plugin-bundles
Work with the TaskingAI plugin microservice bundle catalog, plugin schemas, execution, credential validation, image storage, and plugin-specific troubleshooting.
- License 许可
- Apache-2.0
- 最近更新
- 2026-09-03
- Stars
- ★ 351
- Forks
- 27
01 前言 Front Matter
- name
- plugin-bundles
- license
- Apache 2.0
- metadata
- { "disco-role": "operating" }
- description
- Work with the TaskingAI plugin microservice bundle catalog, plugin schemas, execution, credential validation, image storage, and plugin-specific troubleshooting.
- disable-model-invocation
- true
02 资源文件 5
- 参考文档references/api-reference.md9.7 KB
- 参考文档references/bundle-catalog.md10.7 KB
- 参考文档references/native-testing.md7.4 KB
- 参考文档references/troubleshooting.md11.7 KB
- 脚本scripts/inspect_plugin_bundles.py8.1 KB
03 SKILL.md 正文
TaskingAI Plugin Bundles
Use this sub-skill when the task is about TaskingAI's plugin microservice: built-in bundle/plugin discovery, plugin input/output schema selection, plugin execution payloads, credential validation, generated image storage, or plugin-specific troubleshooting.
Load this first
- For catalog selection, schema shape, and no-credential bundle choices, read references/bundle-catalog.md.
- For HTTP route contracts, request/response bodies, credential validation, and execution flow, read references/api-reference.md.
- For source-backed native-test behavior and safe skip criteria, read references/native-testing.md.
- For failure diagnosis, especially local image URL versus S3 storage problems, read references/troubleshooting.md.
- To inspect a local TaskingAI checkout without network calls or credentials, use scripts/inspect_plugin_bundles.py.
Source-backed facts to preserve
- The plugin microservice exposes 47 built-in bundles and 86 plugins in the verified catalog snapshot.
- Static import checks passed in Python 3.10 for
APIRouter,Bundle,Plugin,Arithmetic, andChartMaker. - Catalog metadata is defined by bundle schemas and plugin schemas, then loaded into in-memory caches at service startup.
- Plugin execution validates bundle credentials before plugin lookup/input validation, then calls the dynamically loaded bundle/plugin handler class.
- Generated-image plugins use either local storage or S3-compatible storage based on object-storage configuration.
Quick routing
- Need a safe synthetic no-credential tool? Prefer
arithmetic/addorarithmetic/dividefor precise numeric behavior. Usechart_maker/*orqr_code_generator/generate_qr_codeonly when image storage andproject_idare intentionally in scope. - Need to send a backend-created tool instance/action through TaskingAI's backend object lifecycle? Route that task to
../backend-api/and return here only for plugin bundle/schema payload details. - Need inference-provider model invocation or provider credentials unrelated to plugin bundles? Route to
../inference-providers/. - Need deployment environment basics, container startup, or cross-service env wiring? Route to
../deployment-configuration/; return here for plugin-specific storage and credential variables.
Operating workflow
- Identify the bundle and plugin. Use the no-credential and credentialed catalogs in references/bundle-catalog.md. Confirm whether provider credentials, network access,
project_id, or image storage are required. - Validate schema before execution. Compare the task payload to the plugin input schema types:
string,integer,number,boolean,string_array,integer_array,number_array,boolean_array,image_url, orfile_url. Required inputs must be present; URL types must start withhttp. - Choose the credential path. For no-credential bundles, pass
{}as credentials. For credentialed bundles, pass either plaintextcredentialsor encrypted credentials, never both. Use the verification endpoint if credentials must be encrypted for later reuse. - Execute through the plugin service contract. POST to
/v1/executewithbundle_id,plugin_id,input_params,credentials, and optionalproject_id. Expectstatus: successplusdata.statusanddata.dataon normal execution. - Handle storage outputs. If the plugin returns
urlorimage_url, check whether local or S3 storage is configured and diagnose with references/troubleshooting.md before blaming the provider.
Hard usability cases covered here
- Select a no-credential built-in bundle for a synthetic tool workflow and explain exact expected input/output schema: use
arithmetic/addwith numericnumber_1,number_2and numericresult, or usearithmetic/divideand explicitly rejectnumber_2 = 0. - Diagnose a generated chart or QR-code URL that works under local storage but fails under S3, or vice versa, by checking
OBJECT_STORAGE_TYPE, localHOST_URL/PATH_TO_VOLUME, S3 endpoint/bucket/public-domain variables, and whether the returned URL path includes the generated image category prefix.
04 同仓库更多 Skills
passnet-triton-opt
Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block/grid/warp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.
passnet-solve
END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.
passnet-skill
PassNet GPU kernel optimization via compiler passes. Design and implement Triton-based optimization passes, create pass files under ./pass_dir/, self-evaluate with pass_evaluator, and iterate to maximize GPU speedup.
passnet-pattern-fusion
Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()/replacement_args()/replacement_func() files, when a pass "failed to match", or when deciding how to fuse multiple kernels/ops.
passnet-orchestrate
Round/batch planning and decision support for PassNet graph optimization. Use for multi-sample triage, worker allocation, eval-budget policy, and post-eval keep/revert decisions. For solving one concrete sample end to end, use passnet-solve as the entry skill; consult this skill only when you need broader planning or a specific decision gate.
passnet-feedback
Fast, deterministic feedback for PassNet work: pre-flight pattern/match verification WITHOUT burning a GPU evaluation, per-node bottleneck analysis of a sample's graphs, and eval-log parsing into per-variant status + estimated score + failure classification. Use BEFORE every GPU evaluation (check_pattern), at the START of a sample (analyze_graph), and AFTER every evaluation (parse_eval_log).