2 staging gauntlet | moderate: SEAL/TPT fine-tune results, refined tool integration | benchmark + regression (catastrophic forgetting) + safety/alignment (bias drift) + performance (latency/cost) suites; net-positive multi-objective utility U = w_accuracyaccuracy + w_cost(1-cost) + w_safety*safety_score, no safety regressions | pass or archive |
| 3 airlock protocol | high: modifies safety protocols, integrates a brand-new tool, or failed staging with large potential | sandboxed isolated environment; automated risk/reward report | escalated for human approve / reject / modify |
The second half is the entropy-collapse guard. In production the agent evolves continuously, hundreds of small improvements per week, each passing validation individually. The compounding problem is the accumulated weight of all changes: contradictory learnings, stale fixes for resolved issues, redundant nodes that slow retrieval. The Kepler architecture (OpenAI, 2026) solves this with a dual-store pattern: Knowledge is human-authored and version-controlled; Learnings are agent-generated and ephemeral. A daily garbage-collection traversal walks the Learnings subgraph and removes a node if any of three conditions holds: the issue was resolved and promoted to Knowledge, a newer higher-confidence learning contradicts it, or it has not been retrieved in 30 days.
What ships with it
2 files 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.
- 10d ago First seen · 211 lines · 216 tokens per session scan A 09a5b2a5763f
graduated-validation-protocol is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 216 tokens to every session and 3,426 once invoked, about $0.0011 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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Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.