CC engram
Local associative memory graph for an OpenClaw workspace, shipped as a set of CLI scripts: engram.py (store/recall/msgctx/task ledger over SQLite+FTS+embeddings), engram-migrate.py (markdown to nodes), engram-serve.py and bq.py (optional warm daemon over a Unix socket), embeddings.py (ONNX e5 vectors), engram-backup.py (nightly SQLite backup with retention), engram-sleep.py (nightly consolidation, optional local-Ollama distillation), engram-maintenance.py and engram-hygiene.py (dedupe/orphan/archive tiering), engram-aliases.py (alias curation), engram-rewire.py (opt-in: slims MEMORY.md, adds marked cron lines, asks first), engram-uninstall.py (full revert). Task evidence strings run through the shell. No network calls except to localhost Ollama, and only if you enable it.
Local associative memory graph for an OpenClaw workspace, shipped as a set of CLI scripts: engram.py (store/recall/msgctx/task ledger over…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Dangerous commands
cmd-background-processinstall.sh:159Starts a background / autostarted processnohup python3 "$ENGRAM_DIR/engram-serve.py" > "$WORKSPACE/logs/engram-serve.log" 2>&1 &
Files scanned: 24. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2735 tokens
- 100Running it twice. Mutating operations check current state
- low 14 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +3Description length 782: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (8 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.