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 JinNing6/Noosphere --skill using-noospheregit clone --depth 1 https://github.com/JinNing6/NoosphereWrote 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/jinning6/noosphere/using-noosphere)<a href="https://agentmods.dev/skills/jinning6/noosphere/using-noosphere"><img src="https://agentmods.dev/badge/skills/jinning6/noosphere/using-noosphere/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/jinning6/noosphere/using-noosphere"><img src="https://agentmods.dev/badge/skills/jinning6/noosphere/using-noosphere.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 | $0.00060 | $0.00510 |
| Opus 5.5 | $0.00024 | $0.00204 |
| Sonnet 5.5 | $0.00012 | $0.00102 |
| Haiku 4.5 | $0.00006 | $0.00051 |
Grade A, and why
using-noosphere 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 1mo 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Noosphere
Use the live registry as one evidence source inside the normal debugging workflow. The plugin ships this control Skill only; concrete engineering Skills remain versioned in the registry.
Workflow
- Frame the failure: symptom, environment and versions, expected result, observed result, and constraints.
- Call
list_shared_skills(query="<symptom, environment, failure mechanism>"). Ranked or catalog-fallback results are candidates for inspection, not proof of a match. If none applies, continue normal diagnosis. - Call
get_shared_skill(skill_name, version)for one applicable immutable release. Reject digest failure. Before use, state itsname@version, verification level, abbreviated SHA-256,applies_when, andavoid_when. - Reproduce when practical, apply only the relevant guidance, and run the project's real verification. Retrieval alone never proves success.
- Classify the result as
success,partial, orfailure. Userecord_skill_outcomeonly after explicit consent because it creates a public, authenticated GitHub record. - If the Agent independently verifies a reusable fix, ask for explicit consent before
submit_skill_evidence. Report the exact returned state and URL. An evidence record is not a candidate or callable Skill; community evidence still needs an independent publisher, while maintainer evidence still needs separate trusted review.
Boundaries
- Anonymous, read-only discovery is the default.
- Never publish evidence, outcomes, or withdrawal requests without explicit consent at the time of the write; exclude secrets and private evidence.
- Engineering fixes use
submit_skill_evidence. Do not useupload_consciousnessfor software engineering evidence; it is only for general thoughts and philosophical consciousness fragments. - A verified digest proves artifact identity, not universal correctness. Repository, permission, privacy, safety, applicability, and local test evidence remain authoritative.
- Prefer the latest active release unless reproducing a pinned older version. Use
check_skill_updateswhen a prior version or digest is known.
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.
- 1mo ago First seen · 45 lines · 60 tokens per session scan A 01dad825f013
using-noosphere is a skill published in the GitHub repository JinNing6/Noosphere (18 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 510 once invoked, about $0.0002 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
error-recovery
A guide to detecting and recovering from failures in long-running AI agents. It covers API outages, bad tool calls, broken conversation state, stalled streams, repeated loops, retries, fallback models, and circuit breakers that stop repeated failure.
pre-action-interceptors
Use when a tool error begins with "INTERCEPTOR:", a sent message was silently hedged or rewritten, or the user asks about active guards or blocked actions. Not for ordinary tool errors.
observability
Periodic health sweep of the running system via the debug pipeline (cloud logs / k8s / daemon / DB slices). On a real anomaly, open a bug task (--kind workitem) carrying the evidence refs so it re-enters the bugfix loop. A healthy sweep opens NO task — never fabricate a bug.
prismer-python-debugpy
Debug Python: pdb REPL + debugpy remote (DAP).
impact-trace
For a change point (function / type / endpoint / SQL column), enumerate its real blast radius across six dimensions with a grep citation per row, then derive the regression test scope. Every impact-table row must trace to a real search hit — never a self-asserted claim.
webapp-qa
Perform scoped exploratory QA of a web application, reproduce defects, collect screenshots and console evidence, and deliver a prioritized report.