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 agentmods add skills/theafh/ai-modules/task_finishnpx skills add theafh/ai-modules --skill task_finishgit 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_finish)<a href="https://agentmods.dev/skills/theafh/ai-modules/task_finish"><img src="https://agentmods.dev/badge/skills/theafh/ai-modules/task_finish.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00050 | $0.01568 |
| Opus 5 | $0.00025 | $0.00784 |
| Sonnet 5 | $0.00010 | $0.00314 |
| Haiku 4.5 | $0.00005 | $0.00157 |
Grade A, and why
task_finish 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 3d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
task_finish
<task_finish_skill>
<when_to_activate> Activate when the user wants one task closed out:
- "Finish this task" / "mark
<task>done" / "this task is implemented." - "Defer this" / "park
<task>" / "drop this for now." - "Archive this task."
Route elsewhere when the user wants to create a task (task_create or the base task skill), assess readiness before building (task_check), automatically repair readiness issues before building (task_auto_check), choose what to work on next (task_select), do the implementation work (task_implement), verify a believed-done task against the codebase (task_audit), or list / query / update tasks (the base task skill).
</when_to_activate>
<path_resolution>
The bundled scripts (discover_tasks.sh, lint.py) ship in scripts/ next to the base task skill's SKILL.md, not next to this one. After reading that base SKILL.md (per <authority>), resolve each script's absolute path by combining the directory you loaded it from with scripts/<script-name> and invoke that absolute path — never a bare scripts/..., which resolves against the current working directory (the target project) rather than the skill, and so finds the project's own scripts/ or nothing. If the first invocation reports a missing file, re-resolve the absolute path once before treating the script as failed.
</path_resolution>
- Identify the target task. Run the base skill's
<discover>step to resolvetasks/, and confirm which single task file is being closed. Read it so you know its currentstatus, links, and the work it claims. - Decide the outcome. Set
finishedwhen the work is done and shipped, ordeferredwhen the task is parked or dropped and not pursued for now. When the user's intent is ambiguous between the two, ask before changing anything. - Verify before a
finishedclose when needed. Marking a taskfinishedasserts the work is genuinely done, so make the verification decision from the task's currentstatus: when it isaudited, treat the codebase-verification gate as already satisfied and proceed to close-out; when it isimplementedor any other non-auditedlive status being closed asfinished, runtask_audit(the read-only gate) or carry out its check, and resolve or report any gap before closing. Trust a currentauditedstamp; when you have concrete evidence the code changed since that audit, re-verify rather than relying on the stamp. On the trust-the-stamp path, readdesign-extendedfrom task frontmatter as the design-extension signal the close-out rule consumes, reading absence asfalseper the base<frontmatter>entry, so the close proceeds without re-reading the code the shortcut deliberately skips. Absence and a recordedfalseboth decline the refresh, and the report keeps them apart rather than presenting an assessment nobody made as a decision. Adeferredclose skips this step, since parking a task makes no claim about completion. Mark a taskfinishedon codebase evidence, not on prose. This trust-the-stamp gate is the lifecycle-scale instance of the base<verification_economy>rule: the audit is the prior run whose evidence stands until an input changes, and code that changed since the audit is that input change — so the stamp is trusted by default and re-verification fires exactly on fresh evidence. - Run the base skill's
<archive>close-out. Follow thetaskskill's<archive>workflow end to end, including theARCHITECTURE.mdpresence gate and refresh decision the base step defines. Those rules live in the base skill; follow them there rather than restating them here.
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.
- 3d ago First seen · 65 lines · 50 tokens per session scan A 6e6b1e88406b
task_finish is a skill published in the GitHub repository theafh/ai-modules (38 stars, last pushed 4d ago), licensed MIT. It adds 50 tokens to every session and 1,568 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 2026-08-30.
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