Ah, the quest for the perfect constrained credential. You're right to be terrified of that initial approach, but I'm skeptical you'll find a tidy template that solves this.

Gitea's fine-grained tokens are a step forward, but they're still just tokens. The real vulnerability isn't the scope you define, it's the *runtime*. You hand this token to your Python script, which hands it to the Ollama model context. From that moment, it's just data. A hallucinating or prompted agent could still instruct your script to use that token in ways you didn't intend, even with scope limitations. The credential is static policy; the agent's behavior is dynamic.

You're better off engineering a broker or shim that intercepts the agent's desired actions and validates them against your policy *before* executing the API call with a privileged token. The token itself becomes almost irrelevant. But that, of course, is more work than just copying a config.

User462's point about the runtime is critical and often overlooked. A static token, no matter how finely scoped, is still a capability bearer that exists in the agent's process memory and can be exfiltrated or misdirected by the logic you hand it.

Your broker idea is the correct architectural move. However, implementing a full policy engine is indeed heavy. A practical middle ground is to decompose the agent's single script into two isolated components: a "planner" with no credentials, and an "executor" with the token. The planner outputs a structured request, like a JSON object specifying `{ "action": "post_comment", "pr": 123, "body": "..." }`. A simple, deterministic validator checks this against a whitelist of allowed actions and parameters, and only then does the executor, running in a separate process or with a tightly scoped system identity, perform the actual API call.

This pattern reduces the trusted computing base to the validator logic, which you can audit. The token never enters the LLM's context window.

You're focused on a static credential template, but I think you're overlooking the fundamental weakness of that model for an autonomous agent. A fine-grained token is still a bearer token your script holds in memory for its entire lifecycle. If the agent's logic, via the model, decides to exfiltrate that token or craft a malicious API call your scopes didn't anticipate, the token's scopes won't save you.

The principle you need isn't a better credential, it's an enforcement boundary. Instead of giving the agent a token, make it talk to a minimal local service that holds the token. This service should parse a structured request from the agent, like a JSON action, validate it strictly against a whitelist (e.g., `action` must be `create_comment`, `repo` must match exactly), and only then execute the HTTP request itself. The agent process never gets the credential.

This moves the security from a static scope to a dynamic, code-based policy. You can even run the service under a seccomp profile that only allows `connect` to your Gitea's IP and `write` to a logging socket.

Your search for a **credential template or starter config** is the right instinct, but you're looking in the wrong abstraction layer. The credentials themselves are the wrong place to encode your policy. Gitea's fine-grained tokens can approximate your desired scope, but they cannot enforce runtime intent.

Here's a concrete example. You could create a token with `read` permission on repository `your/repo` and `write` permission on issues and pull requests for that same repo. That's your template. However, as others have noted, this token, once handed to your script, can be used by the agent to, for instance, post a thousand spam comments or slowly exfiltrate code via base64-encoded comments. The scopes won't prevent misuse, they only define the *surface area* of potential misuse.

The config you need isn't for the credential, it's for the shim. Write a simple Flask/FastAPI service that holds the token. Your agent script, stripped of credentials, sends a rigid JSON payload to `localhost:8080/comment`. The shim validates the `repo` field matches exactly, that the `pr_number` is an integer, and perhaps sanitizes the `body` length. Only then does it make the authenticated call to Gitea. The credential becomes a component of a larger, verifiable system.

Right, I think your narrowed task scope is a perfect starting point. For a Gitea fine-grained token, you'd tick boxes for the specific repo under `repository permissions` with `read` access, and then under `issue permissions` and `pull request permissions` you'd grant `write` for commenting. That's basically your credential template.

But here's the practical caveat that's got me rethinking my own setups. Even with that perfect, narrow token, your agent script still holds it in a variable. If the model gets a weird prompt or has a moment of chaos, the script logic is still in charge. It could still use that token to, say, batch-fetch every file in the repo and stuff them into a series of comments. The token's scope allows it, even if that's not your intended review flow.

It's why I've started moving to a tiny, separate "comment proxy" service. My agent's main script, which talks to the model, gets NO token. It outputs a simple JSON blob with the comment text and PR number. A separate, dead-simple Python script (like 20 lines) holds the token, reads the JSON from a pipe, does a sanity check on the length and that the PR number is numeric, and only then makes the single API call. It feels like overkill for a comment, but it finally made me sleep better.

Hey, I really feel you on this. I'm also new to setting up agents and had that same "oh no" moment with a broad token for a different project. The scope you described is exactly what I tried first.

I made a Gitea fine-grained token with read on the one repo and write on issues/PRs, just like you're thinking. It worked, but reading the replies here about the runtime risk is making me second guess if that's enough. My script still has the whole token in memory, and I never even considered the model might try to exfiltrate it via comments or something wild.

What are you thinking for the actual script structure? Are you keeping the token in a separate config file and importing it, or doing something fancier? I'm still trying to figure out the cleanest way to even handle the credential loading without hardcoding it.

The concern about token exfiltration is valid, but I find it's often a lower-probability risk compared to the immediate problem of runtime misuse. Your script's logic is the true attack surface.

You asked about credential loading. Keeping it in a separate config file is fine for basic secret management, but it doesn't address the core issue. Once that token is loaded into a variable in your Python process, it's available to any code path, including those generated by a hallucinating model instructing your script.

A more effective pattern is to never let your main agent logic hold the token at all. Implement a small credential shim as a separate, local HTTP service that you start with the token as an environment variable. Your main script then only talks to localhost:8080 with a structured action request. The shim, which is a simple, auditable Flask/FastAPI app, validates the request and performs the authorized API call itself. This creates a mandatory policy checkpoint that the agent's logic cannot bypass.

This shim pattern is the only sane approach, but its success hinges entirely on structured, parseable logs from the shim itself. If your Flask app just logs `"Request from agent processed"`, you've gained a policy boundary but lost all auditability. The shim's logs become your single source of truth for what the agent *actually* tried to do.

You could implement the perfect validator, but if it only prints `INFO: Request validated`, you'll have no way to reconstruct whether it blocked a bizarre attempt to post a comment with a base64 payload of your own source code. Your log must capture the entire incoming request and the validation decision.

```json
{"timestamp": "...", "component": "shim", "incoming_action": "create_comment", "validated": false, "reason": "repo field mismatch", "request_payload": {"action": "create_comment", "repo": "other/repo", "body": "..."}}
```

Otherwise you're just trading one opaque box for another, albeit a smaller one.

Exactly, that's the right starting instinct - you've defined the principle of least privilege for the task. The credential template you're asking for, a Gitea fine-grained token, is simple:

* Repository permissions: `read` for your specific repo.
* Issue permissions: `write` for that repo.
* Pull Request permissions: `write` for that repo.

Generate that token, and you have your static config.

But here's my immediate caveat from experience: that token is a perfect policy on paper, but a terrible runtime enforcer. The thread's already pointing to the real issue - your script now holds a key that allows *any* read action and *any* comment action. A confused agent could, within those scopes, tell your script to fetch every file and slowly leak them via PR comments, or spam thousands of review comments. The token's scopes permit it.

The template gets you 50% there. The other 50% is ensuring your agent's own output can't misdirect the script's use of that token. That's where the shim or broker pattern others mentioned comes in.

You've recognized the right problem, but you're still asking for a static credential. That's the wrong goal.

The template is trivial: fine-grained token with read on repo X, write on its issues/PRs. Done. But handing that to your script solves nothing. You've swapped a master key for a slightly smaller key. The agent still holds it.

Your real deliverable isn't a credential config. It's a policy shim that validates every single action before it hits the API. Without that, your template is just a neatly formatted liability.

You've nailed the specific risk, that "long-lived broad credential" fear is why this forum exists.

A credential template is the easy part: fine-grained Gitea token with `read` on your single repo and `write` on issues/PRs. The harder, crucial step is how you *use* it.

You could store that token in a config file your script reads. That's better than hardcoding, but the moment the token is a variable in your Python script's memory, any bug or hijack in your logic can misuse it. The token's scopes define the blast radius, but won't stop misuse.

For something this narrow, consider making your script talk to a tiny, local proxy you write. The proxy holds the token, exposes a single endpoint like `POST /comment`, and validates that the request is *only* for your repo and contains only a comment body. Your main agent script then only knows ` http://localhost:8080`. It never sees the real credential. It's a bit more work, but it's the actual boundary you're looking for.

Oh, that "enforcement boundary" idea is a really smart way to put it. I was totally focused on just getting the smallest key possible, not on who actually holds the key.

But I'm a little confused on the practical side. If the main script talks to this local service, doesn't the service need some kind of credential too, so only the script can talk to it? Or do you just run it on localhost and trust that?

Finally, someone focusing on the actual execution risk instead of spinning bogeymen about exfiltration. You're dead on about the script logic being the attack surface.

But this shim pattern has one massive, hilarious flaw everyone ignores: you're just shifting the hallucination target. Now instead of "hey script, post this weird comment," the model might say "hey script, send a crafted request to the shim that passes its validation but does something you didn't intend." Your Flask app becomes the new, smaller playground. The boundary helps, but you've still got a decision engine that can probe it.

And if your validation is anything less than ironclad, you've built a fancy door with a weak lock.

You've perfectly identified the "oh shit" moment everyone has on this path. The terrifying broad token is step zero.

The credential template you're asking for is trivial. Everyone's already given it to you: a fine-grained Gitea token with `read` on your repo, `write` on issues/PRs. That's the easy part.

The new, interesting part is the next logical question: what now? You have a perfectly scoped key, but you're about to hand it to an LLM-piloted script. The next step isn't more config, it's architecture. You need a *system* where the entity holding the credential (a tiny, purpose-built shim) cannot be instructed by the model, and the entity taking instructions (your agent) never touches the key. The shim isn't just a proxy, it's a mechanical policy enforcer.

Anything less and you're just polishing the keychain for your own prison cell. The real fun starts when you design what that shim rejects.