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 shengsheng90/DSH-taskboard --skill manage-taskboardgit clone --depth 1 https://github.com/shengsheng90/DSH-taskboardWrote 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/shengsheng90/dsh-taskboard/manage-taskboard)<a href="https://agentmods.dev/skills/shengsheng90/dsh-taskboard/manage-taskboard"><img src="https://agentmods.dev/badge/skills/shengsheng90/dsh-taskboard/manage-taskboard/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/shengsheng90/dsh-taskboard/manage-taskboard"><img src="https://agentmods.dev/badge/skills/shengsheng90/dsh-taskboard/manage-taskboard.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 | $0.00088 | $0.00736 |
| Opus 5 | $0.00044 | $0.00368 |
| Sonnet 5 | $0.00018 | $0.00147 |
| Haiku 4.5 | $0.00009 | $0.00074 |
Grade A, and why
manage-taskboard 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Manage Taskboard
Use the in-process taskboard_* tools for Agent work. Use dsh-taskboard only for human-operated scripts and interoperability; do not make the model shell out when a native tool exists.
Execute one task
- Call
taskboard_listwith the exact project id. Prefer an eligibletodo; do not selectbacklog, archived, dependency-blocked, or already claimed work. - Call
taskboard_getimmediately before claiming. Preserve the opaque task id and currentversionexactly; never derive an id from a display key such asDSH-42. - Call
taskboard_claimwith that id and version. Treat a stale-version or claim conflict as a signal to reread and reconsider, not to retry blindly. - Read the full description, comments, relations, dependency state, development context, and attachment references before changing files. Work only in the task's declared workspace, branch, or worktree.
- Complete the work and run relevant verification. If requirements change, call
taskboard_getagain before continuing. - Record the final result with
taskboard_commentortaskboard_submit_review, always using the version returned by the latest read or write. Never modify the task description. - Call
taskboard_submit_reviewwith a concise result comment and concrete verification evidence. This moves owned work toin_review; it never marks workdone.
Keep every write version-linear: after any successful comment, relation, block, or other mutation, use its returned version for the next write. If another actor wins the race, reread the task and reconcile instead of overwriting their change.
Handle exceptional outcomes
- Call
taskboard_blockonly on the in-progress task you hold the claim for, and only with a concrete reason when work cannot proceed. Include the missing dependency, decision, permission, or external condition. Atodoyou have not claimed is not yours to block: report the obstacle in a comment and leave the column to a human. - Call
taskboard_release_claimwhen intentionally abandoning owned work. Explain what remains and leave useful progress in a comment first when possible. - Use
taskboard_relateonly after reading both tasks. Keep relations within one project and do not create parent cycles. - Never call or emulate acceptance. Only a human may accept
in_reviewasdone, return it for rework, approve backlog work, archive it, or permanently delete it.
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.
- 9d ago First seen · 34 lines · 88 tokens per session scan A 5bcec60fa203
manage-taskboard is a skill published in the GitHub repository shengsheng90/DSH-taskboard (328 stars, last pushed 8d ago), licensed Apache-2.0. It adds 88 tokens to every session and 736 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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