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 xskill-self-improving-objectgit 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/xskill-self-improving-object)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/xskill-self-improving-object"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/xskill-self-improving-object/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/xskill-self-improving-object"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/xskill-self-improving-object.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 | $0.00207 | $0.02709 |
| Opus 5 | $0.00103 | $0.01354 |
| Sonnet 5 | $0.00041 | $0.00542 |
| Haiku 4.5 | $0.00021 | $0.00271 |
Grade A, and why
xskill-self-improving-object 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 12d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XSkill Self-Improving Graph Objects
Overview
The improvement mechanisms earlier in Ch7 (prompt refinement, SEAL data generation, fine-tuning) all modify the agent itself. Knowledge augmentation is lighter: it accumulates knowledge from past executions and retrieves it at inference time, touching neither the model nor its prompts. The motivating measurement from the chapter: on the Kaggle GameArena chess benchmark, 78% of Gemini-2.5-Flash losses were illegal moves, rule violations rather than weak strategy. The agent kept repeating the same category of mistake because it had no memory of past failures.
XSkill (Jiang et al., 2026) extracts two complementary knowledge types from trajectories:
- Experiences operate at the action level. Each execution node's input, action, and outcome becomes a candidate experience record. Experiences alone reduce tool errors from 29.9% to 16.3% (a 45% reduction).
- Skills operate at the task level. The path from the root query node to a successful resolution node becomes a candidate multistep skill. Together with experiences, the average success rate rises from 33.6% to 40.3%.
Cognee closes the remaining gap: XSkill's skills are static artifacts. Cognee
treats a skill as a first-class graph node (the SkillNode example) with execution
records, a success rate, and an amendment history. Its four-stage pipeline is
add (parse SKILL.md, compute content hash) then cognify (extract trigger
phrases and complexity) then search (route by which skill SUCCEEDS at similar
tasks) then learn (log an observation per execution). When a skill degrades,
amendify() rewrites it against the last 10 failures, validates the amendment
against held-out records, and rolls back on failure.
When to Use
- After an agent has accumulated execution traces and you want it to stop repeating avoidable mistakes without retraining
- Environments that drift (a Kubernetes API change, a new CI/CD stage) where a static SKILL.md silently goes stale
- Routing among several overlapping skills where description similarity picks the wrong one and demonstrated success should decide
- Building the self-evolution loop on top of the execution-graph substrate
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.
- 12d ago First seen · 176 lines · 207 tokens per session scan A fc178b506fd6
xskill-self-improving-object is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 207 tokens to every session and 2,709 once invoked, about $0.0010 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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