qcoder-autonomous-issue-solver

A coding skill for taking a clearly defined software issue from reproduction through a tested patch. It uses a bounded loop of investigation, small changes, and verification.

In plain words
What is it for?
Repairing bugs, resolving failing tests, performing maintenance, and implementing narrowly specified features with testable acceptance conditions.
Why use it?
It gives agents a controlled method for fixing bugs or failing tests while recording evidence and stopping when the work is out of scope.

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/echoomegaprime/echo-qcoder/qcoder-autonomous-issue-solver
Any agent
npx skills add echoomegaprime/echo-qcoder --skill qcoder-autonomous-issue-solver
Clone the repo
git clone --depth 1 https://github.com/echoomegaprime/echo-qcoder

Made for: Claude Code, Codex.

Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 496 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 $0.00075 $0.00496
Opus 5 $0.00037 $0.00248
Sonnet 5 $0.00015 $0.00099
Haiku 4.5 $0.00007 $0.00050

Measured yesterday against content hash 8271c3465e95, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

qcoder-autonomous-issue-solver 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.

qwen-skills/qcoder-autonomous-issue-solver/SKILL.md · 35 lines

How it starts

The opening of the file, as written. The whole thing — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QCoder Autonomous Issue Solver

Expected input

Require a concrete outcome, a scoped repository, and an executable or observable acceptance condition. Derive missing technical detail from code, tests, logs, and live state before asking.

Autonomous loop

  1. Read instructions and record the starting branch, revision, dirty files, and acceptance command.
  2. Reproduce the failure or create a failing test. If reproduction is impossible, collect direct evidence and state the falsifiable hypothesis.
  3. Build a minimal repository map with semantic navigation, manifests, entry points, tests, and recent history.
  4. Make exactly one evidence-backed change at a time. Before each change, record hypothesis, supporting evidence, falsifier, rollback, and expected result.
  5. Run the narrowest useful check. On failure, diagnose the mechanism before editing again; retain useful evidence and abandon disproven hypotheses.
  6. Escalate through focused, related, full, and security verification. Use the todo stop guard to continue unfinished work, but never loop past eight repair attempts without a new hypothesis or new evidence.
  7. Inspect the final diff, prove unrelated changes remain untouched, commit only owned files when required, register the result, and checkpoint durable evidence.

The installed mini-SWE-agent CLI is a reference and benchmark tool, not delegated authority. Do not start a second autonomous agent unless the mission explicitly calls for it. Aider, Goose, and OpenHands contribute workflow patterns only until separately benchmarked and promoted.

Facts that must not be inferred

Never infer test success, issue closure, commit or push state, deployment, target ownership, or rollback readiness. Read the source of truth.

Stop conditions

Stop at a doctrine hard limit, missing authorization, irreducible ambiguity that would change product behavior, exhausted evidence paths, or a failing rollback test. Preserve the working tree and report the exact blocker.

Read the full file on GitHub · 35 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. yesterday First seen · 35 lines · 75 tokens per session scan A 8271c3465e95

Subscribe to this mod's changes

qcoder-autonomous-issue-solver is a skill published in the GitHub repository echoomegaprime/echo-qcoder (0 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 496 once invoked, about $0.0004 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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