SKILLEMALL.ai

BC Telos

Personal TELOS reads and updates goals, beliefs, narratives, and strategies with timestamped backups; Project TELOS analyzes .md/.csv directories for dependencies, bottlenecks, and alignment. USE WHEN Telos, life goals, projects, dependencies, update TELOS, narrative points, McKinsey report, dashboard, what am I wrong about, life frames, mental models. NOT FOR conversational constitutional review (Interview).

danielmiessler/Personal_AI_Infrastructure Agent Skills author: danielmiessler MIT 57 files · 20 scripts body ≈ 1 926 tokens Open the sourcegithub.com↗ analyzed 27 h ago

Personal TELOS reads and updates goals, beliefs, narratives, and strategies with timestamped backups; Project TELOS analyzes .md/.csv directories for…

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
96
Quality 40%
82
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Exfiltration read-dotenv DashboardTemplate/README.md:54
      Reads a .env file
      cp .env.example .env
    • low Broad scope meta-dynamic-shell Intents/Update.md:71
      Shell command executed automatically when the skill loads (Claude Code !`cmd` preamble)
      !`FILE="$1"; CONTENT="$2"; DESCRIPTION="$3"; bun ~/.claude/skills/Telos/Tools/UpdateTelos.ts "$FILE" "$CONTENT" "$DESCRIPTION"`
    • low Broad scope meta-dynamic-shell Intents/Update.md:87
      Shell command executed automatically when the skill loads (Claude Code !`cmd` preamble)
      !`head -50 ~/.claude/LIFEOS/USER/TELOS/updates.md`
    • low Broad scope meta-dynamic-shell Intents/Update.md:90
      Shell command executed automatically when the skill loads (Claude Code !`cmd` preamble)
      !`FILE="$1"; cat ~/.claude/LIFEOS/USER/TELOS/"$FILE"`

    Files scanned: 56. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "evals"

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 5 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1926 tokens
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 412: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.