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 softspark/ai-toolkit --skill fixgit clone --depth 1 https://github.com/softspark/ai-toolkitWrote 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/softspark/ai-toolkit/fix)<a href="https://agentmods.dev/skills/softspark/ai-toolkit/fix"><img src="https://agentmods.dev/badge/skills/softspark/ai-toolkit/fix.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 31 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00038 | $0.01140 |
| Opus 5 | $0.00019 | $0.00570 |
| Sonnet 5 | $0.00008 | $0.00228 |
| Haiku 4.5 | $0.00004 | $0.00114 |
Grade A, and why
fix 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 4d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix Command
$ARGUMENTS
Attempts to fix code errors autonomously.
Usage
/fix <file_or_scope>
# Example: /fix src/utils.ts
Automated Error Classification
Before entering the fix loop, classify errors to prioritize auto-fixable ones:
# Pipe lint or test output
ruff check . 2>&1 | python3 ${CLAUDE_SKILL_DIR}/scripts/error-classifier.py
mypy src/ 2>&1 | python3 ${CLAUDE_SKILL_DIR}/scripts/error-classifier.py
npx eslint . 2>&1 | python3 ${CLAUDE_SKILL_DIR}/scripts/error-classifier.py
The script outputs JSON with:
- total_errors: count of all parsed errors
- auto_fixable_count: errors that tools can fix automatically (e.g., F401 unused imports, formatting)
- manual_count: errors requiring human/agent intervention
- tools_detected: which linters produced the output (ruff, mypy, eslint, tsc, phpstan)
- errors[]: each error with file, line, code, message, and auto_fixable flag
- suggested_order: files to fix, auto-fixable first
- fix_strategy: recommended approach (auto-fix first, then manual)
Use this to run auto-fixers (e.g., ruff check --fix .) before spending time on manual fixes.
Protocol (The "Fix Loop")
-
Analyze: Run validation to get the exact error message.
# Get error npm test src/utils.ts 2>&1 | tee error.log -
Diagnose: Analyze
error.log.- Use
debugging-tacticsskill. - Trace the error to the source line.
- Use
-
Patch: Apply a fix.
- Use
sedorwrite_file.
- Use
-
Verify: Run validation again.
- If PASS: Stop.
- If FAIL: Repeat (Max 3 retries).
Safety Limits
- Max Retries: 3
- Scope: Only modify the specified files.
- Stop Condition: If new errors appear that are totally different, STOP and ask user.
Example Flow
User: /fix app.py
Agent: Running tests... FAIL (NameError)
Agent: Fixing app.py (Import missing module)
Agent: Running tests... PASS
Agent: Fixed NameError in app.py
Rules
- MUST know the exact symptom (error message, failing test, lint code) before editing — guessing is not fixing
- MUST verify the fix by rerunning the same command that exposed the problem, not a different validator
- NEVER modify tests to make them pass — fixing the test is not fixing the bug
- NEVER touch files outside the declared scope — scope creep hides regressions
- CRITICAL: hard-stop after 3 iterations. If the fix loop has not converged, the problem is deeper than
/fixhandles — escalate to/debug. - MANDATORY: if new, unrelated errors appear during a fix attempt, stop and ask the user — do not chase them
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.
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.
- 4d ago First seen · 104 lines · 38 tokens per session scan A 5cb4e2f97477
fix is a skill published in the GitHub repository softspark/ai-toolkit (170 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 1,140 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 2026-09-03.
Other skills, from other repositories
cline-fix-volatile-msg
Ladder-aware Cline Anthropic caching — verify the rolling read/write ladder on the wire, then add the tools breakpoint and tune TTL. Updated for the 2026-08 AI-SDK monorepo.
continue-fix-volatile-msg
Ladder-aware Continue Anthropic caching — verify the rolling ladder on the wire, then enable it by default and add TTL coverage.
opencode-detect-openai-compat
OpenCode's caching detection misses OpenAI-compatible proxies routing to Anthropic/Bedrock. Broaden the predicate.
cline-pin-timestamp
Cline's system prompt includes a timestamp that may be recomputed per request, invalidating the system-prompt cache.
log-analyzer
Senior-SRE log analysis specialist. Use when investigating incidents from logs, triaging error spikes, extracting timelines, correlating distributed traces, or separating signal from noise across plain-text, JSON (slog/zap), syslog, journald, container, and Kubernetes logs. ALWAYS use when the user asks to "analyze…
incident-postmortem
Incident post-mortem specialist for writing blameless post-mortems, extracting timelines from logs/events, conducting root cause analysis (5-Why, fishbone), classifying severity, and generating tracked action items. ALWAYS use when writing a post-mortem, reviewing an incident, extracting a timeline, performing root…