Agent skill

Tasks

by Prismer-AI in Prismer-AI/PrismerCloud

Manage Prismer workspace tasks across the full Kanban lifecycle — create, list, inspect, update, complete, approve, reject, cancel.

MITAuto-check passedProductivity & Automation

Install Tasks

skills CLI
$ npx skills add Prismer-AI/PrismerCloud --skill tasks -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Prismer-AI/PrismerCloud tasks --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sdk/cloud/catalog/skills/tasks .claude/skills/tasks && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tasks
GitHub stars
1.6k
Token cost
~5.8k tokens
SKILL.md length
2,547 words
Files
1
Skills in repo
88
Repo updated
First seen
Licence
MIT

At a glance

Manage Prismer workspace tasks across the full Kanban lifecycle — create, list, inspect, update, complete, approve, reject, cancel.

  • Works in 4 steps: Call cloud task create --assignee-name… → For a delegation-only request, stop… → NEVER use a generic Task / subagent /… → …
  • The user asks to add a card to the board
  • SKILL.md covers ⛔ Hard rules — delegation…, ↩️ 结果回流 — 委派后怎么拿到结果(HARD), When to use and Lifecycle, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tasks is an agent skill from Prismer-AI/PrismerCloud. Manage Prismer workspace tasks across the full Kanban lifecycle — create, list, inspect, update, complete, approve, reject, cancel. Use whenever the user asks to add a card to the board, dispatch work to another agent, track progress, or move a task between states. Executes via the cloud task CLI.

Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Task management. The licence is MIT.

When your agent uses it

  • The user asks to add a card to the board
  • Dispatch work to another agent
  • Move a task between states

Example prompts

  • “/tasks”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Call cloud task create --assignee-name (or --assignee-id). That is the ONLY legitimate delegation path. A delegation is a tracked board…
  2. For a delegation-only request, stop after reporting the returned IDs / titles. If the user also requested orchestration, follow-through…
  3. NEVER use a generic Task / subagent / fan-out / parallel-agent tool to silently do the work in-process. That bypasses the kanban board…
  4. The phrase "I've also started working on these" after a cloud task create is a red flag that you violated rule 3. The correct phrase is…

What it can do on your machine

Read from SKILL.md and the folder at commit e5d9444. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tasks loads about 5.8k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 2,547 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~5.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Prismer-AI/PrismerCloud at commit e5d9444, republished under its MIT licence (© Prismer-AI). 2,547 words, ~5,767 tokens.

Download SKILL.mdSave it as .claude/skills/tasks/SKILL.md (or your agent's skills folder).
name
tasks
description
Manage Prismer workspace tasks across the full Kanban lifecycle — create, list, inspect, update, complete, approve, reject, cancel. Use whenever the user asks to add a card to the board, dispatch work to another agent, track progress, or move a task between states. Executes via the `cloud task` CLI.
scope
common

Tasks

Use this skill to drive the workspace Kanban end-to-end. Tasks are durable board items with owner, priority, schedule, and conversation linkage. Every operation goes through the cloud task CLI — never reply that a task was created/moved/completed unless the command actually returned a task ID and status.

⛔ Hard rules — delegation discipline

When the user asks you to assign / delegate / hand off work to ANOTHER agent (e.g. "create kanban tasks and assign to @research-agent", "give this to Bob"):

  1. Call cloud task create --assignee-name <agent> (or --assignee-id). That is the ONLY legitimate delegation path. A delegation is a tracked board card that ALSO auto-dispatches: the card shows on the Kanban (留痕/可观测) and the cloud routes it to the target agent's daemon the moment the assignee is set — the target picks it up via its own dispatch loop, no manual "drag to Running" needed. (A --no-card pure run exists for internal orchestrator sub-steps that should NOT surface a card.)
  2. For a delegation-only request, stop after reporting the returned IDs / titles. If the user also requested orchestration, follow-through, or result collection, continue read-only status/result collection with bounded waits as described below. Never duplicate the assignee's work yourself.
  3. NEVER use a generic Task / subagent / fan-out / parallel-agent tool to silently do the work in-process. That bypasses the kanban board, the assignee never sees the card, the user's mental model ("an agent is working on this") is violated, and the delegation is fake. If you find yourself reaching for any tool whose name is "Task", "Subagent", "Worker", "Fanout", "ParallelAgents", or anything else that spawns an inline executor — stop and re-read this section.
  4. The phrase "I've also started working on these" after a cloud task create is a red flag that you violated rule 3. The correct phrase is "Tasks created and assigned. @research-agent will pick up the cards."

This is non-negotiable. Bypassing it makes the agent ecosystem look broken even when the cards are correct, because the user sees the answer come back too fast and notices that the supposed assignee was never @-mentioned in the conversation.

↩️ 结果回流 — 委派后怎么拿到结果(HARD)

委派出去之后,结果不会自动回流到你的对话或 run —— 没有回调、没有自动推送。被派的 agent 在它自己的独立 context 里执行(不进你当前这条会话、也不会在这里发言),产物落在 task 卡 + 看板上。所以:

  • 要看结果,你必须主动拉:cloud task get <taskId> 读 status / result / 附件。status=completed 才算交付;review 等你 approve/reject。
  • 编排节奏:cloud task create --assignee-name <peer> → 拿到 taskId → 反复 cloud task get <taskId> 直到 status 进终态 → 读产物 → 决定下一步。(有 cloud task wait <id> 时优先用它,省手搓轮询。)
  • 「看不到对方回复 ≠ 它没在干」。被派 agent 沉默是正常的——它在独立 context 里跑,结果只在卡上。绝不要因为对话里没动静就自己把活重做一遍(那就是影子执行,违反上面的 Hard rules)。
  • 只要求委派时,报 taskId 后结束本轮;明确要求编排/收集结果时,同轮可继续 cloud task get,退避轮询、有超时,不重新执行受派任务。不要自行安排未来轮询或声称已有后台监控。

When to use

  • The user asks to create, assign, schedule, or track work ("add to kanban", "give Bob this task", "remind me to ship X by Friday").
  • You need to delegate a concrete deliverable to another agent. (See ⛔ rules above.)
  • A long-running objective should persist as a workspace goal (kind=goal).
  • The user asks about board state ("what's pending", "show me Bob's tasks", "what's blocking the release").
  • A task already on the board needs to be updated (priority change, retitle, attach context), completed, approved/rejected in review, or cancelled.
  • The task output is a document / deck / spreadsheet / PDF / report. In that case use the office-artifacts skill before completion; a prose-only result is not enough for file-deliverable tasks.

Lifecycle

pending → assigned → running → review → completed | failed | cancelled
              ↑                     ↓
              └──── blocked ────────┤  (unblock returns to assigned)
                                    ↓
                             awaiting_approval
                                    ↓
                        approved / rejected (back to review)

Nine-state lifecycle: pending / assigned / running / review / blocked / awaiting_approval / completed / failed / cancelled. awaiting_approval cards stay visible on the review column — never treat them as gone. blocked has its own column.

A work_item shows on the Kanban; a goal shows on the Goals lane. Filter at list time with --kind work_item,goal if you only want board projection. A delegation (cloud task create --assignee-name …) is a work_item board card that also auto-dispatches (dispatchPolicy=on-assign) — it is BOTH visible on the board AND executing. A work_item you create for a human (or to drive yourself via the UI) stays manual: it only runs on an explicit drag-to-Running. Only globally-visible formal tasks are board cards — a chat @mention is a session-internal run, NOT a board card, and never produces one.

Permission model (v2.0 — release 200 state machine)

The platform enforces a trust-tier × transition matrix (see docs/release200/15-task-state-machine-and-kanban.md §5.2). Each transition's allowed actors are part of the contract; calling outside the matrix returns 403 forbidden with { actorTier, requiredTiers }, and impossible transitions return 409 invalid-transition with { allowedFromHere }.

ActionPermitted by
create, retitle, change priority, reassign, cancel, approve, rejectcreator / orchestrator / admin
claim, update --progress / --status-message, complete (assignee submits to review), failassignee
review → completed (approve)creator / orchestrator / admin — never the assignee (no self-approval)

If you create a task and intend to drive it yourself (no other agent involved), self-assign first: cloud task update <taskId> --assignee-id <your-imUserId>. Otherwise complete and update --progress will both 403.

v2.0 endpoint canonicalisation: every kanban operation (/complete, /approve, /reject, /cancel, drag-to-column, restore-from-cancelled, blocked self-report) is forwarded internally to POST /api/im/tasks/:id/transition. The CLI verbs below are stable; the legacy POST /tasks/:id/{complete,approve,reject,cancel,fail} endpoints carry a Deprecation header but stay functional until 2026-09-01. After that date, scripts hitting the API directly must migrate to /transition; CLI users are insulated.

CLI Reference

Create
bash
# Standard board card
cloud task create \
  --title "Review PR #42" \
  --description "Security check for the auth refactor; ship blocker." \
  --capability code-review \
  --priority high \
  --assignee-id <imUserId>           # or --assignee-name "@alice"
  --conversation-id <convId>          # pin to the session you're in

# Long-running objective (goal lane, not Kanban)
cloud task create --kind goal --title "Reduce p95 latency to <300ms" --priority high

# Scheduled / recurring
cloud task create --title "Daily 9am report" --schedule-cron "0 9 * * *"
cloud task create --title "Run in 2h" --schedule-at "2026-05-19T16:00:00Z"

# Marketplace task with credit escrow
cloud task create --title "Scan deps" --reward 10
Read
bash
cloud task list                                 # YOUR cards (created by or assigned to you)
cloud task list --mine                          # only tasks assigned to YOU (excludes ones you created)
cloud task list --status pending                # your cards, filtered by status
cloud task list --kind work_item,goal           # your cards, board projection only
cloud task list --conversation-id <convId>      # your cards pinned to a conversation
cloud task get <taskId>                          # full detail + logs + runs (a card you have access to)

⚠️ cloud task list is scoped to YOU. As a regular (executor) agent you only ever see cards you created or that are assigned to you — the server enforces this on EVERY filter (you cannot widen it with --kind / --status / --view). You cannot enumerate other roles' cards. This is deliberate: if you can't see someone else's task, you can't accidentally poach it.

  • --assignee-id <other> / --creator-id <other> for someone who isn't you → 403.
  • The workspace orchestrator and humans see the whole board (they coordinate / drive the UI). If you are NOT the orchestrator and need to know what another role is doing, @ them or @ the orchestrator — don't try to list their cards.
🚦 Act only on YOUR cards (HARD)

You only see your own cards, and even within them, seeing a card does NOT always mean it's yours to transition.

  • Only claim / start / update --progress / complete a task whose assignee is YOU. Check the card's assignee against your own identity (agent_im_user_id / agent_username in <execution_context>) before acting.
  • A card you can see but whose assignee is another role / agent (e.g. it appears because you created it for someone else) is read-only for you — do NOT claim it, do NOT start working it. The permission layer 403s the transition, and the real failure is social: you've told the room you're doing someone else's work.
  • If you think a card should move (wrong assignee, stalled, mis-prioritised), @ the orchestrator / task owner and say why — don't poach it. Reassignment is the creator/orchestrator's call.
  • The orchestrator role legitimately reads the full board to coordinate. Reading ≠ owning.
Update + transition
bash
cloud task update <taskId> --title "Updated title" --priority urgent
cloud task update <taskId> --progress 0.5 --status-message "halfway done"
cloud task claim <taskId>                       # take an unassigned task
cloud task complete <taskId> --result "LGTM, merged into main"
cloud task fail <taskId> --error "Build broke on CI"
cloud task approve <taskId>                     # accept a review-state result
cloud task reject <taskId> --reason "missing test coverage on edge case"
cloud task cancel <taskId>                      # withdraw before terminal state

Operating Rules

Create
  • Never create an unscoped task. A workspace is required — use PRISMER_WORKSPACE_ID from the runtime when present, else fail loudly and ask.
  • Never create a work_item without a concrete assignee. Pass --assignee-id or --assignee-name that resolves to a single visible agent. Vague routing (no assignee → "someone will pick it up") leaks tasks into limbo.
  • Avoid vague titles like "help with this". The title must name an observable deliverable.
  • Put acceptance criteria + constraints + handoff context in --description, not in chat. The description is the source of truth the assignee reads.
  • Don't use task creation to hide uncertainty. If ownership or scope is unclear, ask the user first.
Approve / Reject / Complete
  • Read the task and its result before approving (cloud task get). The only exception is when the user just supplied explicit approval in this turn.
  • Don't approve your own incomplete work to bypass review.
  • Don't approve when the user asked for changes or raised unresolved concerns — use task reject --reason with a concrete actionable reason.
  • Don't task complete if acceptance criteria are unmet. If human review is the gate, leave it in review and request approval instead.
  • Never reject without a reason. Keep reasons factual and actionable (point to the missing artifact or failing criterion).
  • task cancel is for withdrawal of intent (the work is no longer wanted). task reject is for review failure (the result is wrong). task fail is for execution failure (it tried and broke). Don't mix them.
  • For file deliverables, do not complete until the generated file has been placed in $PRISMER_ARTIFACTS_DIR or uploaded via cloud asset upload --task-id. The Kanban card's artifact chip depends on the task-linked asset upload.
Update
  • Don't send an empty update.
  • Don't force --status completed here — use task complete so the lifecycle hook fires (result summary, escrow release, board move).
  • --progress is a float 0.0–1.0 with real meaning. Don't fake precision; agents reading the board treat 0.73 as a measured ratio.
Read
  • Don't infer task completion from task list summaries alone — get the task to confirm status.
  • When no tasks match, report that directly. Never fabricate board state.
  • Respect visibility — the service already scopes by workspace + ACL; don't try to query around it.

SPEC discipline (you as task owner)

A task without a SPEC is a task that will drift. Before assigning a non-trivial task (>15 min of agent work or any cross-agent handoff), write a SPEC.md.

Show full SKILL.md (972 more words)Show less
Write SPEC.md when
  • task is delegated to another agent (cross-agent handoff)
  • task involves >15 min of work
  • task touches a deliverable the user will see / use
  • you yourself want a contract to refer back to later
SPEC.md structure (4 sections — all optional but Goal is highly recommended)
## Goal
one-sentence terminal state (what does "done" look like?)

## Background
why this task exists; constraints from prior work

## Constraints
bullet list — what must not break; what must be preserved

## Out of scope
bullet list — what this task explicitly does NOT cover
Write via
cloud task spec-set <taskId> --file SPEC.md
cloud task spec-set <taskId> --markdown "..."
cloud task spec-show <taskId>
Anti-patterns
  • Don't write SPEC after assignment — owner authoring before handoff IS the contract
  • Don't change SPEC silently after status=running — owner must surface change; assignee must re-confirm

TODO discipline (you as task assignee)

When you receive an assigned task, before executing, decompose the SPEC into a TODO list. The TODO list is your visible plan — the orchestrator (creator agent / human) watches it for drift and intervenes if your plan stops matching the spec.

Write TODO.md when
  • you are the assignee on any task with capability not in ('trivial', 'one-shot')
  • you are about to start work (TODO before code)
  • you discover new sub-steps mid-execution — append rather than silently expand
TODO.md structure
# TODO — <one-line restatement of objective>

- [ ] step 1 (small, observable)
- [ ] step 2
  - [ ] sub-step 2a (depth ≤ 3)
  - [ ] sub-step 2b
- [ ] step 3

3–15 items is a healthy range. <3 means you didn't decompose. >15 means task should split.

Tick discipline
  • Tick - [ ] → - [x] IMMEDIATELY when a step finishes (tick first, then move on)
  • Do NOT tick speculatively — only after the step is verifiably done
  • Each tick emits SSE → orchestrator sees progress; this is your observability layer
  • If you skip a step (no longer needed) — remove it with a one-line note, don't leave unticked
Write via
cloud task todo-add <taskId> "step text"
cloud task todo-done <taskId> <idx>
cloud task todo-uncheck <taskId> <idx>
cloud task todo-show <taskId>
Anti-patterns
  • Don't execute without a TODO.md (runaway risk)
  • Don't tick the whole list at once at the end (orchestrator loses observability window)
  • Don't silently expand TODO with steps that diverge from SPEC — surface to creator first

Self-check & verification

A task is NOT done until acceptance criteria pass. There are 4 verify modes; you interact with them differently.

4 verify modes (cloud does NOT pre-bake methods)
  • qualitative — verifier agent picks a method at runtime (Playwright / LLM-judge / human eye).
  • quantitative — verifier agent runs a real measurement against a threshold.
  • agent-self-check — assignee self-evaluates before status=review.
  • manual — human reviewer ticks the box in ApprovalCard.

Cloud does NOT ship a Playwright runner / endpoint-status checker / benchmark harness. You (the agent) own the implementation. Use any tool in your skills.

Before setting status=review (as assignee)

You MUST run cloud task verify <taskId>. This:

  1. Lists all criteria with verifyMode='agent-self-check'
  2. For each, you read the expectation and self-assess (be honest — if you can't verify, mark failed and turn the task to status=blocked instead of review)
  3. CLI exits 0 only if all self-check criteria pass; non-zero blocks review

You MUST also confirm:

  • TODO.md progressPct ≥ 80% (cloud enforces; <80% triggers confirm modal)
  • All evidence assets attached (cloud task attach artifacts/foo.png then add to criterion.evidenceRefs via cloud task verify-criterion ... --evidence asset:<id>)
As a verifier agent (when you receive task.verify.requested)

You will receive a task dispatch message:

type: task.verify.requested
taskId: ...
criterionId: ...
expectation: "<markdown>"
spec: "<SPEC.md content>"
evidenceRefs: [...]

Your job is to:

  1. Read SPEC + criterion.expectation + existing evidence

  2. Decide at runtime what method to use:

    • qualitative → Playwright screenshot, LLM-judge, human eyeball (escalate if you can't judge)
    • quantitative → run a benchmark, GET a metric endpoint, run pytest — pick the right tool
  3. Execute the method; collect evidence (screenshots, metric values, logs)

  4. Attach evidence as task-bound assets: cloud task attach <file>

  5. Report outcome:

     cloud task verify-criterion <tid> <cid> \
       --outcome passed|failed \
       --note "Method: Playwright spec checkout.spec.ts; ran 5 iterations; saw 28.9s p95. Steps: ..." \
       --evidence asset:<id>

The note MUST include enough detail that a reader can re-run your method:

  • what tool / command you used
  • key parameters
  • the actual measurement / observation
  • (optional) why you chose this method
Anti-patterns
  • DO NOT mark outcome=passed without actually running a verification (false positive)
  • DO NOT skip evidence attachment (note alone is not auditable)
  • DO NOT use one fixed method blindly — pick the right tool for the criterion
  • DO NOT escalate to manual without first trying — verifier-agent is the default
  • DO NOT pretend cloud has built-in playwright/benchmark runners — you run them locally
CLI quick reference
bash
# SPEC (owner)
cloud task spec-set <taskId> --file SPEC.md
cloud task spec-show <taskId>

# TODO (assignee)
cloud task todo-show <taskId>
cloud task todo-add <taskId> "step text"
cloud task todo-done <taskId> <idx>
cloud task todo-uncheck <taskId> <idx>

# Acceptance criteria
cloud task acceptance <taskId>                           # show all criteria + status
cloud task add-criterion <taskId> \
  --mode qualitative --expectation "..." \
  --verifier-agent agent_xxx                             # default = creator
cloud task verify <taskId>                                # assignee self-check
cloud task verify-criterion <taskId> <cid> \
  --outcome passed --note "..." --evidence asset:<id>   # verifier agent report
cloud task apply-template <taskId> --template <id>       # adopt a template

产物输出(user-deliverable)

文件交付是显式的(release202/09 P2 起)。把文件写进 artifacts/ 不会自动交付——artifacts-watcher auto-scan 默认 OFF(2f3e901b retire watcher auto-scan)。仅当 carrier 决策是文件时,才使用 cloud deliver / cloud task attach 显式交付该文件。

bash
# 1) 把最终产物写到 artifacts/(draft / 中间文件走 scratch,不会被交付)
echo "..." > "${PRISMER_ARTIFACTS_DIR}/my-report.md"

# 2) 显式交付(按目标二选一)——传【绝对路径】:
cloud deliver      "${PRISMER_ARTIFACTS_DIR}/my-report.md"   # 动作 A:挂到你「当前这条回复」上
cloud task attach  "${PRISMER_ARTIFACTS_DIR}/my-report.md"   # 动作 ③:挂到「看板任务卡」(mode:task-attach)

⚠️ 写进 artifacts/ ≠ 交付。没有 auto-scan。每个文件都要显式 cloud deliver(聊天回复产物)或 cloud task attach(任务卡产物),否则用户收不到。artifacts/ 放最终交付物,draft / 中间文件走 scratch/。

常态(含 hermes):直接 cloud deliver <abs-path> 即可——hermes 虽没有 per-dispatch env id(PRISMER_RUN_ID / PRISMER_CONVERSATION_ID unset),但 daemon(release203/15c)会按你的 agent 身份自动关联你当前的 dispatch,补全 run/conversation。spawn 适配器(claude-code / codex)env 已注入,同样无需 flag。 仅以下情形才手传 flag:(a) 你要对另一个(非当前)dispatch 交付;(b) daemon 返回 409 歧义(同 agent 多个并发 dispatch 无法区分)。这时才从 <execution_context> 抄 <run_id> / <conversation_id>:

bash
cloud deliver "<abs-path>" --run-id "<run_id>" --conversation-id "<conversation_id>"

详见 office-artifacts SKILL(唯一交付契约)。

Workflow:

  1. 中间脚本 / draft 写到 ${PRISMER_SCRATCH_DIR}/(scratch,不进 Asset、不交付)
  2. 最终产物写到 ${PRISMER_ARTIFACTS_DIR}/(agent process env; physically ${TASK_WORKDIR}/artifacts/)
  3. 显式交付每个产物:聊天回复用 cloud deliver <abs-path>(动作 A),任务卡用 cloud task attach <abs-path>(动作 ③)。命令成功会打印 assetId
Carrier 选择与显式交付

carrier 优先级是:用户明确要求 > Runtime carrier directive > 已选内容 skill > task 默认。tasks skill 只管任务与回执,不擅自把内容改成 Markdown 文件、Asset 或 Memory Page。

  1. inline PKF:按 Runtime carrier directive 返回单一 inline PKF ContentBlock / sentinel。不写文件,不调 cloud deliver / cloud task attach,不并行产出 .md / .html / .css / .js。
  2. 用户明确要求文件:按用户指定格式或内容 skill 决定的格式写入 ${PRISMER_ARTIFACTS_DIR}/,再显式调 cloud deliver / cloud task attach。不要另造 Markdown 投影。
  3. Asset / Memory:只有用户要求或 Runtime carrier directive 明确选中时才 materialize;必须以服务端返回的 id / path / version / hash 以及读回结果作为权威证据。
  4. 任务 evidence:截图、benchmark JSON、日志等原始证据可作为明确文件附件显式 cloud task attach;它们不会改变主交付物的 carrier。

禁止:

  • inline 选中后并行生成 Markdown/HTML/CSS/JS 或附件;
  • 把 draft / 中间产物放进 artifacts/(draft 走 scratch/);
  • 把“写进 artifacts/”当成已交付——auto-scan 是 OFF,文件 carrier 必须有显式 receipt;
  • 用 role 模板或 task 流程覆盖 Runtime carrier directive。

Self-check checkpoint:

cloud task update --status review 内嵌调用本地 daemon /v1/checkpoints/pre_status_change。该 hook 比较 artifacts/ 内文件 sha256 与已 attach Asset.contentHash,若 diff 非空:

  • exit code = 12
  • stderr 列出未 attach 的文件路径 + hash + size
  • agent context 看到 stderr 自行 decide → 补 attach 或加 --skip-checkpoint

这不是 mandate retry 机制 —— 是给 agent 自己看的 self-check,不污染 user session 也不发 IMMessage。

Output reporting

After any state-changing operation, echo back the task ID and the status the service returned (not the status you expected). Example:

Created task cm5gxyz... in assigned state, pinned to conversation cm5conv..., assigned to @alice.

If the CLI exits non-zero, surface the error code and message verbatim. Never fabricate a task ID — if you didn't run the command or it failed, say so.

Backing capabilities (D22 mapping)

Replaces these v1.x built-in skills: task-create, task-list, task-get, task-update, task-complete, task-approve, task-reject, task-cancel.

© Prismer-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in sdk/cloud/catalog/skills/tasks of Prismer-AI/PrismerCloud.

Open the folder on GitHubat commit e5d9444

Compare with similar skills

Tasks next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tasks compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tasks this skillPrismer-AI/PrismerCloud1.6k—~5.8kAutomated safety check: PassMIT
Superset Agent Standupsuperset-sh/superset15k—~712Automated safety check: PassCustom licence
AgentRQ Workspace Agentagentrq/agentrq1.1k—~1.9kAutomated safety check: PassAGPL-3.0
Markdown Task Managerioniks/MarkdownTaskManager535—~2.2kAutomated safety check: PassMPL-2.0
Pi Messenger Crewnicobailon/pi-messenger720—~3.7kAutomated safety check: PassNone
Codekanban CLIfy0/CodeKanban225—~2.7kAutomated safety check: PassApache-2.0

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  • Prismer Image Generation

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  • Manim Explainer Videos

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  • Prismer Role Builder

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    Creates or updates Prismer role templates from a persona, SOP or job description, and turns a role into a working agent that runs its first task through a bundled script.

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Questions about Tasks

What does Tasks do?

Manage Prismer workspace tasks across the full Kanban lifecycle — create, list, inspect, update, complete, approve, reject, cancel. Tasks is an agent skill from Prismer-AI/PrismerCloud. Manage Prismer workspace tasks across the full Kanban lifecycle — create, list, inspect, update, complete, approve, reject, cancel.

When should I use Tasks?

Tasks fits situations like: the user asks to add a card to the board; dispatch work to another agent; move a task between states.

How do I install Tasks in Claude Code?

Run `npx skills add Prismer-AI/PrismerCloud --skill tasks -a claude-code`. Or copy the skill folder (sdk/cloud/catalog/skills/tasks in Prismer-AI/PrismerCloud) into .claude/skills/tasks in your project. Claude Code loads it when a task matches its description.

How do I install Tasks in Codex?

Run `npx skills add Prismer-AI/PrismerCloud --skill tasks -a codex`. Or copy the skill folder (sdk/cloud/catalog/skills/tasks in Prismer-AI/PrismerCloud) into .agents/skills/tasks in your project. Codex loads it when a task matches its description.

Can I use Tasks in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Prismer-AI/PrismerCloud --skill tasks -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tasks, .gemini/skills/tasks, .github/skills/tasks and .opencode/skills/tasks in your project.

What does Tasks need to run?

SKILL.md names no scripts, command-line tools or credentials: Tasks is instructions for the agent only.

Does Tasks access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tasks safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tasks use?

Tasks is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tasks use?

About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tasks?

Skills that share tags, products or a category with Tasks: Superset Agent Standup (superset-sh/superset, 15k stars), AgentRQ Workspace Agent (agentrq/agentrq, 1.1k stars), Markdown Task Manager (ioniks/MarkdownTaskManager, 535 stars) and Pi Messenger Crew (nicobailon/pi-messenger, 720 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tasks?

Prismer-AI (a GitHub organization) maintains it in Prismer-AI/PrismerCloud, which has 1,555 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on September 30, 2026.

Source: Prismer-AI/PrismerCloud on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.