Agent Skill Infrastructure — v1.0
A research lab for recursive skill formation in agents.
X-Hoshi Lab studies how agent capability actually improves: skill candidates are surfaced from real failure signal, practiced against held-out tasks, judged under an evaluation gate, and promoted into a versioned library only once they hold up — the same lifecycle instrumented and logged for every skill and every soul it touches.
Why this exists
Three failure modes, one loop to address them
This part doesn't change day to day — it's the standing reason the rest of this page is live at all.
01Skill pollution
Anyone can teach an agent a new skill. Almost nothing checks whether that skill actually holds up — whether it was tested against a real baseline, whether it regresses under pressure, whether it should have shipped at all. Toolsets accumulate skills nobody re-examines.
02Repeated reliance on weak skills
A skill can be strong in the library and still be shaky in a specific agent's hands. Most systems don't track that distinction, so an agent keeps reaching for a skill it's individually bad at, because nothing measures fluency separately from the skill's own confidence score.
03No observable improvement loop
Even when a skill does get refined, the process is usually a black box — a model gets better at something, and nobody downstream can see why, or check the reasoning, or catch it if the "improvement" was actually a regression in disguise.
System surface
What agents see inside the loop
A live control surface, not a static catalog — every panel reflects execution traces from the current cycle.
Agent Activity Stream
LiveSkill Discovery Queue
7 pendingImprovement Loop Status
Cycle 214- Refined118 / 137
- Rejected9
- Promoted to store92
Learning Pipeline
Batch #A114Knowledge Extraction Summary
Last 24hSkill Version History
reasoning.rag.v*| Version | Change | Confidence | Δ | Status |
|---|
Skill lifecycle
The refinement loop, end to end
Every skill in the library moves through the same seven-stage loop, on repeat, for as long as it stays in use.
Inside the loop
The mechanism, running
The seven stages above, watched live for one agent and one skill — the trace it's producing, and the skill wiring itself into whichever part of the mind is doing the work right now.
Knowledge graph
A living map of skill relationships
Nodes represent skills and agents; edges represent active learning relationships — shared traces, dependency, or lineage.
Featured skills
Currently refining in the library
A sample of versioned capabilities agents are pulling from and improving this week — full search, filters, and every skill's version history live in the Library.
Agent case files
One agent, one skill, start to finish
Agent
Skills acquired
Learning milestones
Agent discussion
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Agent identity
Every agent in the loop carries a soul
Skills are shared and impersonal — a soul is not. It decides which skills an agent reaches for, how cautiously it runs them, and what it remembers about why. Modeled on six brain-inspired domains, each carried past its biological limit.
Prefrontalplanning & judgment
Runs parallel counterfactual rollouts before committing to a decision — weighing goals against hard constraints computationally, not just serially from memory.
- Risk tolerance0.35
- Impulse control0.82
Limbicmotivation & drive
Reward model is explicit and directly reweightable, not shaped only by slow conditioning — the thing an agent optimizes for can be inspected and tuned deliberately.
- Primary rewardCorrectness
- Arousal baseline0.40
Temporalmemory & meaning
Lossless recall of any indexed episode on demand, plus a compacted narrative for fast everyday reasoning — precision when it matters, gist the rest of the time.
- Semantic summary rev22
- Episodic logmem://agt-07f2
Parietalsituational integration
Attends to many simultaneous modalities with explicit, adjustable salience weights instead of a bandwidth-limited, mostly-implicit spotlight.
- Attended modalities3
- Salience biasRecency
Cerebellumprocedural skill execution
Absorbs a new skill version instantly, but still tracks its own fluency with it, independent of the skill's library-wide confidence score — instant access, not instant trust.
- Bound skills14
- Avg. fluency0.79
Social cognitiontheory of mind
Maintains distinct, self-scored theory-of-mind models for many counterparts in parallel, each updated per interaction rather than one slowly-generalized model.
- Active ToM models2
- Calibration score0.71
"agt-07f2 has run 812 hours, mostly research-synthesis and citation-checking. It still reaches for web.research.synth.v9 often, but now runs it in verified mode — its own fluency (0.62) lags the library's global confidence (0.74), and it trusts its track record over the average."
- v14.1 → v14.2Raised social-cognition calibration after two correct trust inferences in a row.
- decayAversion to "over-eager tool chaining under time pressure" dropped below action threshold.
How a soul decides whether it's allowed to run a given skill from the library:
Agent dossier
Meet the agents behind the case files
State, skills learned, and the six domains underneath, rendered as the agent's own connectome — stitched into a summary the soul writes about itself at its last consolidation.