Instrument an existing agent

Retrofit Progress Observability onto an existing codebase with the smallest possible diff, then run it once so spans start flowing. For a project that does not exist yet, this is the wrong skill — it only edits what is already there.

This skill writes code — the instrumentation edits and nothing else. It never restructures the app, and it never reads the platform back: confirming the traces arrived is left to /health-check.

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1 · Detect — before touching anything

Scan the repo and report what you found, then the diff you intend to make, before editing:

• Language & entry point — pyproject.toml/requirements.txt,

package.json (check "type": "module" — it decides the wiring path), *.csproj.

• LLM SDKs and frameworks in use — imports of openai, anthropic,

langchain, llama_index, @langchain/*, Microsoft.Extensions.AI, etc. Check each against the supported-instruments list in the language reference. A client pointed at an OpenAI-compatible endpoint (OpenRouter, LiteLLM, vLLM, Together, most gateways) counts as OpenAI — see the reference. A .NET agent built from AIProjectClient (Azure AI Foundry) has no wrappable chat client — different wiring; see the Foundry section in references/dotnet.md.

• Existing telemetry — OpenTelemetry setup, Traceloop, or a previous

Progress Observability init. Look for it before writing anything, and check the reference for ordering rather than assuming init belongs first.

Python (measured). The SDK attaches to a provider the app already installed, so Progress ends up alongside the app's exporter rather than replacing it — but only if init runs after the app's own setup. Init first and the app's settracerprovider() becomes a no-op with one warning line, killing its existing telemetry while Progress spans keep arriving. Grep for settracerprovider / TracerProvider(. Details in references/python.md.

.NET (measured). No ordering constraint: providers coexist and the app's own tracing is unaffected. The hazard is different — a chat client that already has .UseOpenTelemetry() records every call twice once .AddObservability() is added, doubling token and cost figures. Grep for UseOpenTelemetry before wiring and report the overlap rather than removing either layer yourself. One hit is not overlap: UseOpenTelemetry(sourceName: ObservabilityTracer.SourceName) on an agent builder is Progress wiring itself — leave it. Progress spans land in their own trace by design; see references/dotnet.md.

TypeScript: not measured here. Don't carry the Python behavior across; if you hit an app with existing OTel, say the interaction is unverified rather than guessing, and check the traces on both sides before declaring success.

• Scope — a monorepo, several services, or more than one agent needs a

"which one?" question before you touch anything. Don't pick for the user.

• Config style — dotenv, user secrets, plain env — instrumentation config

should arrive the same way the app's other secrets do.

• Package manager — lockfiles decide the install command: uv.lock /

poetry.lock / Pipfile for Python, yarn.lock / pnpm-lock.yaml for Node (defaults: pip / npm); .csproj always means dotnet add package.

• Meta package present? (Python) — if the dependency list has

langchain-core or llama-index-core but not the langchain / llama-index meta package, note it: adding it is a required Wire edit below. Check the dependency file, not the imports.

If a framework in use is not in the supported list — DSPy, AutoGen, Pydantic AI, Semantic Kernel and Google ADK are the common ones — say so plainly and offer the decorator / manual-span fallback from the reference. Never imply auto-instrumentation covers a framework it doesn't. LangGraph is supported in Python (it rides the LangChain instrumentor and produces full graph topology); it is not supported in the JS SDK. Check the language reference rather than assuming either way.

2 · Wire — follow the language reference exactly

Open the matching reference and use its snippets verbatim — they are verified against the published packages, not reconstructed:

• references/python.md — progress-observability (PyPI)
• references/typescript.md — @progress/observability (npm)
• references/dotnet.md — Progress.Observability.Instrumentation (NuGet)

Rules that hold across all three:

• Init at process start, before any LLM client exists. In ESM Node that

means the hooks import and Observability.instrument() run before the app is even imported; in Python, before clients are constructed; in .NET, before the agent is built. Two exceptions, both in the language references: an app that installs its own OpenTelemetry provider (init goes after that), and Haystack (init goes after import haystack.tracing). Check the reference before assuming earlier is safer — in both cases it isn't.

• Minimal diff. Typically: one dependency, one import, one init call, env

var wiring, and a flush-on-exit. If you find yourself moving app code around, stop and reconsider — including "just" exporting a module-scope script so you have something to wrap. That is restructuring, and it is never the answer.

• App with no LLM calls: instrument every step, not the entry point alone.

Decorators (Python/.NET) and wrapFunctionWithSpan (TS) are the only span source in that app, so wrap the entry point and each internal step and every tool-like callable, on the functions the app already has. One span around the top is the failure mode to avoid: it produces a clean, plausible trace that misrepresents a multi-step pipeline as a single unit, and nothing in the output reveals it. Each reference has the kind-by-kind table. When auto-instrumentation is doing the work instead, add none of them.

• Python + LangChain or LlamaIndex: the meta package is a REQUIRED edit.

langchain-core / llama-index-core alone leave the instrumentor off: the app runs, LLM spans arrive, and no structure is ever emitted — silently. (Measured: 2 spans → 8 for LangChain, 1 → 17 for LlamaIndex, from that one line.) Add langchain / llama-index to the dependency file alongside the -core package, never in place of it — the app imports langchaincore / llamaindex.core by name, so those stay declared. Say in your report that you added it and why; gate table in references/python.md.

• Python: declare httpx alongside the SDK. traceloop-sdk imports it

without declaring it, so importing progress.observability dies with ModuleNotFoundError: No module named 'httpx' in a project with no other source. Most LLM SDKs provide it transitively; add it every time regardless.

• Never ask the user to paste a key into the chat. Reference the env var

or config entry by name and let them set it themselves, in their own shell, .env, or secret store. Read config to detect what exists; never echo a secret's value back.

• A missing key must be loud. In Python and TS, read it so an unset

variable raises (os.environ["OBSERVABILITYAPIKEY"], not .get(...)). In .NET, follow the reference's optional-tracing pattern instead: skip init with a prominent warning and keep the app running. Either way, the one forbidden outcome is silence — an app that runs, exports nothing, and gives no clue why.

• Keys via config, never hardcoded. The key here is the Integration key

(acp…) from Progress Observability → API Keys. It is not the MCP key (acm_…) — that one belongs to the coding agent's environment for the verify step, not to the app.

• Declaring the dependency is a file edit, and it is never optional. Add the

SDK to package.json / requirements.txt / pyproject.toml / .csproj yourself, creating the dependencies section if the manifest has none. Do this even when you have been told not to run installs, and even when you cannot run them — an import of a package the manifest doesn't declare is ERRMODULENOT_FOUND the moment anyone runs the app. Telling the user to run npm install … afterwards is not a substitute for the edit; running the installer is the separate, optional half.

• Check before adding. If the SDK is already in the manifest, don't re-add

it — note its version and move on (suggest an update only if it's older than the reference's verified version). Otherwise add the latest, through the project's own package manager where you can run it (each reference lists the commands), after a quick registry check of the current version — if the registry is ahead of the reference's "verified against" version, say so in your report rather than assuming the reference still holds.

• app_name is the platform identity — a short stable slug. Everything in

the platform (and every other skill in this plugin) filters by it, so confirm it with the user. Read it from the environment with the agreed name as the fallback, rather than hardcoding it outright:

``python appname=os.environ.get("OBSERVABILITYAPP_NAME", "my-app") ``

Same shape in TS (process.env.OBSERVABILITYAPPNAME ?? 'my-app') and .NET. The literal still documents the intent, but the same build can then report as a different service per environment — dev, staging, prod, CI — with no code change. Hardcoding it means every environment lands in one bucket.

Add a variable; don't repurpose an existing one. Where the app already names itself for its own reasons — an agent's name:, a service registration, a CLI banner — leave that alone and introduce a separate app_name. Pointing an existing field at your new env var makes the app's behavior change with a telemetry setting, which is app logic edited for an instrumentation task.

• Content capture default is ON. Prompts/completions are sent unless

content tracing is disabled. For apps handling sensitive data, offer the content off-switch (each reference shows it) — metadata keeps flowing.

3 · Run once

Have the user run the app so it emits at least one trace (run it yourself if it is runnable here). If LLM credentials aren't available, the decorator/manual span path in each reference produces real spans with no LLM call — wire one workflow-decorated function and run that, so the pipeline can be proven end-to-end before the model keys exist.

4 · Confirm & hand off — no platform read

Instrumentation is finished once the edits are in and the app has emitted at least one trace. This skill does not read back over MCP. Confirming that the spans actually landed is a separate, read-only step the user runs when they want it — never something this skill does automatically. Do not call listobservations, getobservation_details, or any other platform tool here.

Report what you already know from the edits themselves — the language, the framework, and whether auto-instrumentation or manual spans are carrying the trace — then tell the user plainly that wiring is done, and hand off with where to confirm the traces:

Wiring is complete. Run your agent so it produces some traffic, then open
observability.progress.com and confirm the traces are flowing in for
service <app_name> — they should appear within a minute of the run.

An unverified wiring is a normal, healthy outcome — especially for a new user on the free tier, who has no MCP key to read traces with. Treating it as a failure is a bad first experience for exactly the people most likely to be trying the product for the first time.

Never touch the platform from this skill — no reads, no writes. The only changes it makes are the local instrumentation edits. <!-- copilot:end -->