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 shyftlabs/continuum --skill continuum-observabilitygit clone --depth 1 https://github.com/shyftlabs/continuumWrote 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/shyftlabs/continuum/continuum-observability)<a href="https://agentmods.dev/skills/shyftlabs/continuum/continuum-observability"><img src="https://agentmods.dev/badge/skills/shyftlabs/continuum/continuum-observability/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/shyftlabs/continuum/continuum-observability"><img src="https://agentmods.dev/badge/skills/shyftlabs/continuum/continuum-observability.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 | $0.00070 | $0.01724 |
| Opus 5.5 | $0.00028 | $0.00690 |
| Sonnet 5.5 | $0.00014 | $0.00345 |
| Haiku 4.5 | $0.00007 | $0.00172 |
Grade A, and why
continuum-observability 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 11d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuum Observability Skill
Authoritative source: docs/observability.md.
Quick start
Tracing is on by default if Langfuse is configured.
docker compose --profile observability up -d # brings up Langfuse on :3000
LANGFUSE_ENABLED=true
LANGFUSE_HOST=http://localhost:3000
LANGFUSE_PUBLIC_KEY=… # from Langfuse UI
LANGFUSE_SECRET_KEY=…
Open http://localhost:3000 — every agent run, LLM call, and tool call shows up automatically.
To disable: LANGFUSE_ENABLED=false.
Trace your own functions
from continuum.observability import observe, SpanLevel
@observe(name="my-pipeline", capture_input=True, capture_output=True,
metadata={"version": "v2"}, level=SpanLevel.DEFAULT)
async def run_pipeline(data):
...
Specialized variants:
from continuum.observability import trace_tool, trace_agent
@trace_tool(name="search_db", tool_type="database")
def search_db(query): ...
@trace_agent
async def my_custom_agent_runner(...): ...
Manual spans
from continuum.core.container import get_container
mgr = get_container().tracing_manager
with mgr.trace(name="onboarding", user_id="u1", session_id="s1",
metadata={"tier": "pro"}, tags=["beta"]) as trace:
with mgr.span(name="step-1", input={"foo": 1}) as span:
span.event(name="cache-hit")
span.score(name="quality", value=0.92)
Generation spans (LLM calls):
with mgr.span(name="step") as span:
gen = span.generation(name="llm-call", model="gpt-4o-mini",
input=messages)
# … call the LLM …
gen.end(output=response_text,
usage_prompt_tokens=120, usage_completion_tokens=80, usage_total_tokens=200)
Async-safe trace context
from continuum.observability import (
set_trace_context, restore_trace_context,
get_current_trace_id, get_current_session_id, get_current_user_id,
)
token = set_trace_context(trace_id="abc", user_id="u1", session_id="s1")
try:
await client.chat(messages) # inherits the trace context
finally:
restore_trace_context(token)
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.
- 11d ago Changed · +37 lines e6ac71f79d88
- 1mo ago First seen · 209 lines · 70 tokens per session scan A 1a2a91628edd
continuum-observability is a skill published in the GitHub repository shyftlabs/continuum (84 stars, last pushed 11d ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,724 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 2026-09-03.
Other skills, from other repositories
Debug Snapshot
Capture the host's environment, working directory, recent git activity, and pipeline-relevant state so you can diagnose "what's actually happening on the machine".
git-advanced-workflows
Master advanced Git workflows including rebasing, cherry-picking, bisect, worktrees, and reflog to maintain clean history and recover from any situation. Use when managing complex Git histories, collaborating on feature branches, or troubleshooting repository issues.
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.
triage-issue
Analyze a GitHub issue, verify claims against the codebase, and close invalid issues with a technical response.
principle-attack-the-premise
Apply when two or more fixes that share one premise have failed the same gate. Write the premise down and test it directly before you write another fix that assumes it.
self-improve
Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed.