How do I install the Example Procedure skill?

Run npx skillmds@latest add timsmykov/example-procedure in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Example Procedure safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Example Procedure?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Example Procedure free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Example Procedure?

timsmykov (@timsmykov) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Example Procedure by timsmykov — Agent Skill

Name: Example Procedure
Author: timsmykov

Example Procedure

Reference skill: turns a folder of source documents into a reviewed summary table with an explicit human confirmation step. Loads on requests like 'build a summary table from these documents', 'structure this collection', 'pull these files into one comparable table'. Does NOT load for finding new sources, for checking a finished artefact (use example-checklist), or for questions about the method itself. Ships as a format example — it demonstrates the split between model reasoning and deterministic scripting, not a real research methodology.

timsmykov 79548ee 5 files · 8.9 KB Updated Sep 17, 2026

File contents

example-procedure

Shows the shape of a procedure skill: where the human decides, where the model reasons, and where a script runs. There is no subject-matter methodology here — a real plugin puts a procedure derived from someone's practice in this slot.

When it applies

There is a set of documents on hand and a structured summary over them is needed.
The user is available to confirm an intermediate decision — the feature set the summary is built on.

When it does not apply

New sources have to be found rather than existing ones processed.
The artefact already exists and needs checking — that is example-checklist.
Someone is asking how the method works rather than asking for it to be run.

Who does what

Step	Who runs it	Artefact
1. Inventory the input set	model	list of documents with type and size
2. Propose features for the summary	model	draft feature set
3. Confirm the features	researcher	approved set
4. Extract values per feature	model	`extracted.json`
5. Assemble the table	`scripts/build_table.py`	`summary.md`
6. Review the result	`example-domain-critic` subagent	list of findings

Step 5 is deliberately given to a script: sorting, column alignment and template substitution have to produce the same bytes on a rerun. Leave that to the model and the summary comes out slightly different every time, which makes two runs incomparable.

Procedure

Read the input directory and inventory the documents. Files that could not be read go into a separate list — silently dropping them is not allowed.
Propose 4–7 features for the summary, with a short rationale for each.
Confirmation point. Show the feature set and wait for the researcher's decision. Do not proceed on silence: a model-chosen feature set is a hypothesis, not a result.
Extract values for the approved features into extracted.json; where a document has no value, write null, not a guess.
Run python3 scripts/build_table.py extracted.json --out summary.md.
Hand summary.md to the example-domain-critic subagent and attach its findings to the result.

Output format

summary.md per templates/report.md: the table, explicit coverage (how many documents were processed, how many failed to read), and the list of gaps.

Quality gates

Every row traces to a specific document.
Gaps are shown as gaps, not filled with plausible text.
Features are approved by a human before extraction, not fitted to the result afterwards.
Rerunning on the same extracted.json produces a byte-identical summary.md.

Boundaries

Does not assess the quality of the sources themselves.
Does not replace the researcher's judgment about what counts as material.

---name: example-proceduredescription: Reference skill: turns a folder of source documents into a reviewed summary table with an explicit human confirmation step. Loads on requests like 'build a summary table from these documents', 'structure this collection', 'pull these files into one comparable table'. Does NOT load for finding new sources, for checking a finished artefact (use example-checklist), or for questions about the method itself. Ships as a format example — it demonstrates the split between model reasoning and deterministic scripting, not a real research methodology.---# example-procedureShows the shape of a procedure skill: where the human decides, where the model reasons, and where a script runs. There is no subject-matter methodology here — a real plugin puts a procedure derived from someone's practice in this slot.## When it applies- There is a set of documents on hand and a structured summary over them is needed.- The user is available to confirm an intermediate decision — the feature set the summary is built on.## When it does not apply- New sources have to be found rather than existing ones processed.- The artefact already exists and needs checking — that is `example-checklist`.- Someone is asking how the method works rather than asking for it to be run.## Who does what| Step | Who runs it | Artefact ||---|---|---|| 1. Inventory the input set | model | list of documents with type and size || 2. Propose features for the summary | model | draft feature set || 3. Confirm the features | researcher | approved set || 4. Extract values per feature | model | `extracted.json` || 5. Assemble the table | `scripts/build_table.py` | `summary.md` || 6. Review the result | `example-domain-critic` subagent | list of findings |Step 5 is deliberately given to a script: sorting, column alignment and template substitution have to produce the same bytes on a rerun. Leave that to the model and the summary comes out slightly different every time, which makes two runs incomparable.## Procedure1. Read the input directory and inventory the documents. Files that could not be read go into a separate list — silently dropping them is not allowed.2. Propose 4–7 features for the summary, with a short rationale for each.3. **Confirmation point.** Show the feature set and wait for the researcher's decision. Do not proceed on silence: a model-chosen feature set is a hypothesis, not a result.4. Extract values for the approved features into `extracted.json`; where a document has no value, write `null`, not a guess.5. Run `python3 scripts/build_table.py extracted.json --out summary.md`.6. Hand `summary.md` to the `example-domain-critic` subagent and attach its findings to the result.## Output format`summary.md` per `templates/report.md`: the table, explicit coverage (how many documents were processed, how many failed to read), and the list of gaps.## Quality gates- Every row traces to a specific document.- Gaps are shown as gaps, not filled with plausible text.- Features are approved by a human before extraction, not fitted to the result afterwards.- Rerunning on the same `extracted.json` produces a byte-identical `summary.md`.## Boundaries- Does not assess the quality of the sources themselves.- Does not replace the researcher's judgment about what counts as material.

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