troubleshooting

troubleshooting is a skill for Claude Code, Codex from Cognigy/cognigy-plugin. It costs 45 tokens per session (614 once invoked), scanned A, original, MIT.

A diagnostic guide for Cognigy problems such as empty agent replies, failed tool calls, missing resources, and language-model setup errors. It uses resource details, IDs, connections, and permissions to narrow down the cause.

In plain words
What is it for?
Use it to investigate agent responses, creation failures, missing resources, endpoint connections, language-model availability, and access errors.
Why use it?
It turns vague failures into checks for common causes, such as duplicate tool IDs, a missing language model, a disconnected endpoint, or using the wrong kind of ID.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Part of the cognigy plugin — 15 skills, 2 agents shipped together

Good fit Use it to investigate agent responses, creation failures, missing resources, endpoint connections, language-model availability, and access errors.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cognigy/cognigy-plugin/troubleshooting
Install

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.

Any agent
npx skills add Cognigy/cognigy-plugin --skill troubleshooting
Clone the repo
git clone --depth 1 https://github.com/Cognigy/cognigy-plugin

Made for: Claude Code, Codex.

Or install cognigy, the plugin that ships this one along with the rest of its 15 skills, 2 agents.

Wrote 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.

agentmods badge for troubleshooting

README.md
[![agentmods](https://agentmods.dev/badge/skills/cognigy/cognigy-plugin/troubleshooting.svg)](https://agentmods.dev/skills/cognigy/cognigy-plugin/troubleshooting)
Your own site
<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>
Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 614 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash b5a10ef3595c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

plugin/skills/troubleshooting/SKILL.md · 55 lines

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

  1. Inspect the agent flow and tools first:
    • list_resources { resourceType: "tool", aiAgentId }
    • duplicate toolId values can cause failed tool execution and empty responses
  2. Check LLM exists: list_resources { resourceType: "llm_model", projectId } If none: run setup_llm
  3. Check agent description is not empty: get_resource { resourceType: "agent", id }
  4. 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. Check roles before blaming the API for a 403.
  • createdBy / lastChangedBy on any resource are opaque user ids. Never assume one is the current user — compare it to the id from user/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 }
  • sort takes field:direction and 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.

Read the full file on GitHub · 55 lines

Changes

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.

  1. 8d ago First seen · 55 lines · 45 tokens per session scan A b5a10ef3595c

Subscribe to this mod's changes

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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