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 dongshuyan/compass-skills --skill task-clarifiergit clone --depth 1 https://github.com/dongshuyan/compass-skillsWrote 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/dongshuyan/compass-skills/task-clarifier)<a href="https://agentmods.dev/skills/dongshuyan/compass-skills/task-clarifier"><img src="https://agentmods.dev/badge/skills/dongshuyan/compass-skills/task-clarifier/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/dongshuyan/compass-skills/task-clarifier"><img src="https://agentmods.dev/badge/skills/dongshuyan/compass-skills/task-clarifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
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 →
- low Privilege Escalation · line 64 Skill requests more permissions than appear necessary for its stated functionality. Review if elevated access is justified.Fix: Request only the minimum permissions required. Document why each permission is needed. Remove broad permissions like '*' or 'all'.
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 | $0.00074 | $0.02470 |
| Opus 5 | $0.00037 | $0.01235 |
| Sonnet 5 | $0.00015 | $0.00494 |
| Haiku 4.5 | $0.00007 | $0.00247 |
Grade A, and why
task-clarifier 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 9d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Clarifier
Language Rule
All output directed at the user — questions, options, recommendations, confirmations, summaries — must be written in the user's language. Detect the user's language from their message. Default to Chinese when unknown. If the user writes Chinese, every word of output must be Chinese. Do not use English in any user-facing output unless the user writes in English.
Three Goals (all must be met before ending)
- Help the user fully understand their own needs — including dimensions the user has not yet considered, latent contradictions, and implicit assumptions that materially affect the outcome
- Help the AI fully understand the user's needs — completely, specifically, unambiguously; no unconfirmed dimension is filled with a default value
- Show the user that the AI has fully understood their needs — present the understanding in an explicitly verifiable form; all inferences listed separately for the user to confirm one by one
Startup Reads
On activation, silently attempt the following best-effort reads. If any source is unavailable, unsupported, or fails, continue without it — do not block the clarification loop.
Use the current agent or harness's native skill mechanism first. This skill must work in Codex, Claude Code, OpenClaw, OpenCode, and other agents that can read a SKILL.md file. Do not assume a specific agent name, skill root, home directory layout, shell, or operating system.
User profile summary — If $user-profile-keeper is available, read its clarification_summary view (low-sensitivity, need-alignment-related preference data only). Prefer the agent's built-in skill invocation, MCP/tool bridge, or documented companion-skill API.
Task forest — If $task-forest is available, read the current workspace task list and open todos. Prefer the agent's built-in skill invocation, MCP/tool bridge, or documented companion-skill API.
If direct script execution is the only available integration path, first discover the companion skill directory through the current harness's skill registry or by resolving the repo-local skills/<skill-name>/ directory from this SKILL.md location. Construct file paths by joining path segments with the host language or runtime path utilities so the same logic works on macOS, Linux, and Windows. Use the operating system's available Python launcher (python3, python, or py -3) only after discovery succeeds. Never hard-code paths such as ~/.codex/..., ~/.agents/..., absolute POSIX paths, or Windows drive paths.
What ships with it
2 files 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.
- 9d ago First seen · 199 lines · 74 tokens per session scan A dca80e18fa0a
task-clarifier is a skill published in the GitHub repository dongshuyan/compass-skills (724 stars, last pushed 13d ago), licensed MIT. It adds 74 tokens to every session and 2,470 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 2026-08-30.
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