Install
$ agentstack add skill-gnipbao-content-to-skill-content-to-skill ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Content To Skill
Content To Skill turns messy source material into a runnable skill package. It extracts repeatable mechanisms from content, rejects unsupported claims, and produces a SKILL.md plus any references, examples, scripts, and validation prompts needed for another agent to execute the method later.
Default to Chinese unless the user asks for another language. Prefer creating or patching files when a writable target path is available.
Resource Guide
- Read
references/source-intake.mdbefore processing documents, transcripts, videos, multiple files, or unclear source boundaries. - Read
references/skill-construction-protocol.mdbefore writing aSKILL.md, scaffold, references, examples, or validation plan. - Read
references/evaluation-rubric.mdwhen reviewing a generated skill, deciding whether it is publishable, or repairing a weak conversion. - Run
scripts/check_skill_package.py /path/to/generated-skillafter creating or materially editing a local skill package. - Read
examples/retest-prompts.mdwhen you need realistic forward tests for a newly generated skill.
First Principle
A source is not the skill. A skill is the compressed control system that lets a future agent repeat the useful behavior without rereading the whole source.
Every conversion must preserve this chain:
source evidence -> reusable mechanism -> executable workflow -> output contract -> validation signal
Core Workflow
1. Route The Request
模式选择: choose the smallest mode that preserves the source evidence and produces an executable artifact.
Choose the smallest mode that fits:
| Trigger | Mode | Required action | |---|---|---| | User pastes short text or a clear method | QUICKCONVERT | Extract mechanisms and produce a first-pass SKILL.md or patch. | | User provides documents, folders, transcripts, video, or many sources | SOURCEAUDIT | Build a source inventory, evidence boundary, mechanism cards, then generate. | | User asks for files, scaffold, installable skill, or gives a path | PACKAGEBUILD | Create files first, then validate. | | User provides an existing generated skill and says it failed | REPAIRFROMFEEDBACK | Compare source intent, generated behavior, and failure signal before patching. | | Source cannot be accessed | BLOCKEDSOURCE | Ask for the missing file, transcript, text, or accessible path; do not invent source content. |
If multiple modes match, prefer the one with stronger evidence requirements: REPAIRFROMFEEDBACK, PACKAGEBUILD, SOURCEAUDIT, QUICK_CONVERT.
1.5. CHECKPOINT / STOP Gates
Emit CHECKPOINT / STOP and wait before:
- overwriting an existing skill folder with unrelated local edits
- publishing or pushing a generated skill to a public repository
- processing private, paid, sensitive, or inaccessible source material
- expanding one source into a router, skill family, or SkillBank
- claiming
full_testquality without actually running an independent or fresh-context test
The checkpoint must name the target path or repository, files affected, validation command, rollback point, and exact approval needed.
2. Establish Evidence Boundary
Identify what was actually read and what remains unavailable.
Capture:
- source type: text, document, PDF, spreadsheet, slide deck, audio, video, repository, web page, notes, or mixed
- accessible artifacts: pasted text, local paths, transcripts, extracted text, screenshots, metadata, or summaries
- unavailable artifacts: video without transcript, unreadable binary files, private links, missing attachments, or unsupported formats
- user goal: new skill, skill family, prompt workflow, repair, or evaluation
- target runtime: Codex skill folder, single
SKILL.md, repository scaffold, or patch plan
For video or audio, first obtain a transcript, chapter notes, subtitles, or user-provided summary using available tools. If none is available, ask for the transcript or permission to extract it. Never infer detailed methods from a title alone.
3. Extract Mechanism Cards
Do not summarize the source paragraph by paragraph. Extract portable controls:
## Mechanism Card
Name:
Source evidence:
Trigger:
User job:
Decision rule:
Procedure:
Output:
Quality signal:
Failure mode:
Skill location:
Keep / merge / discard:
Keep only mechanisms that change future agent behavior. Discard anecdotes, slogans, duplicate examples, and unsupported claims unless they become examples, boundaries, or tests.
4. Choose Skill Shape
Select one production shape before writing files:
- Single procedural skill: one bounded repeated task.
- Engineering workflow pack: coding, debugging, architecture, tests, issues, or PR workflows.
- Methodology toolbox: router plus focused diagnostic skills.
- Cognitive distillation skill: a person, worldview, creator, company, or field turned into a perspective lens.
- Skill family or SkillBank: many independent skills with routing, acquisition, versioning, or state.
Default to a single procedural skill unless the source clearly requires routing, shared state, multiple domains, or independent subskills.
5. Generate The Skill Package
When a writable path is available and the user asks to create or convert, create files before the final answer.
Minimum package:
skill-name/
├── SKILL.md
├── agents/
│ └── openai.yaml
└── examples/
└── retest-prompts.md
Add references/ when the source has detailed protocols, rubrics, evidence notes, examples, or domain facts that should not bloat SKILL.md.
Add scripts/ only when repeated mechanical checks, extraction, conversion, or validation should be deterministic.
For public GitHub repositories, add only the human-facing files needed for reuse:
README.md
LICENSE
.gitignore
test-prompts.json
Keep the skill package itself portable: SKILL.md, agents/, references/, examples/, and scripts/ must still work if copied into any skills-compatible runtime.
Generated SKILL.md must include:
- frontmatter with only
nameanddescription - overview and first principle
- resource guide with direct links to references and scripts
- routing or operating modes
- stable workflow that produces decisions or artifacts
- output protocols
- boundaries and anti-patterns
- quality standard and validation loop
6. Validate
For local packages:
- Run the repository or generated package validator when available.
- Run
scripts/check_skill_package.py /path/to/generated-skill. - Read validator failures, patch once, and rerun.
- If validation still fails, report the failing command, likely cause, and rollback point.
For non-local outputs, use the checklist in references/evaluation-rubric.md and label the result as dry-run.
6.5. Failure Branches
| Trigger | Required action | Do not do | |---|---|---| | Source cannot be read | Ask for accessible source, transcript, or a local path | Do not invent source mechanisms | | Target folder already exists | Inspect files and patch only intended paths | Do not overwrite unrelated files | | Existing skill fails validation | Patch one minimal structural issue and rerun | Do not add broad new sections to chase score | | Public repo requested | Validate locally, create README/LICENSE, then ask or use explicit approval before publishing | Do not push unvalidated files | | User asks for a skill family | Create a mechanism map and stop at CHECKPOINT / STOP | Do not explode one source into many skills by default | | Test tooling is unavailable | Mark result dry-run and provide retest prompts | Do not call it full_test |
7. Report
Final reports should include:
## Skill Created
Source boundary:
Generated path:
Skill shape:
Files changed:
Validation:
Rollback point:
Next retest:
Keep design explanation short unless the user explicitly asks for the full extraction notes.
Output Protocols
Source-To-Skill Brief
## Source-To-Skill Brief
Source boundary:
Root job:
Target user:
Mechanisms kept:
Mechanisms discarded:
Skill shape:
Package plan:
Validation plan:
Open uncertainty:
Mechanism Extraction Report
## Mechanism Extraction
Evidence read:
High-signal mechanisms:
Conflicts or weak evidence:
Candidate skill modules:
Discarded material:
Recommended package:
Generated Package Report
## Skill Created
Path:
Name:
Description trigger:
Source boundary:
Files created or changed:
Validation commands:
Validation result:
Retest prompt:
Rollback point:
Boundaries
- Do not claim to have read inaccessible documents, videos, private links, or repositories.
- Do not turn every idea in the source into a rule. Prefer fewer mechanisms that can be executed.
- Do not copy large passages from source material into the skill. Extract controls, examples, and evidence notes.
- Do not create a skill family when one procedural skill will work.
- Do not create scripts for tasks that are better handled by clear instructions.
- Do not add README, install guides, or public docs unless the user asks for a public repository.
- Do not overwrite user files outside the target skill folder without explicit approval.
- Do not claim benchmark quality without validation or forward tests.
Anti-Patterns
- Summary-as-skill: the output explains the source but cannot guide future behavior.
- Template flood: the skill has many sections but no decision rules.
- Evidence laundering: weak or unavailable source material becomes confident instruction.
- Skill bloat: every chapter becomes a subskill.
- Tool fantasy: the skill assumes unavailable transcription, parsing, browser, or API tools.
- No retest: the conversion is never checked against a realistic future request.
Quality Standard
A successful conversion:
- names the source boundary honestly
- extracts mechanisms, not just themes
- has a clear trigger description
- can run without the original source in context
- has stable workflow steps and output contracts
- stores detailed evidence in references, not the main prompt
- names failure branches and stop conditions
- includes retest prompts or fixtures
- passes structural validation when files exist locally
- stops at CHECKPOINT / STOP before public publishing, broad generation, or sensitive source processing
Default score:
Root job clarity: 0-20
Mechanism extraction: 0-20
Executable workflow: 0-20
Output usability: 0-15
Evidence honesty: 0-10
Validation loop: 0-10
Concise expression: 0-5
Total: 100
Below 70: do not publish. 70-84: usable MVP. 85-94: publishable. 95+: benchmark-quality after independent or forward-test evidence.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: gnipbao
- Source: gnipbao/content-to-skill
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.