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 theafh/ai-modules --skill task_fixgit clone --depth 1 https://github.com/theafh/ai-modulesWrote 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/theafh/ai-modules/task_fix)<a href="https://agentmods.dev/skills/theafh/ai-modules/task_fix"><img src="https://agentmods.dev/badge/skills/theafh/ai-modules/task_fix/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/theafh/ai-modules/task_fix"><img src="https://agentmods.dev/badge/skills/theafh/ai-modules/task_fix.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.04813 |
| Opus 5.5 | $0.00039 | $0.01925 |
| Sonnet 5.5 | $0.00020 | $0.00963 |
| Haiku 4.5 | $0.00010 | $0.00481 |
Grade A, and why
task_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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
task_fix
<task_fix_skill>
<when_to_activate> Activate when the user wants the task backlog as a whole audited, tidied, or judged for backlog-coherence:
- "Health-check my tasks" / "clean up the backlog" / "audit the task list" / "lint and fix the tasks tree."
- "Do these ship together?" / "check backlog coherence" / "is the backlog consistent?" / "should some of these be altered or deferred to stay coherent with each other and the current code?"
- "Resolve the backlog judgement calls autonomously" / "run the autonomous task tree shaper" / "let task_fix handle the splits."
Ordinary backlog phrasing is enough for the second group: coherence, consistency, or whether the selected tasks ship together. No special jargon is required, because every everyday invocation already runs the assessment; that phrasing emphasizes or narrows the run rather than switching it on.
Route elsewhere when the user wants to judge a single task's readiness before building (task_check), automatically repair one task until it is ready (task_auto_check), choose what to work on next (task_select), verify one believed-done task against the codebase (task_audit), create a task (task_create), implement one (task_implement), or close one out (task_finish).
</when_to_activate>
<design_note>
task_fix runs inline by default because most tasks/ trees are small and the common fixes are mechanical. The autonomous path is an escalation inside this skill, not a second user-facing skill: auto_shaper_task is spawned only when the user asks for autonomous judgement-call resolution or confirms that the tree is large enough to justify the read fan-out. Backlog-coherence reconcile is assess-then-one-writer under that same exclusive-writer rule: the assessment always reports and never writes, and a run that reconciles picks exactly one writer for its accepted set: either task_fix writes inline, or auto_shaper_task writes serially as the sole writer, never both in one run. The spawned assess agents provide read-side proposals and verification; file-creating splits, task moves, cross-reference rewrites, and frontmatter stamps stay with the single writer so parallel writers never race on the shared link graph.
</design_note>
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 Changed 88345c6eb37b
- 17d ago Changed 5d3b79f3f857
- 19d ago Changed 76005ca41c13
- 20d ago Changed a7da030427c3
- 1mo ago Changed · +2 lines e87a1fc17f97
- 1mo ago Changed · +1 lines f36a71fb5d84
- 1mo ago Changed f3955edc732f
- 1mo ago First seen · 97 lines · 98 tokens per session scan A e740e805920a
task_fix is a skill published in the GitHub repository theafh/ai-modules (38 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 4,813 once invoked, about $0.0004 per session on Opus 5.5. 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.
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