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dynos-fitdynos-fit/dynos-work/skills/dry-run/SKILL.md

dry-run

Skill2 starsChanged 8 months agoMITView on GitHub

Internal dynos-work skill. Validate contract.json chaining across the skill pipeline. Checks that output_schema of each pipeline stage covers the required input_schema fields of the next stage. Runs only when the user explicitly types /dynos-work:dry-run; never auto-triggered from conversation.

3 other projects use a version of this.

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The file

Show its rules and text

We split this skill into the rules it gives an agent

16 rules. 0 are common, 6 are distinctive, and 10 are very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 6

    Distinctive

    This author's own choices. Worth a look.

  • 10

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Treat missing or incompatible contract fields as real pipeline breakage, not paperwork.

    line 12
  2. Distinctive:

    Do not infer compatibility from naming similarity alone.

    line 13
  3. Distinctive:

    If a downstream stage depends on behavior the upstream contract does not guarantee, call it broken.

    line 14
  4. Very specific:

    If any skill directory lacks a contract.json, emit a warning that names every specific directory:

    line 22
  5. Very specific:

    Read the output_schema of stage N.

    line 51
  6. Very specific:

    Read the input_schema of stage N+1.

    line 52
  7. Very specific:

    Check if a field with a matching key exists in stage N's output_schema.

    line 54
  8. Very specific:

    If the field exists, check that the type values are compatible (exact match or both resolve to the same base type).

    line 55
  9. Very specific:

    If the field is missing from stage N's output_schema, record a FAIL with details.

    line 56
  10. Distinctive:

    If the field exists but the type does not match, record a FAIL with details.

    line 57
  11. Very specific:

    If an active or completed task exists in .dynos/, run the runtime contract validator against it to verify real artifacts match the declared …

    line 107
  12. Distinctive:

    If all required chain links pass, print:

    line 119
  13. Distinctive:

    If any required chain link fails, print:

    line 124
  14. Very specific:

    Every skill should have a contract.json file in its skill directory (skills/<name>/contract.json).

    line 131
  15. Very specific:

    Keep field names consistent across stages.

    line 174
  16. Very specific:

    Use the source field to document where the data actually lives on disk or comes from at runtime.

    line 175

“Common” is measured: kept by at least 6 in 10 of the 3 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

dynos-work: Dry Run

Validate that the contract.json files across all skills form a valid pipeline chain. This skill reads every skills/*/contract.json, determines the pipeline order, and verifies that each stage's outputs satisfy the next stage's required inputs.

Ruthlessness Standard

  • Treat missing or incompatible contract fields as real pipeline breakage, not paperwork.
  • Do not infer compatibility from naming similarity alone.
  • If a downstream stage depends on behavior the upstream contract does not guarantee, call it broken.

What you do

Step 1 -- Discover contracts

  1. Scan all directories under skills/ in the project root.
  2. For each directory, check if a contract.json file exists.
  3. If any skill directory lacks a contract.json, emit a warning that names every specific directory:
    WARNING: Missing contract.json in: skills/foo/, skills/bar/
  4. Parse each discovered contract.json file. If any file contains invalid JSON, report it as an error and skip that contract.

Step 2 -- Determine pipeline order

The linear pipeline chain is fixed:

start -> plan -> execute -> audit -> learn

Skills that are NOT part of the linear pipeline (utility skills) are:

  • status
  • investigate
  • resume
  • repair
  • execution
  • dashboard
  • dry-run

Utility skills have contracts but do not participate in linear chain validation. They should still be loaded and listed in the report.

Step 3 -- Validate chain links

For each consecutive pair in the pipeline (stage N -> stage N+1):

  1. Read the output_schema of stage N.
  2. Read the input_schema of stage N+1.
  3. For every field in stage N+1's input_schema where required is true:
    • Check if a field with a matching key exists in stage N's output_schema.
    • If the field exists, check that the type values are compatible (exact match or both resolve to the same base type).
    • If the field is missing from stage N's output_schema, record a FAIL with details.
    • If the field exists but the type does not match, record a FAIL with details.
  4. For optional fields (where required is false), check availability but only emit an INFO-level note if missing, not a FAIL.

Important: Some inputs have source values that reference things outside the immediate predecessor (e.g., user prompt, filesystem paths, or outputs from earlier stages). When an input field's source explicitly references a specific origin other than the previous pipeline stage, note it but do not count it as a chain break. The chain validation focuses on whether the pipeline as a whole produces what downstream stages need.

Step 4 -- Generate report

Print a structured report with the following sections:

=== dynos-work Dry Run: Contract Chain Validation ===

Pipeline: start -> plan -> execute -> audit -> learn

--- Contracts Loaded ---
  [OK]   skills/start/contract.json
  [OK]   skills/plan/contract.json
  ...
  [WARN] skills/dashboard/ -- missing contract.json

--- Chain Validation ---

start -> plan:
  [PASS] manifest.json (object -> object)
  [PASS] spec.md (string -> string)
  [FAIL] design-decisions.md: present in plan input_schema (required: false) but missing from start output_schema [INFO]
  ...

plan -> execute:
  [PASS] manifest.json (object -> object)
  ...

execute -> audit:
  ...

audit -> learn:
  ...

--- Summary ---
Pipeline stages validated: 4
Total required fields checked: NN
Passed: NN
Failed: NN
Warnings: NN

Result: PASS (or FAIL if any required field is missing/mismatched)

Step 5 -- Runtime validation (optional)

If an active or completed task exists in .dynos/, run the runtime contract validator against it to verify real artifacts match the declared contracts:

"${CODEX_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-}}/bin/dynos" ctl validate-contract --skill start --task-dir .dynos/task-{id} --direction both --strict
"${CODEX_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-}}/bin/dynos" ctl validate-contract --skill execute --task-dir .dynos/task-{id} --direction both --strict
"${CODEX_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-}}/bin/dynos" ctl validate-contract --skill audit --task-dir .dynos/task-{id} --direction both --strict

Report any mismatches between declared contracts and actual artifacts. This catches drift between what contracts promise and what skills actually produce.

Step 6 -- Exit

If all required chain links pass, print:

Dry run PASSED. All pipeline contracts are compatible.

If any required chain link fails, print:

Dry run FAILED. See mismatches above.

Contract Schema Reference

Every skill should have a contract.json file in its skill directory (skills/<name>/contract.json). The schema is:

{
  "skill": "<skill-name>",
  "description": "<what this skill does>",
  "input_schema": {
    "<field-name>": {
      "type": "<string | object | array | boolean>",
      "required": true | false,
      "source": "<where this input comes from, e.g. '.dynos/task-{id}/manifest.json'>"
    }
  },
  "output_schema": {
    "<field-name>": {
      "type": "<string | object | array | boolean>",
      "description": "<what this output contains>"
    }
  }
}

Field definitions

FieldRequiredDescription
skillYesThe skill name, must match the directory name
descriptionYesHuman-readable description of what the skill does
input_schemaYesMap of field names to input descriptors
input_schema.<field>.typeYesData type: string, object, array, or boolean
input_schema.<field>.requiredYesWhether this input is mandatory for the skill to run
input_schema.<field>.sourceYesWhere the input originates (file path pattern, user prompt, etc.)
output_schemaYesMap of field names to output descriptors
output_schema.<field>.typeYesData type of the output
output_schema.<field>.descriptionYesHuman-readable description of the output content

Pipeline vs. utility skills

  • Pipeline skills (start, plan, execute, audit, learn) form a linear chain. The output_schema of each stage must cover the required input_schema fields of the next stage.
  • Utility skills (status, investigate, resume, repair, execution, dashboard, dry-run) operate independently. They still need contracts for documentation and tooling, but they are not validated as part of the linear chain.

Tips for skill authors

  1. Keep field names consistent across stages. If start outputs manifest.json, then plan should reference manifest.json as an input, not task_manifest.
  2. Use the source field to document where the data actually lives on disk or comes from at runtime.
  3. Mark fields as required: false if the skill can operate without them (graceful degradation).
  4. The type field should match across producer and consumer. If start outputs manifest.json as type object, then plan should expect type object for manifest.json.

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