agentleFS
imMamdouhaboammarimMamdouhaboammar/matts-skills-into-chatgpt/skills/skill-conductor/SKILL.md

skill-conductor

Skill8 starsChanged 2 months agoMITView on GitHub

Author, refine, evaluate, and package agent skills across their full lifecycle. Use when building a new skill from scratch, improving an existing skill, fixing a skill that triggers unreliably, running skill evals, benchmarking skill performance, or packaging skills for distribution — even if they don't explicitly say \"skill conductor\". Do NOT use for general software coding tasks or using pre-existing skills.

We split this skill into the rules it gives an agent

6 rules. 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Pre-flight verification: Check dependencies and environment before executing workflows.

    line 16
  2. Distinctive:

    Blind agent testability: Ensure instructions are unambiguous when executed without prior conversation history.

    line 21
  3. Distinctive:

    Inline risk checklists: Place verification checklists directly at high-risk seams.

    line 22
  4. Distinctive:

    One term per concept: Use consistent domain terminology throughout.

    line 23
  5. Distinctive:

    Zero secrets / environment cleanliness: Never hardcode credentials, tokens, or absolute user home paths.

    line 24
  6. Very specific:

    Key Point: Keep SKILL.md concise (< 500 lines) and push heavy schemas or tables into references.

    line 51

“Common” is measured: kept by at least 6 in 10 of the 4 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

Skill Conductor

Full lifecycle management for agent skills: draft → test → review → improve → package.

One master discipline to govern skill creation and optimization, rooted in the 10 canonical authoring principles.


Core Invariants & The 10 Canonical Authoring Principles

  1. Pre-flight verification: Check dependencies and environment before executing workflows.
  2. No process in descriptions: Descriptions define triggering boundaries (Use when..., Do NOT use for...), never workflow recipes.
  3. Map of Content (MOC): SKILL.md is a clear architectural map (< 500 lines) pointing to modular references.
  4. Fresh practitioner empathy: Explain the rationale behind steps so agents understand context.
  5. Training Within Industry (TWI): Structure critical instructions as Action, Key Point, and Why.
  6. Blind agent testability: Ensure instructions are unambiguous when executed without prior conversation history.
  7. Inline risk checklists: Place verification checklists directly at high-risk seams.
  8. One term per concept: Use consistent domain terminology throughout.
  9. Zero secrets / environment cleanliness: Never hardcode credentials, tokens, or absolute user home paths.
  10. Match form to failure: Counter specific failure modes with targeted structural constraints and anti-rationalization tables.

Core Lifecycle Modes

ModeTrigger / ContextKey Output
1. CREATE"build a skill", "new skill for..."Full lifecycle: intent → architecture → scaffold → write → eval
2. IMPROVE"fix this skill", "it doesn't trigger"Diagnose → eval loop → gated self-update → iterate
3. VALIDATE"test this skill", "run evals"Structural checks + trigger testing + BinEval scoring
4. REVIEW"review this skill", quality audit11-point quality gate assessment
5. OPTIMIZE"improve triggering", "optimize description"Automated description optimization with train/test splits
6. PACKAGE"package for distribution"Validation + bundle into release artifact

Step-by-Step Procedure (TWI)

Step 1: Capture Intent & Define Triggers

  • Action: Extract 2–3 concrete user scenarios and establish positive and negative trigger boundaries.
  • Key Point: Specify exact phrases users say and adjacent domains the skill must reject.
  • Why: Clear boundaries prevent undertriggering and false-positive overtriggering.

Step 2: Architecture & Progressive Disclosure

  • Action: Select the architectural pattern (sequential, iterative, context-aware) and structure files.
  • Key Point: Keep SKILL.md concise (< 500 lines) and push heavy schemas or tables into references.
  • Why: Bloated instruction files exhaust attention budgets and degrade execution quality.

Step 3: Write Frontmatter & Body

  • Action: Draft YAML frontmatter with kebab-case name and trigger-rich description, followed by MOC, TWI steps, and guardrails.
  • Inline Checklist:
    • Frontmatter name matches directory name
    • Description is $\le 1024$ characters and contains no process steps
    • Negative triggers specified (Do NOT use for...)
    • Anti-rationalization table included
    • No hardcoded tokens, passwords, or machine-specific paths

Step 4: Validate with BinEval 5 Dimensions

  • Action: Evaluate across Discovery, Clarity, Structure, Robustness, Completeness.
  • Key Point: Pass every critical gate check before declaring release readiness.
  • Why: Multi-dimensional evaluation catches hidden failure modes before distribution.

Anti-Rationalization Guardrails

Tempting RationalizationBinding RuleEngineering Rationale
"Putting steps in the description helps the model."Forbidden: zero process in description.Models follow description steps and skip the comprehensive body instructions.
"The skill is small, so negative triggers are unneeded."Mandatory negative triggers in description.Without negative boundaries, skills trigger falsely on loosely related queries.
"One big 1200-line markdown file is easier to manage."Progressive disclosure: SKILL.md < 500 lines.Overloaded contexts dilute attention and lead to instruction-skipping.
"We can skip testing if the markdown looks clean."Trigger evaluation on positive and negative test cases.Clean prose can still fail in actual agent routing.

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.