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 Cognigy/cognigy-plugin --skill troubleshootinggit clone --depth 1 https://github.com/Cognigy/cognigy-pluginWrote 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/cognigy/cognigy-plugin/troubleshooting)<a href="https://agentmods.dev/skills/cognigy/cognigy-plugin/troubleshooting"><img src="https://agentmods.dev/badge/skills/cognigy/cognigy-plugin/troubleshooting.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.00614 |
| Opus 5 | $0.00023 | $0.00307 |
| Sonnet 5 | $0.00009 | $0.00123 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
troubleshooting 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 8d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Troubleshooting
Agent returns empty response
- Inspect the agent flow and tools first:
- list_resources { resourceType: "tool", aiAgentId }
- duplicate
toolIdvalues can cause failed tool execution and empty responses
- Check LLM exists: list_resources { resourceType: "llm_model", projectId } If none: run setup_llm
- Check agent description is not empty: get_resource { resourceType: "agent", id }
- Check endpoint is connected: get_resource { resourceType: "endpoint", id } Verify flowId is set and URLToken exists
create_ai_agent failed
- The tool auto-rolls back created resources on failure. Safe to retry.
- "Could not find entry node": transient issue, retry immediately
- Endpoint step error: check project exists and is accessible
"Resource not found" errors
- All IDs are 24-char hex strings (e.g., 507f1f77bcf86cd799439011)
- UUIDs (36-char with dashes) are referenceIds — most tools need _id, not referenceId
- Use list_resources to find valid IDs
401 / 403 errors, or "who changed this?"
- get_resource { resourceType: "user", id: "me" } returns the account the API key
belongs to, plus its
roles. Checkrolesbefore blaming the API for a 403. createdBy/lastChangedByon any resource are opaque user ids. Never assume one is the current user — compare it to theidfromuser/me. List responses omit them; read them with get_resource { ..., raw: true }.
Finding the most recently touched resource
- Sort server-side instead of paging through everything and comparing by hand: list_resources { resourceType: "project", sort: "lastChanged:desc", limit: 5 }
sorttakesfield:directionand works on any field the resource returns.
setup_llm fails
- See the llm-providers skill for valid provider and model strings
- Verify API key has access to the specified model
delete_resource fails
- Verify the resource ID is a 24-char hex string (not a referenceId UUID)
- Use list_resources to confirm the resource exists before deleting
- Flows, projects and agents are never hard-deleted — delete_resource renames them with a DELETE_ prefix (markedForDeletion: true) so they can be deleted manually in the Cognigy UI. Agent/flow deletion deactivates referencing endpoints (reversible); a renamed project's contents stay live.
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.
- 8d ago First seen · 55 lines · 45 tokens per session scan A b5a10ef3595c
troubleshooting is a skill published in the GitHub repository Cognigy/cognigy-plugin (12 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 614 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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