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 ericrisco/rsc-harness --skill continuous-learninggit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/continuous-learning)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/continuous-learning"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/continuous-learning/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/ericrisco/rsc-harness/continuous-learning"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/continuous-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.02414 |
| Opus 5.5 | $0.00029 | $0.00966 |
| Sonnet 5.5 | $0.00015 | $0.00483 |
| Haiku 4.5 | $0.00007 | $0.00241 |
Grade A, and why
continuous-learning 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 15d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous learning — the feedback loop of the harness
This is the engine that makes the system get better every time it is wrong. When something broke, got corrected, surprised you, or simply taught the workspace something, your job is to catch that lesson and route it back into the durable apparatus so the same mistake cannot recur.
One premise is load-bearing: a lesson that lives only in chat is gone at the next compaction. Verbal reflection only changes future behaviour when it is persisted to memory the agent actually reads next time (this is the Reflexion mechanism — self-reflection written to durable memory, not held in the conversation). In this harness, "memory" is a concrete set of surfaces: a SKILL.md body, 02-DOCS/wiki/harness/decisions.md, 02-DOCS/wiki/harness/user-profile.md, a root CLAUDE.md rule, or a verify.sh check. Chat is not memory. A lesson is "captured" only when it has landed in one of those.
Read technical_level in 02-DOCS/wiki/harness/user-profile.md before you narrate, and speak in the orient voice in that register. It governs the words only — the capture loop runs identically for every reader.
Boundaries
| Situation | Owner | Why |
|---|---|---|
| "We chose Postgres over Mongo, here's the why + the rejected options" | ../decision-records/SKILL.md |
forward choice with alternatives; a choice is not a lesson from a mistake |
| "The migration failed because Postgres rejects NULL on the PK — never assume that again" | continuous-learning |
backward lesson from a mistake |
| "Find the bug causing the 500" | ../debug/SKILL.md |
in-the-moment diagnosis; you run after it resolves |
| "Now that debug found it, make sure we never ship that pattern again" | continuous-learning |
post-resolution capture |
| "Write the SKILL.md and evals for pr-describe" | ../author-skill/SKILL.md |
skill craft — body, description, evals |
| "This lesson means the deploy skill needs a new guardrail" | continuous-learning decides the home → ../author-skill/SKILL.md makes the edit |
you route, it writes |
| "Consolidate these docs / sweep the inbox" | ../harness/SKILL.md |
generic 02-DOCS filing; your trigger is "we learned something the hard way" |
| "What do I know about X" | ../knowledge-ops/SKILL.md |
reference knowledge, as opposed to mistake-derived experiential knowledge |
| "Run this sweep every week" | loop / schedule harness CLI |
you may recommend a cadence; you are not a scheduler |
What ships with it
3 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.
- 15d ago Changed 4dc39a016170
- 1mo ago First seen · 107 lines · 73 tokens per session scan A 32aa931be00b
continuous-learning is a skill published in the GitHub repository ericrisco/rsc-harness (227 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 2,414 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
garden-kb-retriever
Derived retrieval checklist for local knowledge files. Reference-only. No network pull.
context-engineering
Use when designing, reviewing, or debugging how an agent's context window gets filled, pruned, or shared — choosing what loads at boot versus on demand, sizing an install or an always-loaded file, fixing an agent that drifts, repeats itself, or forgets constraints mid-task, planning compaction or summarization…
claude-compaction-restore
Use when a Claude Code session is about to compact, has just compacted, reached its context limit, resumed after /compact, or must rebuild its working picture of a long-running seat from its own restore map, its JSONL transcript and the files it touched.
retiring-and-inheriting-a-seat
Use when you are a sitting agent near your context threshold (85%) and a PLANNED seat transition is due — retire deliberately and hand your seat to a fresh successor primed from a packet, rather than let compaction degrade you. Covers the handover packet, the append-only lineage ledger (one row per tenure)…
session-compaction-and-restore
Use when reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet…
semantix
Install and use the semantix memory kernel as a middleware in your agent: extract user preferences / workflows / experience from past sessions, retrieve and inject them on demand. One binary + your agent's own tools.