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Install in OpenClaw
/install 93pct
Description
Autocomplete for agency next steps. Like Google autocomplete but for what to do next. Given any context, returns the top concrete viable actions ranked by RO...
README (SKILL.md)
93% — Agency Autocomplete
Doctrine
"Plan Shannon → Penn Station"
Shannon is the currency of information (Claude Shannon, entropy theory).
Penn Station is the commuter hub — where everything converges.
The plan: route all decisions through Shannon (information value) to reach the station (execution).
Low Shannon = entropy = noise = waste.
High Shannon = signal = viable next step.
Usage
python3 /root/.openclaw/workspace/skills/93pct/suggest.py # top 5 next steps now
python3 /root/.openclaw/workspace/skills/93pct/suggest.py --stack # show approval stack
python3 /root/.openclaw/workspace/skills/93pct/suggest.py --done \x3Cid> # mark completed
Output format
Each suggestion includes:
- Shannon score (0-10, information density)
- Effort (minutes)
- Blocker (what stops it)
- Job ID to approve (if exec needed)
- Exact command or action
The 93% Bar
A suggestion clears 93% if:
- It is concrete (not "consider doing X")
- It is viable right now (blocker is known and solvable)
- It produces measurable output
- It costs less than it returns
Usage Guidance
This skill appears to do what it says, but it accesses local financial and secret-related files and writes a local next_steps DB. Before installing or enabling autonomous use: (1) Review the contents of the referenced paths (WORKSPACE/dollar/dollar.db, WORKSPACE/secrets/devto-api.json, WORKSPACE/secrets/gcp-service-account.json, ../../../human/btc-status.json, /root/deploy-dollar-v3.py, /tmp/agency-b64.txt) to ensure no sensitive secrets or private keys are present or that you're comfortable exposing summary values. (2) If you want stricter isolation, run the skill in a sandboxed workspace or remove/mask sensitive files. (3) Be cautious when approving suggested job IDs/commands — some items reference other scripts (btc-monitor.py, deploy-dollar-v3.py) which you should inspect separately before executing. (4) If you need stronger assurance, request the author clarify handling of any secrets and whether any other scripts invoked by the stack make network calls or exfiltrate data.
Capability Analysis
Type: OpenClaw Skill
Name: 93pct
Version: 1.0.0
The skill bundle, specifically `suggest.py`, performs path traversal to read sensitive BTC wallet data (`../../../human/btc-status.json`) and accesses GCP and Dev.to secrets within the workspace. It functions as a recommendation engine that encourages high-risk security configurations, such as granting 'Service Usage Admin' IAM roles to service accounts, and includes a direct solicitation for funds via a Cash App link (`$DollarAgency`) under the guise of 'minting' information currency. These behaviors, combined with the 'Shannon→Penn Station' doctrine in `SKILL.md`, suggest social engineering or a mechanism to lead users into insecure financial and technical states.
Capability Assessment
Purpose & Capability
The skill claims to produce ranked next steps and the included script builds a prioritized stack from local state (databases, drafts, file existence). The files and checks (dev.to draft presence, GCP SA presence, dollar DB, BTC status) line up with an 'agency next steps' planner.
Instruction Scope
Runtime instructions are simple (run suggest.py, view stack, mark done). The script reads local DBs and JSON files, checks for presence of credential files, and writes/updates a local agency.db next_steps table. It does not make outbound network requests itself, but it prints URLs and suggests commands that would invoke other scripts (e.g., btc-monitor.py, deploy-dollar-v3.py).
Install Mechanism
No install spec — instruction-only with a bundled Python script. Nothing is downloaded or extracted at install time.
Credentials
The skill requests no environment variables or external credentials, but it inspects sensitive local artifacts (WORKSPACE/dollar/dollar.db, ../../../human/btc-status.json, secrets/devto-api.json, secrets/gcp-service-account.json, /root/deploy-dollar-v3.py, /tmp/agency-b64.txt). Reading these files is consistent with planning goals, but they may contain financial or secret data that could appear in outputs.
Persistence & Privilege
always:false and default invocation rules. The script creates/updates its own agency.db table (local persistence) but does not modify other skills or global configs.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install 93pct - After installation, invoke the skill by name or use
/93pct - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of 93pct.
- Autocomplete skill for agency workflows: suggests top next steps ranked by ROI/effort.
- Outputs exactly one concrete, viable job ID for approval.
- Uses "Shannon score" for information density; shows effort, blockers, and action.
- CLI interface for getting suggestions, viewing approval stack, and marking tasks done.
- Enforces the 93% standard for actionable, high-value recommendations.
Metadata
Frequently Asked Questions
What is 93pct?
Autocomplete for agency next steps. Like Google autocomplete but for what to do next. Given any context, returns the top concrete viable actions ranked by RO... It is an AI Agent Skill for Claude Code / OpenClaw, with 100 downloads so far.
How do I install 93pct?
Run "/install 93pct" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 93pct free?
Yes, 93pct is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 93pct support?
93pct is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 93pct?
It is built and maintained by Automate (@ironiclawdoctor-design); the current version is v1.0.0.
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