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BC conf-abstract-enrichment

Thicken a thin or missing conference-session abstract through targeted web research so short, noisy text becomes classifiable — pulling the speaker's recent work, the linked repo or paper, and the company/product to add signal. Every enriched claim carries provenance (source URL + retrieval date) and a confidence, the enriched text is stored separately from the original (never overwriting it), and enrichment is gated so already-rich abstracts are left alone. Conference-agnostic. Use when an event record has been flagged thin by program extraction, when a session lacks enough text to classify, or when a key axis (depth, topic) is low-confidence. Trigger keywords - abstract enrichment, thicken abstract, enrich session, speaker research, thin abstract, runtime web search for talks.

lyndonkl/claude Agent Skills author: lyndonkl 1 file body ≈ 2 575 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Thicken a thin or missing conference-session abstract through targeted web research so short, noisy text becomes classifiable — pulling the speaker's recent…

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers

ProcedureAI and agentsWriting and documentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Failures and branches w 10
50
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: 1. 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 61/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2575 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 789: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.