klimkit-diagnose

klimkit-diagnose is a skill for Codex from klimentij/klimkit. It costs 60 tokens per session (424 once invoked), scanned A, original, MIT.

A reproduce-first method for investigating bugs, failed tests, continuous-integration failures, flaky behavior, or unexpected production signals. It traces the evidence to identify the cause before changing code.

In plain words
What is it for?
Use it to reproduce and isolate a failure, form evidence-based explanations, add regression tests where suitable, make a small fix, and rerun checks across the affected area.
Why use it?
It prevents fixes based only on guesses. The process produces a record of the failure, its cause, the change made, and the checks that show whether the problem is gone.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to reproduce and isolate a failure, form evidence-based explanations, add regression tests where suitable, make a small fix, and rerun checks across the affected area.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/klimentij/klimkit/klimkit-diagnose
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 klimentij/klimkit --skill klimkit-diagnose
Clone the repo
git clone --depth 1 https://github.com/klimentij/klimkit

Made for: Codex.

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 klimkit-diagnose

README.md
[![agentmods](https://agentmods.dev/badge/skills/klimentij/klimkit/klimkit-diagnose.svg)](https://agentmods.dev/skills/klimentij/klimkit/klimkit-diagnose)
Your own site
<a href="https://agentmods.dev/skills/klimentij/klimkit/klimkit-diagnose"><img src="https://agentmods.dev/badge/skills/klimentij/klimkit/klimkit-diagnose.svg" alt="Measured on agentmods" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 424 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 $0.00060 $0.00424
Opus 5 $0.00030 $0.00212
Sonnet 5 $0.00012 $0.00085
Haiku 4.5 $0.00006 $0.00042

Measured 8d ago against content hash 0a46d98cea06, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

klimkit-diagnose 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.

skills/klimkit-diagnose/SKILL.md · 31 lines

What it actually says

Klimkit Diagnose

Diagnose before fixing. The output should make the failure, root cause, fix, and regression proof inspectable.

Workflow

  1. Ensure Klimkit's docs-first context exists. If the repo lacks the docs/work/ layout, use klimkit-setup first.
  2. Define the symptom in one sentence and identify the smallest observable failure signal.
  3. Read relevant instructions, prior task notes, logs, and nearby tests before changing code.
  4. Reproduce the failure with the cheapest deterministic command, UI path, log bundle, or fixture.
  5. Minimize the failing surface: isolate the module, route, command, input, environment, or data dependency.
  6. Form hypotheses from evidence. Do not patch based only on plausible explanations.
  7. Instrument only when needed, and remove temporary instrumentation before handoff unless it becomes useful production diagnostics.
  8. Write or update a regression test when the repo has an appropriate test surface.
  9. Make the smallest robust fix.
  10. Rerun the failing signal, the new regression coverage, and any blast-radius checks.
  11. Record proof as a numbered note in the current docs/work/ phase folder: reproduction, root cause, changed files, checks run, and remaining risk.

Evidence Rules

  • A bug is not fixed until the original signal is rerun and passes or is explicitly unavailable.
  • If the failure cannot be reproduced, document what was tried and switch to risk-reduction work only with the user's consent.
  • If the root cause is outside this repo or blocked by missing credentials, write a concise blocker note with exact missing access and the safest next action.

Pair this skill with klimkit-implement for checklist, reflection, and final review gates.

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 31 lines · 60 tokens per session scan A 0a46d98cea06

Subscribe to this mod's changes

klimkit-diagnose is a skill published in the GitHub repository klimentij/klimkit (7 stars, last pushed 28d ago), licensed MIT. It adds 60 tokens to every session and 424 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

atmos-introspection

Introspection & Querying: describe/list commands, config filtering, workspace introspection, dependency graphs, YQ integration, --help= scoped CLI help.

cloudposse/atmos · 37 tokens

atmos-schemas

JSON Schema for Atmos: stack-manifest and atmos.yaml config schemas, IDE auto-completion, validate stacks/schema/config, SchemaStore integration.

cloudposse/atmos · 32 tokens

vs-search-tuning-partial-case

Use when the user provides 1-50 concrete bad-case search queries for one Viking Search app and wants local deterministic fixes. This skill only verifies request-level fine-operation interventions against a read-only baseline scene and delivers a console-ready configuration sheet, validated payloads, and a replay…

volcengine/SearchCLI · 85 tokens

atmos-lint

Atmos Terraform linting with TFLint: standalone atmos terraform lint, component-aware config discovery and toolchain versions, TFLint rule configuration, and lifecycle hooks/CI findings. Use when configuring, running, debugging, or documenting Terraform/OpenTofu linting in an Atmos project.

cloudposse/atmos · 64 tokens

atmos-diagnostics

Atmos diagnostics: machine-readable JSONL event streams, diagnostics.enabled/file/includeoutput, subprocess start/end/output events, masking, and debugging Atmos execution.

cloudposse/atmos · 33 tokens

vs-search

Search runtime and scene management: verify queries, inspect scenes, debug app readiness, and diagnose recall or scene-config issues.

volcengine/SearchCLI · 27 tokens