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AC pahf

PAHF (Personalized Agents from Human Feedback) - Continual Personalization Framework. Triggered when applying the PAHF three-step loop: (1) Pre-action Clarification - Resolve ambiguity before action, proactively ask for confirmation (2) Preference-grounded Action - Retrieve user preferences from memory to guide decisions (3) Post-action Feedback Integration - Collect feedback after action, update preference memory Use when: - User expresses preferences or habits - Need to make decisions with multiple valid options - User corrects or adjusts your behavior - Need to remember personalized settings - Detecting potential preference changes

ClawHub Agent Skills author: Wenju Gao v1.0.2 3 files body ≈ 2 325 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationAI and agentsInfrastructurePersonal productivitytype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "dependencies"
    • note frontmatter-key unknown frontmatter key "privacy"
    • note frontmatter-key unknown frontmatter key "consent"

    Process rating: all ten parameters 64/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (pahf) differs from the folder (pafh-mini)
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 50 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 2325 tokens
    • 100Progress reporting. Reports progress
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 644: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 50 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    This instruction-only personalization skill is not malicious, but it asks agents to read and persist personal memory data with broad triggers and some automatic writes, so users should review it carefully before installing.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026