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BB figjam-storymap-llm

Use when you need to parse or audit a FigJam User Story Map (Jeff Patton methodology) into LLM-readable Markdown or JSON — after a Story Mapping workshop, before publishing a Story Map template, or when feeding a Story Map to a coding agent (Cursor, Claude Code, Copilot).

ClawHub Agent Skills author: Monika Zapisek v0.1.3 MIT-0 8 files body ≈ 3 423 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

AnalyzerFigmaAI and agentstype 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
B
88/100
safety, quality, tests
Safety 60%
86
Quality 40%
91
Run on models
none yet
Process rating
B
70/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: figjam-storymap-llm (ClawHub)

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 · 14

    ✓ No critical or high findings

    Medium and low: 14
    • low Secrets in code secret-high-entropy-token references/figjam-template-spec.md:19
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      ├── [01_S…ies]            ← backbone L1
      fixture
    • low Secrets in code secret-high-entropy-token references/figjam-template-spec.md:21
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      ├── [02_S…sks]           ← backbone L2
      fixture
    • low Secrets in code secret-high-entropy-token references/figjam-template-spec.md:203
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      [01_S…ies]    <- backbone L1: [ACT_*]
      fixture
    • low Secrets in code secret-high-entropy-token references/figjam-template-spec.md:204
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      [02_S…sks]    <- backbone L2: [TASK_*]
      fixture
    • low Secrets in code secret-high-entropy-token references/figjam-template-spec.md:336
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      4. Walk [01_S…ies] -> list of [ACT_*].
      fixture
    • low Secrets in code secret-high-entropy-token references/llm-ready-figjam-guidelines.md:152
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Backbone | `[01_S…ies]` + `[02_S…sks]` | Static header structure |
      table
    • low Secrets in code secret-high-entropy-token references/system-prompt.md:26
      High-entropy token-like string (may be an id, hash or a credential)
      ├── [01_S…ies]         (backbone L1: [ACT_*])
    • low Secrets in code secret-high-entropy-token references/system-prompt.md:27
      High-entropy token-like string (may be an id, hash or a credential)
      ├── [02_S…sks]         (backbone L2: [TASK_*])
    • low Secrets in code secret-high-entropy-token references/system-prompt.md:80
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      1. ✅ **Backbone exists.** Clear top-level `[ACT_*]` in `[01_S…ies]` and `[TASK_*]` in `[02_S…sks]`.
      quoted
    • low Secrets in code secret-high-entropy-token scripts/figjam_parser.py:54
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      SECTION_BACKBONE_ACTIVITIES = "[01_S…ies]"
      quoted
    • low Secrets in code secret-high-entropy-token scripts/figjam_parser.py:55
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      SECTION_BACKBONE_TASKS = "[02_S…sks]"
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:107
      High-entropy token-like string (may be an id, hash or a credential)
      ├── [01_S…ies]            ← [ACT_01] Activity, [ACT_02] Activity
    • low Secrets in code secret-high-entropy-token SKILL.md:108
      High-entropy token-like string (may be an id, hash or a credential)
      ├── [02_S…sks]           ← [TASK_01] Task, [TASK_02] Task, ...
    • low Secrets in code secret-high-entropy-token SKILL.md:145
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - [ ] Backbone: `[ACT_*]` in `[01_S…ies]`, `[TASK_*]` in `[02_S…sks]`
      quoted

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 70/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 7 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 87 steps
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3423 tokens
    • low 12 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 272: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 87 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill appears safe to install; it reads a user-selected FigJam board and converts it to Markdown or JSON, with normal token and board-content privacy cautions.
    LLM: benign (high) · VirusTotal: · 20 Jul 2026