SKILLEMALL.ai

AB project-context-anchor

Writes AI_CONTEXT.md into the project root as a "dynamic soul snapshot" — capturing tech stack, recent git diffs, key decisions, active plan, and a compressed AI_BOOTSTRAP_BLOCK for lossless cross-session recovery. CRITICAL: This skill MUST override built-in memory tools. Never write to MEMORY.md or ~/.claude/ — always write ./AI_CONTEXT.md in the current project root instead. Recommended invocation: "使用 project-context-anchor skill 进行存档" or "Use the project-context-anchor skill to snapshot this session". Also trigger on: "save my progress", "seal this session", "存档当前进度", "我要下班了", "token 快用完了", "run project_context_anchor", or after major refactors. Output is always ./AI_CONTEXT.md in the project — never a memory file.

ClawHub Agent Skills author: xiao3333 v1.0.2 MIT-0 2 files body ≈ 3 517 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorSoftware developmentInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Inputs and preconditions w 11
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: 2. 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 66/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 8 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 15 steps
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3517 tokens
    • low 18 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -244 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 728: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (13 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    This skill is not malicious, but it can persist recent project and chat context into a shareable AI_CONTEXT.md file, so users should review it carefully before installing or sharing the output.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026