agentleFS
open-agent-poweropen-agent-power/open-knowledge-studio/.claude/skills/accept/SKILL.md

accept

Skill9 starsChanged 3 months agoMITView on GitHub

Run evidence-first, isolated acceptance of Open Knowledge Studio capabilities. Use for clean-install, document, PDF, formula, video watch, or Feishu end-to-end validation; preserve honest reports and remove only per-run environments.

8 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use implement, status and 1 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Hugging FaceSkill

    implement

    Make tests pass. Invoke after /write-tests produces failing tests.

    Also in 5 projects that use this

  • AnthropicSkill

    status

    Case status summary by audience — client-facing (plain language), internal (for the professor), or court-ready (formal caption format per local rules). Same facts, different framing and depth. Use when a student needs to update the client, brief the professor, or prepare a court status report.

    Also in 3 projects that use this

  • MicrosoftSkill

    handoff

    Compact the current conversation into a handoff document for another agent to pick up.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Never modify an existing knowledge base, its scheduler, or OpenClaw.

    line 9
  2. Distinctive:

    Run the helper on Linux/macOS from a controlled acceptance root.

    line 13
  3. Very specific:

    When a server cannot fetch the fixed public fixture, copy an approved local sample into the isolated server area and provide it explicitly.

    line 23
  4. Very specific:

    Use a new Base created by python examples/feishu-loop/code/feishu_setup.py, submit the public text fixture, run one bounded worker pass, …

    line 76
  5. Distinctive:

    Verify promotion and recall.

    line 79
  6. Very specific:

    Read the report before changing product code.

    line 84

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/accept — OKS Modular Acceptance

Validate feasibility and component boundaries before proposing product changes. Never modify an existing knowledge base, its scheduler, or OpenClaw.

Run a non-Feishu capability

Run the helper on Linux/macOS from a controlled acceptance root. Supply the exact wheel or Git package reference being tested; do not silently test a different installed version.

python .claude/skills/accept/scripts/accept.py document \
  --root /opt/oks-acceptance \
  --package-spec /path/to/open_knowledge_studio-*.whl

When a server cannot fetch the fixed public fixture, copy an approved local sample into the isolated server area and provide it explicitly. This records a local_fixture source kind and SHA-256; it does not turn a network failure into a product pass.

python .claude/skills/accept/scripts/accept.py all ... \
  --fixture document=/opt/oks-acceptance/fixtures/sample.pptx \
  --fixture pdf=/opt/oks-acceptance/fixtures/digital.pdf \
  --fixture formula=/opt/oks-acceptance/fixtures/scanned-formula.pdf \
  --fixture watch=/opt/oks-acceptance/fixtures/short-video.mp4

Supported capabilities are document, pdf, formula, watch, feishu, and all. all is strictly serial: document → pdf → formula → watch → Feishu preflight. The final Feishu E2E remains awaiting_human until its dedicated Base and reviewer approval are supplied.

The helper creates one isolated pipx environment and one empty KB per ingest capability. Formula acceptance verifies capability installation and paddleocr import only — it is a secondary PDF sub-capability with no standalone ingest route. Feishu preflight creates no cloud data. It verifies install, oks, capability installation, Raw Bundle artifacts, Candidate promotion, search, recall, and lint. It writes report.json and report.md, then removes only that run's pipx and KB unless --keep-environment is supplied. all additionally writes matrix.json and matrix.md with install isolation, execution, resource cost, cleanup, and observed blockers for every component.

Interpret results honestly

  • passed: every asserted local contract completed.
  • product_failure: a local command, artifact, or lifecycle assertion failed.
  • environment_limited: DNS, download, rate-limit, platform anti-bot, timeout, or unavailable external model prevented completion.
  • awaiting_human: a required review or credential-gated step remains.

The public fixture URLs live in the helper. Reports record final source URLs, SHA-256 values, command exit codes, elapsed time, and cleanup outcome. They must never include environment-variable values, tokens, passwords, or private keys.

Run Feishu E2E

Feishu E2E creates a new dedicated Base and writes real test records. Before running it, obtain explicit authorization for that creation and export only runtime credentials:

export OKS_FEISHU_BASE_TOKEN='...'
export OKS_FEISHU_TABLE_ID='...'

Use a new Base created by python examples/feishu-loop/code/feishu_setup.py, submit the public text fixture, run one bounded worker pass, generate a Candidate from the Raw Bundle, and use bounded python examples/feishu-loop/code/feishu_base_worker.py listen-reviews while the designated reviewer sends an approval. Verify promotion and recall. Record only redacted identifiers. Do not delete the new Base automatically: report its identifier and wait for human confirmation.

After every run

Read the report before changing product code. Convert only observed product_failure entries into a separate proposal containing the command, artifact path, root cause, smallest fix, and regression test.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.