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<what this creates and why>
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Based on SOC occupation classification
| name | Author Procedures Design And Structure |
mind.author_procedures@mind:id: SKILL.META.AUTHOR_PROCEDURES.DESIGN_AND_STRUCTURE
(meta-skill; guides creation of procedures executed by membrane tool)
Procedures = YAML executables executed by membrane. They gather context, ask questions, create graph clusters.
Skill (knowledge) → Procedure (executable) → Membrane (executor) → Graph (nodes + links)
Procedures differ from skills:
Key patterns in THIS system:
Write procedures that gather context, check reality, ask rich batched questions, and create dense clusters.
domain: "<area of work>" # string
trigger: "<gap or event>" # what starts this procedure
cluster_output: "<nodes and links>" # what gets created
protocol_document:
path: "procedures/procedure_<verb>_<object>.yaml"
structure: [triggers, requires_skills, contextual_knowledge, steps, output]
Procedure isn't a form. It's a guided conversation.
Each question:
Agent doesn't just fill blanks. Agent understands the question, thinks, answers with intent.
A rich question has:
| Field | Purpose |
|---|---|
| ask | The actual question |
| why | Why this matters for the cluster |
| context | What exists, what to consider |
| options | Auto-injected choices from graph |
| guidance | How to think about answering |
| creates | What this answer produces (transparency) |
| comment | Agent's reasoning → becomes moment |
Shallow (bad):
ask: "Which validations?"
Rich (good):
ask: "Which validations does this health indicator verify?"
why: |
Health indicators exist to verify that validations hold at runtime.
Without this link, we don't know what invariant this indicator protects.
Without this link, we can't query "which validations have coverage?"
context: |
Validations in this space:
- V1: Link endpoints must exist
Currently covered by: (none)
- V6: Required fields present
Currently covered by: (none)
- V2: Physics values in range
Currently covered by: health_physics_bounds
You should verify at least the uncovered ones.
guidance: |
Pick validations that this indicator can actually verify.
Consider: what does the observation point (dock) actually check?
If it queries nodes and checks fields → V6.
If it checks link structure → V1.
Multiple selections encouraged. One indicator can verify several validations.
creates: |
For each validation you select:
health ─[verifies]→ validation
This makes the coverage queryable.
comment: (agent fills with reasoning)
Every question has a comment field. Agent fills it with reasoning.
Why:
What agent writes:
comment: |
Selected V1 and V6 because this health indicator queries nodes
and checks required fields. V2 is physics-specific and already
covered by health_physics_bounds. The input dock reads from
GraphOps._query which validates structure, not physics values.
Auto-persists as:
moment:
type: decision
text: "Selected V1 and V6 because this health indicator queries..."
about: [health_indicator, V1, V6]
expresses: AGENT_Keeper
Now the graph remembers why, not just what.
┌─────────────────────────────────────────────┐
│ QUESTION │
│ │
│ ask: "Which validations...?" │
│ │
│ why: (explains importance) │
│ │
│ context: │
│ - V1: uncovered ← you should pick this │
│ - V6: uncovered ← you should pick this │
│ - V2: covered by X │
│ │
│ guidance: (how to think about it) │
│ │
└─────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│ AGENT THINKS │
│ │
│ Reads why → understands importance │
│ Reads context → sees what exists │
│ Reads guidance → knows how to decide │
│ │
│ Decides → picks V1, V6 │
│ Explains → writes comment │
│ │
└─────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│ ANSWER │
│ │
│ selected: [V1, V6] │
│ comment: "Selected V1 and V6 because..." │
│ │
└─────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────┐
│ CREATES │
│ │
│ Links: │
│ health ─[verifies]→ V1 │
│ health ─[verifies]→ V6 │
│ │
│ Moment: │
│ "Selected V1 and V6 because..." │
│ about: [health, V1, V6] │
│ │
└─────────────────────────────────────────────┘
The context field isn't static. Membrane queries graph and injects:
Before injection:
context: |
Validations in this space:
{query: narrative.validation in {space}}
Show coverage status for each.
After injection:
context: |
Validations in this space:
- V1: Link endpoints must exist
Coverage: NONE ← needs health indicator
- V6: Required fields present
Coverage: NONE ← needs health indicator
- V2: Physics values in range
Coverage: health_physics_bounds (created 3 days ago)
Agent sees reality. Can't pick phantom nodes. Sees gaps.
| Question Type | Guidance Style |
|---|---|
| Select one | "Pick the most specific match. If multiple fit, pick primary." |
| Select many | "Pick all that apply. More connections = better coverage." |
| Create new | "Only if nothing existing fits. Check options first." |
| Name/text | "Use slug format. Should describe function, not implementation." |
| Observation point | "Pick where the actual data is read. Trace to specific function." |
Every step's comment becomes part of the cluster's history:
Procedure run: add_health_coverage
Step 1: pick_space
answer: space_MODULE_schema
comment: "Schema module because this verifies graph structure"
→ moment_DECISION_pick-space-001
Step 2: pick_validations
answer: [V1, V6]
comment: "V1 and V6 because we check structure and fields, not physics"
→ moment_DECISION_pick-validations-002
Step 3: pick_algorithm
answer: narrative_ALGORITHM_schema-validation
comment: "Direct match - this algorithm is what we're verifying"
→ moment_DECISION_pick-algorithm-003
Step 4: pick_input_dock
answer: thing_FUNC_GraphOps__query
comment: "This is where all validation queries run through"
→ moment_DECISION_pick-input-dock-004
Final cluster includes:
- 4 nodes
- 14 links
- 4 decision moments (linked to nodes they affected)
Query later: "Why is health_schema_compliance linked to V1?" Answer: Find moment → read comment → "V1 because we check structure..."
Each question is designed to create links:
| Question | Creates |
|---|---|
| "Which space?" | contains link |
| "Which validations?" | verifies links (multiple) |
| "Which algorithm?" | checks link |
| "Input dock where?" | observes link to function |
| "Output dock where?" | observes link to function |
| "Which objective?" | supports link |
| "Related patterns?" | implements links |
| "Peer indicators?" | relates links |
More questions = more links = denser cluster.
Questions aren't bureaucracy. They're link generators.
For each question type, tell agent how to create answer:
Select from options:
guidance: |
Review each option in context.
Check: Does this indicator actually verify this validation?
Check: Can the dock observe what this validation requires?
Select all that genuinely apply.
Write comment explaining:
- Why you selected these
- Why you didn't select others
Create new text:
guidance: |
Name should be:
- Slug format (lowercase, hyphens)
- Descriptive of function (what it checks)
- Not implementation detail (not "graphops-query-checker")
Good: "schema-compliance", "energy-bounds", "link-integrity"
Bad: "checker-1", "my-health", "graphops-validator"
Write comment explaining naming choice.
Observation point:
guidance: |
Trace the data flow:
1. Where does the value we're checking originate?
2. What function actually produces/transforms it?
3. Pick the most specific point where we can observe.
For input dock: Where data is READ
For output dock: Where result is REPORTED
Write comment explaining why this observation point.
| Aspect | Design |
|---|---|
| Questions are rich | Include why, context, guidance, creates |
| Context is injected | Graph state shown, not guessed |
| Guidance teaches | How to think, not just what to pick |
| Creates is transparent | Agent sees what answer produces |
| Comment captures reasoning | Every step has explanation |
| Comments become moments | Reasoning persisted in graph |
| Questions create links | Each answer generates connections |
| No phantom links | Options come from graph |
Procedure questions = guided thinking + link generation + provenance capture
Naming:
procedure_<verb>_<object>procedures/procedure_<verb>_<object>.yamlStructure:
batch_questions:
- trigger: "What gap/event starts this procedure?"
- output: "What nodes/links get created?"
- existing: "What must already exist?"
- enables: "What does this output enable?"
contextual_knowledge:
domain: |
<What this area of graph is about>
<Key concepts and relationships>
<What already exists and how structured>
constraints: |
<Invariants that must hold>
<Patterns to follow, anti-patterns to avoid>
dependencies: |
<What must exist before protocol runs>
<What output enables>
quality_criteria: |
<What makes good vs bad output>
<Examples of well-formed clusters>
understand_situation → check_prerequisites → gather_details → create_cluster
Each step type has specific structure (see Step Types below).
Multiple nodes + rich internal links + links to existing nodes + moment tracing.
protocol: procedure_<verb>_<object>
version: "1.0"
description: <what this creates and why>
triggers:
- gap: "<graph query that reveals need>"
- event: "<action that triggers>"
requires_skills:
- "<skill documents for domain knowledge>"
contextual_knowledge:
domain: |
<domain facts>
constraints: |
<invariants and patterns>
dependencies: |
<prerequisites and enables>
quality_criteria: |
<good vs bad examples>
steps:
understand_situation:
type: ask
auto_fetch:
- query: { find: <type>, in_space: "{space}" }
store_as: existing_items
context: |
{contextual_knowledge.domain}
Current state: {existing_items | summarize}
questions:
- name: goal
ask: "What outcome? What problem?"
why: "<reasoning>"
guidance: "<how to think about it>"
creates: "<what this produces>"
next: check_prerequisites
check_prerequisites:
type: branch
checks:
- condition: "<missing dependency>"
action:
type: call_protocol
protocol: <prerequisite_protocol>
on_complete: reload_state
- condition: "<already exists>"
action: { type: complete, message: "<why no work needed>" }
- condition: "ready"
action: { goto: gather_details }
gather_details:
type: ask
context: |
Goal: {goal}
{contextual_knowledge.constraints}
Available: {existing_items | list}
questions:
- name: <field>
ask: "<question>"
why: "<reasoning>"
context: "<injected from graph>"
guidance: "<how to decide>"
creates: "<what this produces>"
good_answer: "<example>"
bad_answer: "<example>"
expects: { type: <type>, <constraints> }
# ... batch 3-7 questions
moment: { agent_provides: [description, reasoning] }
next: create_cluster
create_cluster:
type: create
nodes:
- id: "<primary>_{name}"
node_type: narrative
# fields...
- id: "moment_{timestamp}"
node_type: moment
prose: "{agent.description}"
links:
- type: contains
from: "{space}"
to: "<primary>_{name}"
- for_each: <linked_items>
type: relates
from: "<primary>_{name}"
to: "{item}"
- type: expresses
from: "{actor_id}"
to: "moment_{timestamp}"
moment: { agent_provides: [description] }
next: $complete
output:
cluster:
nodes: [<list>]
links: [<list>]
summary: "<template>"
type: ask
auto_fetch: [{ query: ..., store_as: ... }]
context: "<situation + loaded data>"
questions:
- name: <key>
ask: "<question>"
why: "<reasoning>"
context: "<injected state>"
guidance: "<how to decide>"
creates: "<what this produces>"
good_answer: "<example>"
bad_answer: "<example>"
expects: { type: ..., constraints... }
moment: { agent_provides: [description] }
next: <step>
type: query
auto_fetch:
- query: { find: ..., where: ..., in_space: ... }
store_as: <key>
purpose: "<why loading>"
next: <step>
type: branch
checks:
- condition: "<expression>"
action: { goto: <step> } | { type: call_protocol, ... } | { type: complete, ... }
type: call_protocol
protocol: <name>
reason: "<why needed>"
context: { <passed_values> }
on_complete: <step>
type: create
nodes: [{ id: ..., node_type: ..., fields... }]
links: [{ type: ..., from: ..., to: ..., properties: ... }]
moment: { agent_provides: [description] }
next: $complete
contextual_knowledge:
domain: |
Health indicators verify runtime behavior tests can't catch:
drift, ratio degradation, production-only states.
Link to validations via `relates` with direction=verifies.
Have docks (observation points) attached via `attached_to`.
constraints: |
- Must verify at least one validation
- Must have input dock AND output dock
- Mechanism specific enough to implement
- Thresholds distinguish WARNING from ERROR
quality_criteria: |
Good: "Compare max(narrative.energy) against 1.0 threshold after decay phase"
Bad: "Check if healthy"
contextual_knowledge:
domain: |
Validations = invariants that must always hold.
Link to behaviors via `relates` with direction=ensures.
Priority: HIGH=system breaks, MED=degraded, LOW=inconvenience.
constraints: |
- Must have failure_mode
- Should link to at least one behavior
- Name pattern: V-<AREA>-<INVARIANT>
quality_criteria: |
Good: "Energy values must remain in [0,1] range after every tick phase"
Bad: "Energy should be valid"
why fieldcontext (injected from graph)guidance (how to decide)creates (transparency)| Protocol | When | Creates |
|---|---|---|
protocol:explore_space | Before creating | exploration moment |
protocol:author_protocol | To create protocol | protocol YAML |
@mind:id + file + headerfile + symbol@mind:TODO@mind:escalation@mind:propositionIf blocked → @mind:escalation + @mind:proposition → proceed with proposition.