fix-errors

A root-cause-first workflow for fixing a concrete coding error reported by logs, failed commands, exceptions, or diagnostics. It investigates the smallest relevant code path before changing anything.

In plain words
What is it for?
Use it to fix a failing command, runtime exception, stack trace, type error, or lint failure, then rerun the focused check.
Why use it?
It avoids random fixes and prevents unrelated cleanup from expanding the task. It also distinguishes code problems from environmental issues that cannot be reproduced.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/pymodel/pythinker-cli/fix-errors
Any agent
npx skills add PyModel/pythinker-cli --skill fix-errors
Clone the repo
git clone --depth 1 https://github.com/PyModel/pythinker-cli

Made for: Claude Code, Codex.

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 211 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00023 $0.00211
Opus 5 $0.00012 $0.00105
Sonnet 5 $0.00005 $0.00042
Haiku 4.5 $0.00002 $0.00021

Measured yesterday against content hash 9c6494b35cdb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

fix-errors 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 yesterday.

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.

src/pythinker_code/skills/fix-errors/SKILL.md · 34 lines

What it actually says

Fix Errors

Use when the user provides an error message, stack trace, failing command, type diagnostic, lint failure, or runtime exception.

Workflow

  1. Capture the exact error text, command, affected file, and line if available.
  2. Reproduce or inspect the smallest relevant code path.
  3. State the root cause before editing.
  4. Make the smallest change that fixes the root cause.
  5. Rerun the focused failing command or a targeted equivalent.

Rules

  • Do not make random fixes hoping one works.
  • Do not suppress diagnostics unless the code is intentionally invalid and the test requires it.
  • Do not broaden scope into unrelated cleanup.
  • If the error is environmental or cannot be reproduced, report what was verified and what is blocked.

Output

SUMMARY
ROOT CAUSE
CHANGES
VERIFICATION
REMAINING ISSUES
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. yesterday First seen · 34 lines · 23 tokens per session scan A 9c6494b35cdb

Subscribe to this mod's changes

fix-errors is a skill published in the GitHub repository PyModel/pythinker-cli (20 stars, last pushed 5d ago), licensed Apache-2.0. It adds 23 tokens to every session and 211 once invoked, about $0.0001 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 2026-08-30.

Related

Other skills, from other repositories

release-notes

Generate user-facing release notes for Intelligent Terminal. Use when asked to write release notes, changelog, what-is-new summary, or prepare a release. Compares git commits between releases, looks up PR-linked issues and community contributors, then outputs formatted notes with "Verbed + Impact + Scenario" style…

microsoft/intelligent-terminal · 73 tokens

peer-review

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…

xintaofei/codeg · 71 tokens

scientific-critical-thinking

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…

xintaofei/codeg · 63 tokens

statistical-analysis

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required…

xintaofei/codeg · 111 tokens

statistical-power

Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers…

xintaofei/codeg · 190 tokens

pr-integration-test

Design, implement, and validate Intelligent Terminal integration tests for a target pull request or regression. Use when asked to add PR integration tests, convert a bug fix into E2E coverage, prove existing behavior still works, map tests to the release checklist, or verify E2E reports mark checklist cases complete.

microsoft/intelligent-terminal · 66 tokens